A particular idea can often be coded linguistically in several different ways: that is to say, linguistic convention allows speakers various options. At the lexical level, speakers can choose from sets of near synonyms (walk, march, stride, strut…). Similarly, at the grammatical level, there are often several options for encoding slightly different construals of the same situation: for instance, in English, there are several ways of marking past events (was walking, walked, had walked), two indirect object constructions (give him the book vs. give the book to him), and so on. Cognitive linguists have long been claiming that languages abhor (complete) synonymy and evidence is accumulating showing that in the vast majority of cases, the various options are preferred in different contexts.
However, the criteria governing the selection of the appropriate form are often far from obvious, and hence, there is now a considerable amount of empirical work attempting to describe the differences between near synonymous lexemes or constructions (for book-length treatments see Arppe 2008; Divjak 2010; Klavan 2012 and references therein). Most researchers who attempt to discover the factors influencing a speaker’s decision to use a particular form rely on the analysis of large corpora. A typical analysis involves extracting a large number of examples from a corpus and coding those for a number of potentially relevant features (Klavan 2012) or even as many potentially relevant features as possible (Arppe 2008; Divjak 2010). The usage patterns obtained can then be analyzed statistically to determine which of the candidate features are predictive of the form which is the focus of the study. The most rigorous studies also fit a statistical model to the data and test it on a new set of corpus examples (the testing set) to see how well it generalizes to new data.
One problem faced by researchers in this area is how to evaluate such models. A model that supplies the target form 85% of the time may be regarded as better than one that predicts it 80% of the time – but can this be regarded as adequate? After all, such a model still gets it wrong 15% of the time. The answer, of course, depends partly on (1) how many options there are to choose from (51% correct is very poor if there are only two options, but would be impressive if there were ten), but also on (2) the degree to which the phenomenon is predictable (100% correct is not a realistic target if the phenomenon is not fully predictable), as well as (3) what is being predicted: individual choices or rather proportions of choices over time. As Kilgariff (2005) and many others have observed: language is never ever random; however, it is also rarely, if ever, fully predictable.
The obvious solution for cognitive linguists is to compare the model’s performance to that of native speakers of the language. Such a comparison could, in principle, result in three possible outcomes. First, the model may perform less well than humans. If this is the case, then the model is clearly missing something, and this tells us that we must go back to the data and find out what we have not coded for, add new predictors to the model, and test it again. Secondly, the model may perform as well as humans. This is clearly an encouraging outcome, but if we are interested in developing a psychologically realistic model (as opposed to simply describing the corpus data), we would want to make sure that the model is relying on the same criteria as the speakers. We could conclude that this was the case if the pattern of performance was similar, that is to say, if the model gave clear predictions (i. e. outputs a high probability for one particular option) when the speakers consistently choose that same option, and, conversely, if uncertainty in the model (several options with roughly equal predicted probabilities, of, for example, 0.2–0.3 in the case of 3–5 alternatives, as opposed to one clear favourite) corresponded to variability in human responses. Finally, the model may perform better than humans. Statistical models have been found to outperform human experts in a number of areas including medical diagnosis, academic and job performance, probation success, and likelihood of criminal behaviour (Dawes et al. 1989; Grove et al. 2000; Stanovich 2010). To our knowledge, no model of linguistic phenomena currently performs better than humans (for instance, is able to choose the form that actually occurred in a particular context in a corpus more accurately than the average human informant) but it is perfectly possible that, as our methods improve, such models will be developed.
2 Previous studies
There are now a number of published multivariate models that use data, extracted from corpora and annotated for a multitude of morphological, syntactic, semantic and pragmatic parameters, to predict the choice for one morpheme, lexeme or construction over another. However, most of these studies are concerned with phenomena that involve binary choices (Gries 2003; De Sutter et al. 2008) and only a small number of these 1 corpus-based studies have been cross-validated (Keller 2000; Wasow and Arnold 2003; Sorace and Keller 2005; Roland et al. 2006; Arppe and Järvikivi 2007; Divjak and Gries 2008). 2 Of these cross-validated studies, few have directly evaluated the prediction accuracy of a complex, multivariate corpus-based model on humans using authentic corpus sentences (with the exception of Bresnan 2007; Bresnan and Ford 2010; Ford et al. 2013a; Ford et al. 2013b), and even fewer have attempted to evaluate the prediction accuracy of a polytomous corpus-based model in this way (but see Arppe and Abdulrahim 2013 for a first attempt). Below we will review the latter two types of cross-validated studies.
Bresnan (2007) was the first to evaluate a multivariate corpus-based model (Bresnan et al. 2007) designed to predict the binary dative alternation. A scalar rating task was used to evaluate the correlation between the naturalness of the alternative syntactic paraphrases and the corpus probabilities. Materials consisted of authentic passages attested in a corpus of transcriptions of spoken dialogue; the passages were randomly sampled from the centers of five equally sized probability bins, ranging from a very low to a very high probability of having a prepositional dative construction. For each sampled observation the alternative paraphrase was constructed. Both options were presented as choices in the original dialogue context. Contexts were edited for readability only by shortening and by removing disfluencies. Items were pseudo-randomized and construction choices were alternated to make up a questionnaire. Each of the 19 subjects received the same questionnaire, with the same order of items and construction choices. Subjects were asked to rate the naturalness of alternatives in a given context by distributing 100 points over both options. Responses were analysed as a function of the original corpus model predictor variables by using mixed effects logistic regression. Bresnan found that subjects’ scores of the naturalness 3 of the alternative syntactic paraphrases correlate well (R2=0.61) with the corpus probabilities and can be explained as a function of the same predictors. Individual speakers’ choices matched the choice attested in the corpus in 63–87% of all cases (with a baseline of 57% correct by always choosing the most frequently occurring option). Bresnan concluded that language users’ implicit knowledge of the dative alternation in context reflects the usage probabilities of the construction.
Bresnan and Ford (2010) and Ford and Bresnan (2013a, 2013b) investigated the same question across American and Australian varieties of English. Relevant here is that they ran a continuous lexical decision task (Ford 1983) to check whether lexical-decision latencies during a reading task reflect the corpus probabilities. In a continuous lexical decision task subjects read a sentence word by word at their own pace, and made a lexical decision as they read each word (participants were presented with a sentence one word at a time and must press a “yes” or “no” button depending on whether the “word” is a real word or a non-word). The participants were instructed to read the contextual passage first and then make a lexical decision for all words from a specific starting point. That starting point was always the word before the dative verb. There were 24 experimental items, chosen from the 30 corpus items used in the scalar rating task (Bresnan 2007). A mixed effects model fit to the data confirmed that lexical-decision latencies during the reading task reflect the corpus probabilities: more probable sentence types require fewer resources during reading, so that reading times measured in the task decrease in high-probability examples.
Arppe and Abdulrahim (2013) contrast corpus data and forced-choice data on four near-synonymous verbs meaning come in Modern Standard Arabic to assess the extent to which regularities extracted from a corpus overlap with collective intuitions of native speakers. A model of the corpus data was built using polytomous logistic regression based on the one-vs-all heuristic (Arppe 2008, 2013a) and was compared to data from a forced-choice task completed by 30 literate Bahraini native speakers of Arabic who read 50 sentences and chose the missing verb from a given list of verbs. The 50 experimental stimuli were chosen to represent the full breadth of contextual richness in the corpus data and the entire diversity of probability distributions, ranging from near-categorical preferences for one verb to approximately equal probability distributions for all four verbs. Arppe and Abdulrahim (2013) found that as the probability of a verb, given the context, rises, so does the proportion of selections of that verb in the context in question (proportion being the relative number of participants selecting the particular verb). Importantly there were hardly any cases where a low-probability verb received a high proportion of choices, and only a few in which high-probability verb received a low proportion of choices.
3 Russian verbs of trying
In this paper, we explicitly compare the performance of a statistical model derived from a corpus with that of native speakers. The specific phenomenon that we will investigate concerns six Russian verbs (probovat’, silit’sja, pytat’sja, norovit’, starat’sja, poryvat’sja) which are similar in meaning – they can all be translated with the English verb “try” – but which are not fully synonymous. As explained in Divjak (2010: 1–14), these verbs were selected as near-synonyms on the basis of a distributional analysis in the tradition of Harris (1954) and Firth (1957), with meaning construed as contextual in the Wittgensteinian sense. Synonymy was thus operationalized as mutual substitutability or interchangeability within a set of constructions forming a shared constructional network. This is motivated by a Construction Grammar approach to language in which both constructions and lexemes are considered to have meaning; as a consequence, the lexeme’s meaning has to be compatible with the meaning of the construction in which it occurs and of the constructional slot it occupies to yield a felicitous combination. Therefore, the range of constructions a given verb is used in and the meaning of each of those constructions are indicative of the coarse-grained meaning contours of that verb. The results can then be used to delineate groups of near-synonymous verbs. On this approach, near- synonyms share constructional properties, even though the extent to which a construction is typical for a given verb may vary and the individual lexemes differ as to how they are used within the shared constructional frames.
To study verbal behavior within a shared constructional frame we build on earlier work by Divjak (2003, 2004, 2010), who constructed a database containing 1351 tokens of these verbs. The source of the data was the Amsterdam Corpus, supplemented with data from the Russian National Corpus, which contains written literary texts. About 250 extractions per verb were analysed in detail, except for poryvat’sja, which is rare and for which only half that number of examples could be found. Samples of equal size were chosen for two reasons: (1) interest was in the contextual properties that would favour the choice of one verb over another, and by fixing the sample size, frequency was controlled, (2) the difference in frequency of occurrence between these verbs is so large (see Table 6 below) that manually annotating a sample in which the verbs would be represented proportionally would be prohibitively expensive. The sentences containing one of the six TRY verbs were manually annotated for a variety of morphological, semantic and syntactic properties, using the annotation scheme proposed in Divjak (2003, 2004). The tagging scheme was built up incrementally and bottom-up, starting from the grammatical- and lexical-conceptual elements that were attested in the data. This scheme captures virtually all information provided at the clause (in case of complex sentences) or sentence level (for simplex sentences) by tagging morphological properties of the finite verb and the infinitive, syntactic properties of the sentences and semantic properties of the subject and infinitive as well as the optional elements. There were a total of 14 multiple-category variables amounting to 87 distinct variable categories or contextual properties. Divjak and Arppe (2013) used this dataset to train a polytomous logistic regression model (Arppe 2013a, 2013b) predicting the choice of verb. As a rule of thumb, the number of distinct variable combinations that allow for a reliable fitting of a (polytomous) logistic regression model should not exceed 1/10 of the least frequent outcome (Arppe 2008: 116). In this case, the least frequent verb occurs about 150 times, hence the number of variable categories should be approximately 15. The selection strategy they adopted (out of many possible ones) was to retain variables with a broad dispersion among the 6 TRY verbs. This ensured focus on the interaction of variables in determining the expected probability in context rather than allowing individual distinctive variables, linked to only one of the verbs, to alone determine the choice. As selection criteria they required the overall frequency of the variable in the data to be at least 45 and to occur at least twice (i. e. not just a single chance occurrence) with all six TRY verbs. Additional technical restrictions excluded one variable for each fully mutually complementary case (e. g. the aspect of verb form – if a verb form is imperfective it cannot at the same time be perfective and vice versa) as well as variables with a mutual pair-wise Uncertainty Co-Efficient UC value (a measure of nominal category association; Theil 1970) larger than 0.5 (i. e. one variable reduces more than ½ of the uncertainty concerning the other). Altogether 18 variable categories were retained (11 semantic and seven structural), belonging to seven different types. These are listed in Table 1.
Using the values of these variables as calculated on the basis of the data in the sample, the model predicts the probability for each verb in each sentence. More interestingly from an analyst’s perspective, the model tells us how strongly each feature individually is associated with each verb (e. g. norovit’ and especially poryvat’sja are strongly preferred when the infinitive describes a motion event while pytat’sja, starats’ja and silit’sja are dispreferred in this context; probovat’ does not have a preference one way or the other). This enables us to characterize each verb’s preferences (Divjak 2010; Divjak and Arppe 2013; Arppe 2013b).
Assuming that the model “chooses” the verb with the highest predicted probability (though strictly speaking a logistic regression model is attempting to represent the proportions of possible alternative choices in the long run), its overall accuracy was 51.7% (50.3% when tested on unseen data). This is well above chance: since there are six verbs, chance performance would have been at 16.7%. This overall accuracy may, however, still seem disappointingly low until we remember that the verbs have very similar meanings and are often interchangeable: that is to say, most contexts allow several, if not all, verbs. So the more interesting question is how the model’s performance compares with that of humans. We explore this question in three studies.
4 Study 1 – Forced choice task
In this study, we investigate Russian speakers’ preferences for verbs of trying in specific sentential contexts using a forced-choice task. We then compare the speakers’ preferences to those of the model, asssuming that the model “prefers” the verb with the highest predicted probability. Choosing a verb to go in a particular sentence is a fairly artificial task: it is not what speakers do during normal language use. However, a forced-choice task provides useful information about speakers’ preferences, and for this reason such tasks are routinely used in psycholinguistic research as well as in language testing. From our point of view, its major advantage is that it allows us to obtain comparable data from the model and from native speakers.
We extracted 60 sentences from the Divjak (2010) dataset. The sentences were selected to represent the whole spectrum of the probability distributions. The probabilities of the selected sentences are visualized in Figure 1 where each colour represents a different verb (colours represent sentence-specific probabilities rather than verbs, i. e. red is always used to mark the verb that has the highest probability of occurring, regardless of which of the six verbs it is; green is always used for the second most likely verb, etc.) and the height of each coloured portion of the bar represents the probability of the verb’s occurrence as predicted by the Divjak and Arppe (2013) model. As we move from left to right, we see that the predominance of one verb over all other options diminishes, until we end at the right hand side with a number of cases in which the distribution of probabilities starts to equal out over all 6 verbs.
Four of the sentences were close to categorically biasing contexts according to the Divjak and Arppe (2013) model, i. e. the model assigned a probability of 0.70 or above to one verb, and the predicted probabilities for all other verbs were ≤0.10. Thirty-one experimental sentences were strongly biasing, i. e. the model predicted a probability value of more than 0.50 for one of the verbs. In the remaining 25 sentences, there was no clear winner, with up to five verbs with predicted probabilities ≥0.10. Because the sentence selection was driven by concerns about the probability distribution, not all six target verbs are represented in equal numbers in the experimental sentences. Table 2 specifies the number of sentences used for each of the six TRY verbs.
We then created four experimental lists, each with a different random order. In each sentence, the TRY verb was replaced with a blank, and the six possible verbs were printed below it in alphabetical order.
One hundred and fifty nine adult native speakers of Russian were recruited via e-mail announcements and through personal contacts. The participants were randomly assigned to one of the four lists. Twenty-five participants did not supply responses for all verbs and were excluded. The data for the remaining 134 participants (28 males, 106 females) was entered into the analysis. The participants ranged in age from 17 to 64 (mean 30, SD=10). The vast majority either held a university degree or was studying for one.
The participants were given the following instructions (in Russian):
You will be presented with 60 sentences from which a verb has been deleted. Read the sentences and the answer options and choose the verb that fits the context best from the list of six options. Work at a quick pace, don’t think too long over one answer, don’t go back and change things: there are no right or wrong answers and we are interested in your first choice.
To obtain comparable data from the model, we excluded the 60 test sentences from the Divjak (2010) dataset and trained the model on the remaining sentences. We then used the model to compute the probability for each of the six verbs in each of the test sentences.
4.2 Results and discussion
4.2.1 Analysis 1: Model vs. average participant
In order to compare the model and the participants, we assumed that the model’s response on the forced choice task would be the verb with the highest predicted probability for a given context. In the analysis that follows, we take the verb which actually occurred in the corpus to be the “correct” response. Of course the attested corpus example may be an unrepresentative one, so this is not necessarily the best way to evaluate the model. We will return to this issue in sections 4.2.2 and 6.
Since there were 60 sentences and six verbs, chance performance would be about 10/60; given the skewed distribution of verbs over experimental sentences discussed above, always choosing the same verb would result in a correct choice for between 4 and 20 out of 60 sentences, depending on the verb (see Table 2). Always selecting the TRY verb most frequently used in corpus data, pytat’sja, would have yielded a correct choice in 10 out of 60 sentences (see Table 2). The model predicted the verb that actually occurred in the corpus for 23 of the 60 test sentences – i. e. 38% of the time. This is considerably lower than the performance on randomly chosen sentences (50% – see above), and reflects the fact that the testing set intentionally contained a larger proportion of verbs in highly ambiguous, or variable, contexts than would be the case in a random sample. The mean number of “correct” choices for the participants was 27.7, i. e. 46% of the time (SD 4.7) and the median was 28; the scores ranged from 13 to 38. Thus, there is considerable individual variation in humans (no doubt reflecting the fact that the participants often guessed), and the model performed about a standard deviation less well than the average human. In other words, although both model and speaker perform 2.5 to 3 times better than chance, they still make the “wrong” choice in more than half of all cases.
Tables 3, 4 and 5 provide summaries of the results by verb. Table 3 specifies the number of trigger sentences in which the verb that was used in the original corpus sentence was correctly retrieved by the model or by the human respondents. While the model performs particularly poorly on pytat’sja and starat’sja, the human respondents struggle with silit’sja, which is unsurprising as the verb is relatively infrequent (see Table 6 below for further discussion) and obsolescent.
Table 4 summarizes the choices made by our respondents for each verb. Each row in the table summarizes the participants’ responses to sentences containing one of the six verbs. The numbers across the diagonal provide information about “correct” responses, i. e. proportion of times when participants supplied the verb that actually occurred in the corpus (e. g. 58% of the time for norovit’); the other figures in the same row give us the proportion of the time that other verbs were used in the same contexts. Thus, row 1 tells us that, on average, for corpus sentences that originally contained norovit’, participants supplied that verb 58% of the time, probovat’ 3% of the time, silit’sja 5% of the time, and so on.
It is clear from the table that the participants used all the verbs in each type of context, although they also had a strong preference for one of the verbs (and in the case of starat’sja contexts, for two verbs, pytats’sja and starat’sja). Moreover, as we can see, the highest values (in boldface) are not always on the diagonal. The verb silit’sja for example, was frequently replaced with pytat’sja by native speakers, and starat’sja is nearly equally often predicted as pytat’sja than as starat’sja.
For ease of comparison, we present the results for the model in a format similar to Table 4, containing the data provided by the respondents. Yet, it must be borne in mind that the 60 sentences were selected so as to contain a substantial number of cases with inherent variability, allowing virtually all of the six TRY verbs. Therefore, the average probabilities mask a substantial amount of variability in the sentence-wise verb-specific probability estimates. Thus, the first row in Table 5 gives us the predicted probability of norovit’ in norovit’ contexts (averaged across all sentences with norovit’), followed by the probabilities for the other verbs in norovit’ contexts. Here the highest values are not always on the diagonal either, and pytat’sja and starat’sja as well as poryvat’sja are often replaced with silit’sja by the model.
As we can see, the results for the model and the participants are broadly similar, but there are also some differences. The model has particular problems with starat’sja and especially pytat’sja. The average predicted probability of pytat’sja in relevant contexts is 0.23 (Table 5), yet in the 60-sentence test sample, the model chose it as the most probable option (by a very narrow margin) in only one out of ten contexts in which pytat’sja was expected (Table 3). The corresponding figures for human participants, on the other hand, are considerably higher: the average predicted probability of pytat’sja in relevant contexts is 0.50 (Table 4) and humans chose it in 8/10 cases (Table 3). Furthermore, as can be seen by looking at the figures in column 5 of Table 4, participants often overgeneralized pytat’sja, using it in contexts where other verbs occurred in the corpus: in fact, for 20 out of the 50 sentences with verbs other than pytat’sja, the majority of the participants chose pytat’sja; the model did this much less frequently (in only 8 out of 50 cases). In contrast, the human participants struggled with the verb silit’sja, while the model did quite well with this verb. These differences are likely to be due to frequency effects. As shown in Table 6 that contains the frequencies with which the TRY verbs appear followed by an infinitive, the verbs differ considerably in their frequencies: pytat’sja is the most frequent verb by a large margin, while silit’sja is one of the least frequent and is in fact becoming obsolete.
We know that humans are highly sensitive to frequency information (for reviews, see Ellis 2002; Divjak and Caldwell-Harris 2015), so it is not surprising that they tended to select the most frequent (and hence most general) verbs when they had no strong preference for a verb with a more specific meaning, i. e. when the contextual factors were not strong enough to clearly favour one outcome. This is especially the case in an experimental setting with only a small number of contexts, which limits the possibility of the effect of the estimated probabilities to emerge; (estimated) probabilities show their effect in the long run, and this typically requires more than a few dozen sentences. The model, in contrast, makes its predictions entirely on the basis of how often the sentence-wise combination of the variables discussed earlier (Table 1) is associated with each verb, as it had no access to information about the token frequencies of individual verbs (recall that the frequencies in the sample used for training were roughly equal by design to level the playing field for the contextual properties of interest). Moreover, the model considers relative frequencies of the outcome verbs, given the particular contexts, not overall proportions in general language usage.
To accommodate frequency information, we multiplied the predictions of the original model by the square root of each verb’s relative frequency. Using the square root is a common practice when dealing with skewed distributions (Field et al. 2012); it is also psychologically realistic in that frequency effects are most noticeable at lower frequencies. Table 7 presents a summary of predictions for each verb; for ease of comparison with Tables 3 and 4, the figures given in Table 7 were converted to probabilities by dividing the frequency adjusted values for each verb in each sentence by the sum of the frequency adjusted values for all six verbs.
The frequency-adjusted model predicted the target verb correctly in 28 of the 60 sentences – in other words, overall, it performed at exactly the same level as the average human participant. As expected, the frequency adjustment made the performance more human-like on pytat’sja and starat’sja. Moreover, like human participants, the frequency-adjusted model tended to overgeneralize pytat’sja, which is now the most frequently chosen option for all verbs except probovat’. It also undergeneralizes silit’sja and instead predicts it to be pytat’sja or starat’sja. On the other hand, it performed less well than both original model and human participants on sentences with norovit’ and poryvat’sja.
Thus, adding frequency information improved performance, but the overall improvement was relatively modest, and performance on some verbs actually deteriorated. This signals that the trade-off between frequency information and contextual information with which native speakers operate is more sophisticated than we can capture with a logistic regression model that runs on contextual features enriched with the frequency of the TRY verb in the targeted syntactic context.
4.2.2 Analysis 2: Model vs. participants as a group
All the analyses so far assumed that the verb which actually occurred in the corpus was the “correct” response. This is the fairest way to compare the model’s performance to that of humans, but it is problematic in the sense that not all corpus examples are necessarily representative. In fact, since the corpus includes a high proportion of literary texts, it is possible that a number of the uses involved the author deliberately using an unusual verb for special effect. To determine whether this is the case, we conducted a second analysis to see how often the participants, the model, and the corpus “agreed” (i. e. both participants and the model choose the verb that occurred in the corpus) and how often they “disagreed.” For this analysis, the verb that was selected by the largest number of participants was deemed to be preferred: in other words, we treated each individual response as a “vote” for a particular verb in a particular sentence, and the verb that got the most votes was the winner. Logically, there are five possibilities:
the corpus, the model and the participants all agree;
the model chooses the verb that occurred in the corpus while the participants prefer a different verb;
the participants prefer the verb that occurred in the corpus while the model prefers a different verb;
the model and the participants both prefer the same verb, but not the one that occurred in the corpus;
the model and the participants prefer different verbs, and the corpus contains yet another verb.
As we can see, experimental items where the model and the participants agreed on a verb different from the verb used in the corpus account for nine out of 60, i.e. 15% of all cases. In such cases, the choice of the verb attested in the corpus is arguably unusual or has become obsolete, and the verb preferred by the participants (and the model) should be regarded as (currently) “correct.” Thus, the accuracy figures given in the preceding section underestimate the participants’ (and the model’s) true performance by about 15%. The corpus and the frequency-adjusted model agreed on 28 (19+9) out of the 60 sentences, that is in 46.6% of all cases. This is virtually identical to the average human performance: as indicated earlier, the mean human score was 27.7 and the median 28. However, as shown in the table, the humans as a group did considerably better, choosing the “correct” verb in 35 (19+16) or 58.3% of sentences. Why should there be such a discrepancy between individual and group performance? One possibility is that the difference is due simply to the fact that, between them, 134 participants have experienced more verb tokens in relevant contexts than any one participant, and hence had more opportunities for learning the differences between the contexts (in the widest sense of the word, i. e. not necessarily limited to sentential contexts, and including subtle pragmatic differences and attitudes) in which the verbs occur. If this is the case, then we would expect older participants (who have had more experience, possibly including more experience with the type of texts the corpus contained) to perform better than younger participants. In order to test this possibility, we computed a Pearson product-moment correlation between participants’ age and their scores in the experiment. The relationship turned out to be insignificant (rPearson=–0.09, p=0.323), suggesting that all participants have had enough relevant experience. Hence, it is unlikely that the difference between individual and group scores can be explained by the amount of experience – although it is possible, of course, that what matters more than sheer amount is the type of experience, for instance, exposure to particular genres.
Another possibility is that different participants relied on different features, and hence collectively the group were able to take advantage of more information than any one individual. This possibility will be explored in Study 2 (see Section 5).
4.2.3 Analysis 3: Using forced-choice responses as the test corpus
In the previous two sections, we compared human participants and the model by giving them both the same task: predicting the verb that actually occurred in each sentence in the test corpus. An alternative way to evaluate the model is to see how well it can predict the participants’ responses: in other words, we take all of the participants’ responses (134×60 sentences) and use them as another test corpus for the model. In this section, we assess the model’s performance on this test corpus.
A polytomous mixed-effect regression model (with participant as the random effect) of the type described in Section 3 achieves a likelihood-based pseudo-variance of MacFadden’s RL2=26.2% in explaining the individual categorical choices in the forced choice data using exactly the same model specification, i. e. variable combinations, as was used to explain the literary corpus data. This is slightly less than the original corpus-based model that achieved 31%.
We can gain a better understanding of how the predictions for the forced- choice corpus compare to the predictions for the literary texts corpus by inspecting the resulting odds tables. The odds from the forced-choice model are represented in Table 9. Boldfaced odds greater than one signal variable levels in favour of a specific verb, odds less than one capture variable levels against a specific verb, and odds in parentheses denote insignificant variable levels. Take for example the fact that the TRY verb occurs in a main clause (CLAUSE.MAIN) which has significant positive odds in favor of probovat’, pytat’sja, starat’sja and silit’sja but neutral odds for norovit’ and poryvat’sja. The comparatively high odds of a perfective aspect on the TRY verb (FINITE.ASPECT_PERFECTIVE) in favor of probovat’ stand out — this is due to the fact that probovat’ is one of only three verbs that have a perfective counterpart, and the verb that occurs most frequently in the perfective aspect in the data.
Even at first glance, it is clear that while some verbs have clearly different profiles, others are more similar to each other. For example, probovat’, pytat’sja, silit’sja and starat’sja share four of their favourable odds and the differences between probovat’ and pytats’sja in terms of odds in favour are marginal (perfective aspect triggers probovat’). Other verbs, such as norovit’ and poryvat’sja are markedly different, sharing at most one favoured property with the four other verbs. As was the case with the odds derived from the literary corpus data (presented in Table 10 below), overall, the presence of the infinitive plays a significant role in the selection of norovit’ and poryvat’sja, but for the other four verbs it is either much less relevant or even signals repulsion in the case of pytat’sja.
When we compare the odds tables (Table 9) and (Table 10) in more detail, we see that the odds in favour of one and the same verb are different depending on the corpus. The verb that shows least variation in this respect is starat’sja which is in both datasets likely to be used in declarative sentences with a human subject, if the TRY verb occurs in a gerund or if the infinitive has imperfective aspect marking; in the literary corpus data high control over the infinitive action was another trigger, while in the forced-choice corpus data occurring in a main clause and describing a past attempt at a physical action turned out to be triggers. Other verbs, such as pytat’sja and poryvat’sja seem to be triggered by entirely different sets of properties in the literary data and the forced-choice data. Pytat’sja is such an example: while in the literary corpus model variable levels such as past tense, a high level of control over the infinitive action and physical activities trigger pytat’sja, in the forced-choice model it is a human subject, occurrence as gerund, being used in a main clause and in a declarative sentence that trigger the verb. Furthermore, in the literary corpus model, nine out of the 19 variables are insignificant, while in the forced-choice model only three out of 19 are insignificant and nine out of 19 are significantly against. Nevertheless the aggregated effects, i. e. overall, the correlation between the corpus-based probabilities and the forced-choice proportions stands at rpearson 0.46 (t = 9.8012, df = 358, p < 0.001).
Why then are the odds in the models different? Primarily, because the selection of the sample sentences in the forced-choice model and the frequencies of the properties associated with these sentences is different. The sample of 60 sentences presented to the subjects is much more limited in terms of the range of possible contextual properties and property combinations that it contains than the literary corpus; this affects the contribution each property makes to the choice of one option over another. This key difference also has to be borne in mind when attempting to use elicited data to investigate the behavior of properties for which no or not enough corpus data is available (Bresnan 2007).
5 Study 2: Modelling group effects and individual differences
We know that language learners are highly sensitive to frequency. However, they cannot track the frequency of everything they encounter – so how do they know what to track? This problem has led many researchers (see, e. g. Golinkoff et al. 1994; Markman 1987; Woodward and Markman 1998) to conclude that humans have innate biases which lead them to focus on some features while ignoring others. Note that this conclusion is based on an implicit assumption, namely, that all speakers of the same language converge on (more or less) the same grammar. While this assumption is quite widespread, there is now considerable evidence that it is incorrect: there are in fact significant differences between individual speakers’ grammars (for reviews see Dąbrowska 2012, 2015). It is possible, then, that different individuals concentrate on different features, and this could explain why the group in the present study did better than the average individual: between them, they are able to cover all the relevant features. We explore this possibility in the second study.
As explained earlier, the TRY verb dataset was coded for 87 features, but the model developed by Divjak and Arppe (2013) included only 18 hand-picked features. Comparative modelling suggested that different variable combinations could achieve comparable results, and that omitting some affected prediction accuracy more than omitting others. Here, we take this line of thought further and apply it in modeling individual differences. In total 134 different “dumb” models were constructed to match the number of participants in study 1. Each of these “dumb” models used a different, randomly selected subset of 18 variables. These variables were chosen from the 25 that were retained after complying with the requirements for gracefully fitting the individual one-vs-rest models, i. e. the variables occur at least once in the Forced Choice stimuli, and at least once with all of the 6 TRY verbs in the full dataset. Two exactly collinear properties were excluded (dealing with the complementary aspect marking of the finite and infinitive verbs); other than that, collinearity was not considered since it does not affect overall prediction accuracy if it is pervasive, i. e. present not just in the sample but throughout the population (cf. Harrell 2001: 65), which is what we assume here. As before, the verb with the highest predicted probability was regarded as the model’s choice and the scores were compared to the scores we got from the respondents.
5.2 Results and discussion
The prediction accuracy of these 134 models ranges from 30% to 45% (mean 39% and median 38.3%). For robustness, we also ran this same procedure using 2500 random models; this yielded a wider range but a similar mean and median. The results are presented in the Appendix.
The worst and best models share 12 out of 18 contextual variables, as illustrated in Table 11. The table shows that certain properties such as (present) tense and (imperfective) aspect of the finite verb, as well as aspect of the infinitive contribute to the individual profiles of the verbs. Although tracking these properties significantly improves prediction accuracy, they are not typically included in lexical semantic studies.
The models supplied the target verb (that is to say, the verb that actually occurred in the corpus) on average in 23.4 out of the 60 sentences, i. e. 39% of the time (median 23, SD 1.7, range 18–27). Thus, the average level of accuracy of the “dumb” models was virtually identical to that of the hand-crafted model, which, as we have seen, selected the target verb for 23/60 sentences, and slightly below that of human participants who scored 28/60. Interestingly, however, there was much less variation in the “dumb” models’ accuracy scores than in humans: recall that the standard deviation for humans was 4.7 – almost three times larger than for the dumb models, and the range of scores was 13–38 – almost four times larger. This is rather surprising, and suggests that it does not really matter which contextual features humans track, as long as they track enough features. 4
Allowing the models to “vote” in the same way as the human participants in Study 1 resulted in a negligible improvement in performance, from 23.4 to 24. This is probably due to the fact that, in contrast to the humans, the models’ property space remained constrained: although the 134 models were able to track more properties, only 25 out of the 87 annotated for were available to them (Divjak 2010). The improvement in performance that we observed for the human participants strongly suggests that they not only must have tracked different property constellations, but that they also had access to a larger range of properties than were considered in our study. We return to this issue in the concluding section (Section 7).
6 Study 3: Acceptability ratings
In a third study, we compare the probability the corpus model assigns to encountering each of the six verbs in the 60 test sentences to the acceptability ratings that adult native speakers would assign to those combinations. Several papers have investigated the relation between corpus-based frequencies and native speaker judgments (Featherston 2005; Kempen and Harbusch 2005; Arppe and Järvikivi 2007; Klavan 2012; Bermel and Knittl 2012), including the relation between probabilities conditioned on one contextual element and acceptability ratings (Divjak 2008, 2016). This study is, however, the first to correlate corpus-based probabilities for the choice of one verb over another, conditioned on all other elements present in the sentence, with native speaker ratings of the suitability of these verbs in those sentences. It therefore measures, in more detail than the forced choice task, how well model-predictions align with native speaker intuitions.
The same 60 sentences as selected for the forced choice task presented in Section 4.1.1 were used in the acceptability ratings task. Yet, instead of offering them to native speakers in the form in which they occurred in the corpus, we created six versions of each sentence, using each of the six TRY verbs. Six stimulus sets were derived in such a way that the probability distributions estimated by the polytomous logistic regression model were equally well represented across all six sets. Within each set, the sentence order was randomized once, to avoid having more likely or more unlikely items cluster together, and each participant saw 10 cases of each verb. Since the literary corpus model predicted probabilities for all six verbs in each context, precise predictions about acceptability are available for all possible verb-by-context combinations in the form of probabilities of occurrence.
One hundred and three adult native speakers of Russian were recruited via e-mail announcements and through personal contacts. The participants were randomly assigned to one of the six lists. The vast majority either held a university degree or was studying for one. Respondents could enter a prize drawing where in total six Amazon or Ozon vouchers of £15 each could be won.
The participants were given the following instructions (in Russian):
In this experiment you will be asked to rate how natural sentences sound. We are specifically interested in the use of verbs meaning TRY such as probovat’, pytat’sja, starat’sja, silit’sja, norovit’ and poryvat’sja. There are 72 sentences in total and we would like you to rate them on a scale from 1 (sounds very strange) to 10 (sounds completely natural). Work at a quick pace, don’t think too long over one answer, don’t go back and change things: there are no right or wrong answers and we are interested in your first choice.
6.2 Results and discussion
For the analysis of the data, the raw acceptability ratings were residualized against participant and position of the sentence in the experiment, i. e. acceptability was regressed on participant and position of the sentence in the experiment and the residuals from this regression were used. This ensures that (what remained of) each acceptability rating was free of differences in how participants used the scale, or how their ratings changed over the course of the experiment. The residualized ratings were also rescaled so that each participant’s used the entire range (1–10). Our results, visualized in Figure 2, show a clear two-way distinction between low-probability items for which the acceptability can vary, with acceptability then converging and finally linearly increasing from p-values of 0.15, as shown by the red line. This means that, whereas the high probability of a verb given its context by and large entails acceptability of that verb in that context (as witnessed by the relatively empty lower right hand quadrant), the (relative) low probability of a verb given its context does not entail lower acceptability. In other words, the probability that the corpus model calculates for encountering each of the six verbs in the 60 test sentences roughly converges with how acceptable each sentence will be for adult native speakers for all but the lowest-probability cases (i. e. from p=0.15 upwards).
This result confirms previous findings by Arppe and Järvikivi (2007), Divjak (2008) and Bermel and Knittl (2012) who concluded that meta-linguistic acceptability relates to probability in a non- straightforward way, as both high and low probable items may exhibit a high degree of acceptability. In fact, Figure 2 shows that the low probability of an item given its context can correlate with any degree of acceptability.
The goal of much computational modelling work is to develop the best – i. e. most accurate – model of the phenomenon in question. As we have seen, once its predictions were adjusted for verb frequency, the Divjak and Arppe (2013) model for choosing between 6 Russian near-synonyms was able to predict the verb that actually occurred in the test corpus with 47% accuracy. While this may seem disappointing at first, a comparison with the performance of 134 human judges reveals that this is actually an excellent result. Many linguistic phenomena are simply not fully predictable, and if we are interested in modelling human knowledge, we should compare our models’ performance to that of human respondents.
To investigate this further, we created 134 models which used a random selection of the features, and they all performed within the human range. This demonstrates that a very large number of models can approximate human behaviour, which is in itself hugely varied. Divjak and Arppe (2013: 245) noted already that “there would appear to exist some redundancy among the properties, which testifies to the inherent multicollinearity of linguistic variables that is extremely difficult, if not impossible, to eliminate, as well as to a degree of potentially significant divergence in possible property combinations leading to similar model fit and accuracy.” Any given feature seems predictable from many other features. Because of this redundancy, an utterance can be produced in (unobservably) different ways, which explains how individual differences and uniformity across the community can co-exist (Hurford 2000; Barth and Kapatsinski 2014; Dąbrowska 2013, 2014). Thus, while multicollinearity can be a major headache for statistical modelling (but see Harrell 2001), it may be a blessing for language learners, in that it enables speakers to behave in a way that is broadly similar to that of other speakers even when they all have different underlying grammars. This, combined with the considerable differences in the performance of human participants, suggests that rather than trying to find the single “best” model, it may be more productive to develop a range of models reflecting the range of human performance (as already suggested by Lauri Carlson, cf. Arppe 2008: 208); for a practical implementation, see Barth and Kapatsinski 2014).
Second, our results suggest improvements to future models of linguistic data. Study 3 confirms that meta-linguistic acceptability relates to probability in a non- straightforward way, as combinations with a likelihood of p > 0.1 tend to be judged as acceptable but items below this threshold may exhibit a high as well as a low degree of acceptability. In the case of a six-way choice, the absence of a clear correlation between probability and acceptability is likely due to the fact that low probability can be the result of competition between a number of equiprobable items, i. e. items that are equally likely given the context. This indicates that such information would need to be brought into linguistic models to increase their cognitive reality. Although the difference between the onomasiological and semasiological components of word meaning dates back to Structuralism and Geeraerts et al. (1994) have outlined an overall framework for quantitative onomasiology, we are not aware of any corpus-based modelling work that would have factored onomasiology into its statistical model. Efforts are underway (Divjak and Arppe, in progress) to model this phenomenon using measures from Information Theory such as entropy that captures uncertainty.
Finally, the results reported here also raise some new questions. Although the accuracy of the frequency-adjusted Divjak and Arppe (2013) model was similar to that of the average Russian speaker, it did not perform as well as the participants as a group. We hinted earlier that this is probably due to the fact that the individual differences between speakers were much larger than those between the models used in Study 2. This suggests that speakers differ not just in which features they track, but also how many features they are able to track, and possibly also in their sensitivity to frequency effects. A second line of inquiry that will shed light on this issue is more technical in nature and considers alternative ways of evaluating the model’s performance, by steering clear of considering the highest probability option as the chosen option (cf. the criticism levelled at measures of classification accuracy for multivariate models that model probability distributions, cf. Hosmer and Lemeshow 2000). And finally, if the field of linguistics adopts the approach advocated in this paper, and starts to test corpus-based models against human performance routinely, the cognitive plausibility of the algorithm should be considered as a goodness-of-fit criterion, particularly in research within cognitive linguistic paradigms. Baayen et al. (2013) have shown that statistical classifiers based on cognitively realistic approximations of how humans learn such as Naive Discriminative Learning perform as well as regression models for binary choices. Preliminary results support this finding for more complex corpus models that predict a four-way polytomous choice (Arppe and Baayen 2011).
Capitalizing on the findings we have presented will help linguists address some interesting theoretical questions that have hitherto remained unanswered. As noted earlier, language acquisition researchers worry about how learners know which features to track. The results of Study 2 suggest that it does not really matter what exactly learners track, as long as they track enough features. The results of the random variable selection in particular point to overlapping property combinations making up the core of a lexeme; this would make it possible for speakers to draw largely similar interpretations regarding lexemes even though the individual properties they have tracked and recorded differ. What this implies for the degree to which all speakers of a language share the same contextual property associations, and thus also any abstract prototypes derived from such sets of properties, requires further research.
The experiments received ethical approval from the University of Sheffield, School of Languages & Cultures. The financial support of the University of Sheffield in the form of a SURE summer research internship to Clare Gallagher is gratefully acknowledged; Clare set up the acceptability ratings study and recruited participants for this task.
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For robustness, we also ran the comparative modeling procedure using 2,500 random models. The prediction accuracy here ranges from 23.3% to 48.3% (but with comparable mean 38.6% and median 38.3% as the 134 models). These models also share 12 out of 18 properties, as illustrated in Table 12 below
This verification procedure confirms our findings: the best model invests heavily in formal properties such as tense, aspect and mood as well as properties referring to the clause or sentence; together they make up 10 out of 18 properties used. Although tracking these significantly improves prediction accuracy, they are not typically included in lexical semantic studies while the usual suspects, i.e. semantic properties, seem less reliable predictors for the choice of one near-synonym over another.
Divjak, Dagmar; Dąbrowska, Ewa; Arppe, Antti, 2015, “Replication data for: Machine meets man: evaluating the psychological reality of corpus-based probabilistic models”, http://hdl.handle.net/10037.1/10246 UiT Open Research Data.
There are a number of early studies that employ multiple explanatory variables but do not use these to construct multivariate models. Instead, they consider all possible unique variable-value combinations as distinct conditions (e. g. Gries 2002; Featherston 2005).
Arppe and Järvikivi (2007) criticize Bresnan’s set-up of operationalizing naturalness as a zero-sum game, with naturalness between the two alternatives always adding up to the same value, i. e. 100, as their own study shows that even strong differences in terms of preference might nevertheless exhibit relatively small differences in acceptability. However, Bresnan’s results would seem to indicate that the human participants were agreeing with the corpus-based estimates of the proportions of choice (in the long run) between the two alternatives (rather than with their naturalness). Of course, we cannot be sure what participants in a experiment are doing, regardless of how the instructions are formulated (cf. Penke and Rosenbach 2004).
How many features would be enough requires further investigation, but preliminary results from a 1000-fold random selection of 18 variables from the original full 26-variable set, as reported in Divjak and Arppe (2013), reveal the following: the mean accuracy for these 1000 random models was 45.95%, ranging from 26.87% to 51.59. The best 100 random models (with accuracy values ranging from 49.44% to 51.59%) had on average 11 (60.0%) variables values in common with each other, ranging from as few as six up to as many as 15 common variables in individual pairwise comparisons. Moreover, the best and worst models had only eight variables (44%) in common, which probably explains the substantial difference in model performance. We do not pursue this question further here because our interest is not in finding the most parsimonious model, but rather in exploring the impact of the contextual effects that we had selected on the basis of prior studies. As Tarpey (2009), echoing Box (1979), put it, “in any given data analysis situation, a multitude of models can be proposed. Most of these will be useless… and perhaps a few will be useful.”