Presentation is loading. Please wait.

Presentation is loading. Please wait.

Can Lexical Semantics Predict Grammaticality in English Support- Verb-Nominalization Constructions? Anthony DavisLeslie Barrett CodeRyte, Inc.TheLadders.com.

Similar presentations


Presentation on theme: "Can Lexical Semantics Predict Grammaticality in English Support- Verb-Nominalization Constructions? Anthony DavisLeslie Barrett CodeRyte, Inc.TheLadders.com."— Presentation transcript:

1 Can Lexical Semantics Predict Grammaticality in English Support- Verb-Nominalization Constructions? Anthony DavisLeslie Barrett CodeRyte, Inc.TheLadders.com

2 English SVN constructions support verb + nominalization (direct object) ≈ main verb (root of nominalization) – ‘take a walk’ ≈ ‘walk’, ‘hold a belief’ ≈ ‘believe’ Attested/acceptable SVN combinations appear somewhat (though not completely) arbitrary – ?‘make a walk’, but ‘make a decision’ (cf. French ‘prendre une décision’) – ‘have/harbor a belief’

3 English SVN constructions How much is predictable and how much just idiomatic? Can semantic properties of support verbs and nominalizations account for the observed combinations?

4 English SVN constructions How much is predictable and how much just idiomatic? Can semantic properties of support verbs and nominalizations account for the observed combinations? – Semantic compatibility of shared argument(s): ‘feel pity’, ‘perform/undergo an evaluation’ But many cases are less clear: ‘take a bath’, ‘hold the belief/?knowledge’, ‘have the belief/knowledge’

5 English SVN constructions How much is predictable and how much just idiomatic? Can semantic properties of support verbs and nominalizations account for the observed combinations? – Aspectual or Aktionsart compatibility: For instance, stative SV and N: ‘have/hold/harbor a belief/dislike’ Less clear for other classes

6 English SVN constructions How much is predictable and how much just idiomatic? Can semantic properties of support verbs and nominalizations account for the observed combinations? – Levin class features (fine-grained semantic properties)…

7 Strategy of this research Find SVN combinations in a corpus Cluster support verbs and nominalizations by Levin class features Test for statistically significant effects Evaluation and conclusion

8 Finding SVN combinations Use pointwise mutual information (PMI) between verbs and the heads of their direct objects to find verb-object pairs that are strongly associated Select those with high PMI values that are SVN constructions (or at least plausibly are, in most cases)

9 PMI between verbs and their objects PMI measures how closely two events are associated: Here, we calculate PMI values over ~150 million words of parsed New York Times articles (we used the XLE parser from PARC and discarded sentences longer than 25 words)

10 Examples of PMI values Some objects of ‘eat’ – We also calculated PMI for broader semantic classes of terms, e.g.: food, substance. Semantic classes were taken from Cambridge International Dictionary of English (CIDE); there are about 2000 of them, arranged in a shallow hierarchy verb=eat obj=hamburger obj=pretzel obj in class Food obj in class Substance7.89 obj in class Sound0.324 obj in class Place0.448

11 Examples of PMI values 20 highest PMI values for SVN combinations:

12 Finding SVN combinations We examined the 200 highest-PMI verb-object combinations (PMI > 5) in which the verb is commonly a support verb, and selected the 146 of them that actually appear to be SVN constructions for further analysis – These combinations contain 21 support verbs and 118 nominalizations

13 Levin-class features Levin (1993) is a well-known study of verb diathesis alternations and their underlying lexical semantics Several thousand verbs are categorized by the alternations they exhibit, and in groups with others verbs displaying the same set of alternations We use these categories as features of support verbs and the verb roots of nominalizations

14 Levin-class examples 2. Alternations involving arguments within VP – 2.1 Dative alternation – 2.2 Benefactive alternation – 2.3 Locative alternation … 26. Verbs of creation and transformation – 26.1 build verbs – 26.2 grow verbs – 26.3 verbs of preparing

15 Levin-class features For each verb (support verb or root of nominalization), create a vector of binary features from its Levin-class memberships: – Example: give

16 Levin-class features For each verb (support verb or root of nominalization), create a vector of binary features from its Levin-class memberships: – Example: give – Levin-classes: , 2.1, 13.1

17 Levin-class features For each verb (support verb or root of nominalization), create a vector of binary features from its Levin-class memberships: – Example: give – Levin-classes: , 2.1, 13.1 – vector …22.1…1313.1… 11110…11 11

18 Two ways to cluster the vectors Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions

19 Two ways to cluster the vectors Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions – support verbnominalization

20 Two ways to cluster the vectors Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions – support verbnominalization SV vectornom vector

21 Two ways to use feature vectors Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions – support verbnominalization SV vectornom vector concatenated SVN vector

22 Two ways to use feature vectors Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions – support verbnominalization SV vectornom vector concatenated SVN vector then cluster these concatenated SVN vectors

23 Two ways to use feature vectors Alternatively, cluster the two sets of vectors separately... SV vectorsnom vectors

24 Two ways to use feature vectors … and look for correlations between SV clusters and nom clusters in the 146 SVN pairs SV vectors nom vectors more pairs than expected here fewer pairs than expected here

25 Clustering concatenated vectors 146 SVN pairs clustered into 4, 5, 6, or 7 clusters – CLUTO (Karypis, et al), using “direct” clustering method and cosine similarity metric – Resulting clusters (in 7-way clustering) are a mixed bag… All and only the 12 pairs with take as support verb All 13 pairs with feel, plus 3 (of 8) with suffer Nominalizations denoting emotion (e.g., (‘harbor disdain/resentment’, ‘extend appreciation’) Nominalizations denoting creation, transformation, or destruction (‘undergo transformation/conversion’, ‘suffer alteration/devastation’, ‘perform extermination’)

26 Significance of clusters Does the average PMI of SVN pairs differ significantly across clusters? – Can’t make any assumptions about distributions of PMI scores, so we use score ranks – Test with Kruskal-Wallace analysis of variance (still assumes, perhaps wrongly, identical distributions of ranks; test is for equality of medians) – Test statistic is:

27 Significance of clusters Results fall short of significance (P ≈ 0.08) No support for “better” clusters having significantly higher PMI values Features of individual support verbs (like take)may overwhelm any semantic effects

28 Clusters of support verbs ClusterSupport verbs in cluster 0find, get, reach, undergo 1do, extend, give, raise, show 2feel, harbor, hold, maintain, suffer 3create, effect, form, have, make, perform, take

29 Clusters of nominalizations In all clusterings (4-7 clusters), cluster 0 has the same members: – ‘ache’, ‘admiration’, ‘appreciation’, ‘desire’, ‘dislike’, ‘enjoyment’, ‘evaluation’, ‘feeling’, ‘need’, ‘pity’, ‘regret’, ‘resentment’, ‘respect’, ‘reverence’, ‘taste’, ‘trust’, ‘veneration’, ‘want’ – Clearly, there’s some underlying semantic similarity here (emotion, sensation, judgment)

30 Contingency tables for SVN pairs We examined the distribution of SVN pairs by the cluster membership of their support verbs and nominalizations, for all clusterings – Example for 3 SV and 4 nom clusters: nom cl. SV cl

31 Chi-squared tests

32 Chi-squared, “leaving one out” Does significance vanish when one cluster is removed? yes (3 support verb & 4 nominalization clusters)

33 What’s the source of the significance? SV cluster 2: – feel, harbor, hold, maintain, suffer – The most distinguishing and discriminative Levin class features are “29: verbs with predicative complements” and its subclass “29.5: conjecture verbs’ SV cluster 3: – create, effect, form, have, make, perform, take – The most distinguishing and discriminative Levin class feature is “26: verbs of creation and transformation”

34 What’s the source of the significance? Nominalization cluster 0: – ache, admiration, appreciation, desire, dislike, etc. – The most distinguishing and discriminative Levin class features are “2: Alternations involving arguments within VP” and some of its subclass features This effect is unsurprising – Support verbs denoting creation or transformation aren’t a good semantic match for nominalizations denoting emotion, sensation, or judgment – The number of SVN pairs with SV from cluster 2 and Nom from cluster 0 is low in our tables – However, the Levin-class features characterizing Nom cluster 0 are not directly related to this semantic mismatch

35 Evaluation Overall, the Levin-class features appear not to be the key to understanding semantic regularities (to the extent they exist) in SVN constructions; why?

36 Evaluation Overall, the Levin-class features appear not to be the key to understanding semantic regularities (to the extent they exist) in SVN constructions; why? – The data and analysis we have employed here fail to reveal the genuine relationship between the semantic factors underlying Levin classes and those underlying the acceptability of SVN constructions

37 Evaluation Overall, the Levin-class features appear not to be the key to understanding semantic regularities (to the extent they exist) in SVN constructions; why? – The semantic factors underlying the acceptability of SVN constructions are indeed different from those underlying Levin classes, so no strong correlation is to be expected

38 Evaluation Overall, the Levin-class features appear not to be the key to understanding semantic regularities (to the extent they exist) in SVN constructions; why? – The role of semantic factors in the acceptability of SVN constructions is overshadowed by other considerations that we have not tested for – SVN acceptability is probably somewhat arbitrary; therefore, no strong correlation is to be expected

39 ¡Thanks! ¿Questions? Thanks to Oliver Jojic and Robert Rubinoff at StreamSage for the PMI calculations, to Shachi Dave at StreamSage for running the XLE parser, and to PARC for the use of the parser.


Download ppt "Can Lexical Semantics Predict Grammaticality in English Support- Verb-Nominalization Constructions? Anthony DavisLeslie Barrett CodeRyte, Inc.TheLadders.com."

Similar presentations


Ads by Google