Can Lexical Semantics Predict Grammaticality in English Support-

Verb-Nominalization Constructions?

Anthony Davis Leslie BarrettCodeRyte, Inc.TheLadders.com

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’

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?

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’

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

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)…

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

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)

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)

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 139.249obj=pretzel 90.359obj in class Food 18.156obj in class Substance 7.89obj in class Sound 0.324obj in class Place 0.448

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

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

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

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

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-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: 1.1.2.1, 2.1, 13.1

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: 1.1.2.1, 2.1, 13.1– vector

1 1.1 1.1.2 1.1.2.1 … 2 2.1 … 13 13.1 …

1 1 1 1 0… 1 1 0… 1 1 0…

Two ways to cluster the vectors

• Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions

Two ways to cluster the vectors

• Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions– support verb nominalization

Two ways to cluster the vectors

• Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions– support verb nominalization

SV vector nom vector

Two ways to use feature vectors

• Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions– support verb nominalization

SV vector nom vector

concatenated SVN vector

Two ways to use feature vectors

• Concatenate the vectors of the support verb and the nominalization for each of the 146 SVN constructions– support verb nominalization

SV vector nom vector

concatenated SVN vector

then cluster these concatenated SVN

vectors

Two ways to use feature vectors

• Alternatively, cluster the two sets of vectors separately...

SV vectors nom vectors

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

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’)

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:

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

Clusters of support verbsCluster Support verbs in cluster

0 find, get, reach, undergo

1 do, extend, give, raise, show

2 feel, harbor, hold, maintain, suffer

3 create, effect, form, have, make, perform, take

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)

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. 0 1 2 3

0 23 17 5 17

1 14 23 4 22

2 5 18 18 32

Chi-squared tests

Chi-squared, “leaving one out”

Does significance vanish when one cluster is removed?

yes

(3 support verb &4 nominalization

clusters)

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”

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

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?

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

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

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

¡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.