What to Make of make? Sense Distinctions for Light Verbs (2023)

Abstract: Verbs like make, have and get present challenges for applications requiring automatic word sense discrimination. These verbs are both highly frequent and polysemous, with semantically “full” readings, as in make dinner, and “light” readings, as in make a request. Lexical resources like WordNet encode dozens of senses, making discrimination difficult and inviting proposals for reducing the number of entries or grouping them into coarser-grained supersenses. We propose a data-driven, linguistically-based approach to establishing a motivated sense inventory, focusing on make to establish a proof of concept. From several large, syntactically annotated corpora, we extract nouns that are complements of the verb make, and group them into clusters based on their Word2Vec semantic vectors. We manually inspect, for each cluster, the words with vectors closest to the centroid as well as a random sample of words within the cluster. The results show that the clusters reflect an intuitively plausible sense discrimination of make. As an evaluation, we test whether words within a given cluster cooccur in coordination phrases, such as apples and oranges, as prior work has shown that such conjoined nouns are semantically related. Conversely, noun complements from different clusters are less likely to be conjoined. Thus, coordination provides a similarity metric independent of the contextual embeddings used for clustering. Our results pave the way for a WordNet sense inventory that, while not inconsistent with the present one, would reduce it significantly and hold promise for improved automatic word sense discrimination.

## background and related work jespersen coined the term light verb to denote verbs like have, take and make that carry little (but not zero) semantic information and that select for a noun, verb, or adjective complement to form a complex predicate. in their light verb use, these verbs are semantically bleached versions of main verbs as in (1a) and (1b), respectively: english light verbs usually have a corresponding simple full verb (e.g., attempt), but there are a number of subtle semantic distinctions between the light verb construction and the full verb (for a discussion see @xcite ). automatic word sense disambiguation often relies on look-up in lexical resources like wordnet, where one confronts the challenge of dozens of different senses. wordnet includes 49 senses for make, an inventory that is often criticized by its users, but that is in fact comparable to the number of sense distinctions found in other lexical resources. for example, merriam-webster lists 25 main senses of the transitive verb, most of them with multiple subsenses. even more vexing is the fact that light and full verb uses of make are not distinguished. different proposals for grouping senses into semantically underspecified clusters have been made @xcite , but different automatic or manual efforts have resulted in multiple sense inventories that overlap only partially. we propose a data-driven method to suggest a reduced sense inventory for make based on clusters of its nominal complements. we also introduce a novel evaluation plan that is motivated by our previous study of coordination structures. in such structures, two constituents are conjoined by a coordinating conjunction, such as and or or. prior work has shown that conjoined nouns are semantically related as measured via various wordnet relations like synonymy, antonymy, and co-hyponymy @xcite . this makes anomalous utterances, such as apples and/or texting gloves, or instances of zeugma, as in she made a salad and a mess in the kitchen, unlikely or humorous. to our knowledge, previous attempts at sense distinctions via argument selection have considered only single noun complements of a verb, a difficult task given that light verbs combine with a large number of nouns. our focus in this paper is on make, but we expect our analysis to extend straightforwardly to other light verbs. ## approach we distinguish different senses of make by examining its nominal complements, or nouns that it selects as a direct object. we reason that these noun complements must be sufficiently semantically similar for the verb phrases headed by make to be well formed, and that grouping these nouns can reveal distinct uses of make that point to different senses. to achieve this aim, we extract complements from dependency corpora and find groupings by clustering their word embeddings. foot_0 ## results and discussion in total, we found 493 noun complements of make in the corpora after removing stopwords and tokens that are not present in the word2vec dictionary. the clusters found using simple k-means clustering with @xmath0. outlier clusters have been removed from this table, so we present a reduced set of 26 clusters. the clusters found using k-means clustering with @xmath1. figure 5 presents a visualization of complement clusters from this second analysis using the first two pca components. the second clustering analysis motivates a significantly reduced sense inventory while aligning with senses of make currently present in wordnet. for instance, there is a clear cluster for cases where make corresponds to cooking or preparing food (cluster #7 in however, our first analysis with a larger number of clusters captures some meaningful distinctions that are lost with a smaller value of k. for instance, this analysis provides a cluster of complements like statue and sculpture that correspond to the sense of "building" or "creating." the cluster containing money presents the sense of "gaining," and the cluster with complements such as progress and im-2 when computing pairs between distinct clusters a and b, we use the cross product. when computing in-cluster pairs for a single cluster a, we compute the combinations of elements in a. this avoids duplicate pairs or pairs in which both elements correspond to the same complement instance. cluster # size centroid words sample words 0 3 coup, coup d' etat, coup d' état coup 1 5 entry, metastasis, breast metastasis, breast, entry 2 16 word, phrase, language word, reference, lyric 3 12 noise, ambient noise, noises noise, sound 4 25 sense, impression, feel sense, assumption, representation 5 25 change, adjustment, alter alteration, revision, change 6 20 decision, recommendation, announcement conclusion, agreement, request 7 3 statue, bronze statue, sculpture statue, sculpture 8 13 friend, mother, daughter child, love, mother 9 4 comment, leave comment 10 3 cat, pet, bird pet, cat, bird 11 19 effort, attempt, endeavor project, plan, amendment 12 7 vodka, bottle, brandy wine, bottle, vodka 13 7 contribution, donation, contributions contribution, donation 14 4 reservation, reservations reservation 15 11 money, funds, dollars money, profit, buck 16 11 mistake, blunder, error blunder, mistake, error 17 19 dessert, sandwich, soup lunch, cheeseburger, food 18 10 debut, appearance, debuts cameo, debut, appearance 19 14 joke, laugh, chuckle chatter, mischief, joke 20 9 difference, disparity, discrepancy distinction, gap, impact 21 5 appointment, appointments appointment 22 7 progress, strides, improvement recovery, improvement, progress 23 11 statement, remarks, press release statement, speech, filling 24 6 adaptation, adaption, film adaptation, film 25 33 deal, agreement, offer sale, package, transfer ## limitations a limitation of our coordination evaluation approach is that the clusters to be compared must have a large number of unique members. we found two such lexically-rich clusters, but most clusters did not contain many members that were also attested in coordination phrases. we expect that with more complement data (beyond the 493 nouns from this study), we can obtain larger clusters that will be better suited for this coordination evaluation. the senses captured by these clusters also require a manual evaluation in order to reach the optimal sense distinctions, but we expect that the methodology provided in this paper can aid the process through the use of real-world data. ## conclusion in this paper, we presented a clustering analysis of complements of the light verb make using annotated ud corpora that can pave the way toward a reduced wordnet sense inventory for this verb. furthermore, we proposed and tested a novel method using coordination structures to evaluate the robustness of the complement clustering. future directions may apply this approach straightforwardly to other light verbs whose large sense inventories in wordnet have stymied word sense disambiguation efforts. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 .3 .23 .22 .24 .22 .25 .21 .26 .21 .23 .23 .2 .2 .2 .21 .23 .26 .28 .25 .3 .22 .27 .19 .29 .34 .3 .29 .26 .26 .3 .22 .28 .66 .29 .34 .17 .31 .18 .34 .37 .34 .3 .32 .26 .32 .24 .25 .29 .27 .31 .23 .27 .21 .27 .29 .28 .24 .27 .22 .25 .22 .3 .34 .31 1.0 .16 .46 .18 .38 .31 .38 .25 .54 .25 .27 .25 .22 .17 .23 .16 .5 .15 .26 .17 .21 .19 .19 .14 .15 .19 .21 .27 .31 .27 .46 .15 1.0 .17 .31 .28 .33 .23 .41 .24 .25 .26 .19 .18 .21 .18 .26 .17 .33 .17 .16 .17 .15 .17 .14 .15 .21 .29 .34 .27 .38 .17 .31 .17 .42 .41 .36 .33 .31 .29 .37 .23 .34 .37 .29 .31 .21 .28 .16 .41 .99 .48 .55 .27 .35 .55 .23 .3 .34 .28 .38 .19 .33 .17 .36 .48 .41 .39 .33 .3 .4 .2 .29 .3 .24 .25 .19 .23 .15 .33 .55 .39 .58 .23 .29 .46 .2 .26 .32 .27 .54 .14 .41 .17 .31 .27 .33 .23 .66 .23 .25 .2 .26 .26 .22 .25 .15 .24 .14 .29 .35 .3 .29 .23 1.0 .31 .21 .3 .32 .25 .27 .19 .25 .15 .37 .55 .4 .46 .25 .31 .54 average wu-palmer similarity of complements between clusters ceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers), pages 834-842, berlin, germany. association for computational linguistics. christopher thad hughes and sushant prakash. 2006. clustering wordnet senses utilizing modified and novel similarity metrics.