A Clique-based Graphical Approach to Detect Interpretable Adjectival Senses in H ungarian (2022)

Abstract: The present paper introduces an ongoing research which aims to detect interpretable adjectival senses from monolingual corpora applying an unsupervised WSI approach. According to our expectations the findings of our investigation are going to contribute to the work of lexicographers, linguists and also facilitate the creation of benchmarks with semantic information for the NLP community. For doing so, we set up four criteria to distinguish between senses. We experiment with a graphical approach to model our criteria and then perform a detailed, linguistically motivated manual evaluation of the results.

## introduction the objective of this ongoing research is to model human intuition regarding meaning distinctions, and anchor it to observable data. its importance is given by the fact that according to several authors (eg. @xcite human intuition on sense distinctions varies greatly among individuals, which in turn has a serious effect on lexicography, lexical semantics and nlp, as well. it goes without saying in the lexicographic community that in spite of scrupulous corpus-based investigations, monolingual dictionaries greatly vary with regard to their macro-and microstructure @xcite . the same problem arises in the field of nlp: the sense inventories or knowledge-bases exhibit a great variance regarding how fine-grained meaning distinctions they apply. although lexical semantics in linguistics and word sense induction in nlp are widely studied fields (cf. @xcite @xcite , to our knowledge there is still no agreement on how the meaning space of a word should be partitioned to obtain well-motivated senses. for instance, @xcite introduces a very fine-grained meaning distinction asserting that "adjectives such as good have multiple meanings depending on what they are modifying: good car, good meal, good knife". however, he also adds that good may be conceived of merely "as a positive evaluation of the nominal head it is modifying." accordingly, the present experiment has two main objectives: first, we aim to come up with a definition that is able to provide necessary criteria to distinguish between senses. this definition should enable us to anchor meaning distinctions to not only a set of contexts, but conceptual categories as well. secondly, we aim to model this definition via an unsupervised approach that is able to grasp this definition to minimize the role of human introspection in meaning distinction. we think that our approach is quite promising as one of the main drawbacks of unsupervised models is their poor interpretability, as pointed out by @xcite . on top of that, in their survey they tied graphical models to knowledge-based semantic representations, which implies that unsupervised graph-based wsi is underrepresented in the field. the usual conception of meaning starts from meaning identity: the definition of synonymy (two expressions are synonymous iff they are interchangeable in every context preserving the original meaning) has a long tradition going back at least to @xcite , and all the senses that are not synonyms are considered to be different senses. the subsequent research tends to accept this chain of thoughts. however, in the present discussion we put it in the other way: as opposed to frege and his followers, we do not give a definition for synonymy, but give one to distinguish between meanings. this choice is motivated by the fact that the notion of synonymy is intimately tied to truthconditions, which are notoriously missing from pure distributional semantics. that is why it is so hard to detect true synonyms solely on distributional grounds. and indeed, automatically detected synonym-classes tend to cover also tight seman-tic classes, such as names of nations, colors, even antonyms exhibiting very similar distributional behavior. starting from the presupposition that attributive adjectives can be characterized in a rather simple feature space -constituted only by the following nouns -in the present research we confine ourselves to the investigation of the semantic properties of attributive adjectives. the paper is structured as follows: in section 2 our hypotheses are presented, section 3 describes our methodology, in section 4 we present our validation techniques, while section 5 focuses on the evaluation of our results. we conclude with a summary in section 6. ## criteria for meaning distinction in what follows, we describe the applied criteria, which were implemented in the next phase. contrary to the usual procedure of definition, instead of searching an identity criteria to "give the necessary and sufficient conditions for a to be identical to b when a and b are ks" (cf. @xcite , we search for necessary and sufficient conditions to discriminate between a and b. that is, instead of modeling synonymy, we strive to grasp when the target word surely conveys different meanings on distributional grounds. for doing so, we introduce the notion of near-synonymy (cf. @xcite -a relaxed version of synonymy: two words are near-synonyms if they are interchangeable in a restricted set of contexts so that they preserve the meaning of the original sentence. foot_0 moreover, in accordance with our original purpose (i.e. meaning distinction), we also consider the members of tight semantic classes to be near-synonyms, inasmuch various tight semantic classes denote different senses of a word, even though they do not preserve the truth value. 2 according to our hypothesis two senses have to be differentiated iff: example 1 is intended to further illustrate the above criteria, using the automatically extracted two senses of the adjective napfényes ('sunny'). as can be seen, there is a near-synonym for both senses: napsütéses ('sunshiny') for the first one and napsütötte ('sunlit') for the second one. the nouns listed below the adjectives are the ones that form grammatical constructions with the near-synonyms: napfényes/napsütéses vasárnap ('sunny/sunshiny sunday'), napfényes/napsütéses nap ('sunny/sunshiny day'), etc., and napfényes/napsütötte terület ('sunny/sunlit area'), napfényes/napsütötte terasz ('sunny/sunlit terrace'), etc. however, the two sets of nouns do not overlap: there is no napsütéses terasz ('sunshiny terrace') or napsütötte nap ('sunlit day'), and the same goes for all adjective-noun pairs where the noun comes from the context noun set of the other sense. finally, the nouns that match the above criteria form a semantic category: time periods with the first sense, and areas, places with the second. we wish to examine to what extent the above conditions are necessary and sufficient to differentiate between meanings. for doing so, in section 3 an unsupervised word sense induction experiment on hungarian monolingual data will be described using cliques of target words and their contexts to retrieve senses. the workflow conceptually comprises two main stages: i) the detection of nearsynonymy classes for a given adjective, ii) discriminating between the various meanings of the given adjective by the extraction of the relevant context nouns. ## method our methodology is based on @xcite , as far as meaning distinctions are mod-eled via cliques. however, there are two main differences: first, instead of named entities we focused on adjectival meanings. as overproduction of cliques is much less pronounced in this case, clustering becomes an unnecessary step. however, the resulting cliques need to be validated in terms of the following nouns, possibly along with the subcategorization patterns of the adjectives. secondly, adjectives are represented with static dense embeddings instead of frequency based sparse vectors. ## relevant senses in the present section we introduce some linguistic consideration that had to be taken into account during the evaluation phase to detect distinct classes of attributive modification. ## conclusion and future work the present paper describes an ongoing research, which intends to apply an unsupervised wsi approach to detect interpretable senses from monolingual corpora to contribute to the work of lexicogra-phers, linguists and facilitate the creation of related benchmarks for the nlp community. for doing so, we came up with 4 necessary criteria to distinguish between senses, which were implemented in the next step. finally, a detailed evaluation of the sense distinctions was performed yielding the conclusion that although the coverage definitively needs to be improved, in many cases the attained senses were surprisingly insightful supplying interpretable and intuitively not obvious sense distinction. however, during the evaluation it turned out that belonging to multiple near-synonymy classes is only a necessary but not sufficient condition for meaning discrimination, as adjectives may have collocate nouns or subcategorizate multiple sets of nouns in a single clique (see the case of könnyű 'easy' in subsection 4.2). since this method does not rely on any external knowledge base, it should be suitable for any low-or medium-resourced language.