Monolingual Word Sense Alignment as a Classification Problem (2021)

Abstract: Words are defined based on their meanings in various ways in different resources. Aligning word senses across monolingual lexicographic resources increases domain coverage and enables integration and incorporation of data. In this paper, we explore the application of classification methods using manually-extracted features along with representation learning techniques in the task of word sense alignment and semantic relationship detection. We demonstrate that the performance of classification methods dramatically varies based on the type of semantic relationships due to the nature of the task but outperforms the previous experiments.

## introduction dictionaries are valuable resources which document the life of words in a language from various points of view. creating and maintaining such resources for a constantly changing phenomenon like human language requires much time and effort. with the expansion of collaborativelycurated resources such as wiktionary, processing lexicographical resources automatically and efficiently is of high importance recently in computational lexicography, computational linguistics and natural language processing (nlp). senses, or definitions, are important components of dictionaries where dictionary entries, i.e. lemmata, are described in plain language. therefore, unlike other properties such as references, comparisons (cf.), synonyms and antonyms, senses are unique in the sense that they are more descriptive but also highly contextualized. moreover, unlike lemmata which remain identical through resources in the same language, except in spelling variations, senses can undergo tremendous changes based on the choice of the editor, lexicographer and publication period, to mention but a few factors. therefore, the task of word sense alignment (wsa) will facilitate the integration of various resources and the creation of interlinked language resources. considering the literature, various components of the wsa task has been matters of research previously. however, a few of previous papers address wsa as a specific task on its own. in this paper, our focus is on providing explainable observations for the task of wsa using manuallyextracted features and analyze the performance of traditional machine learning algorithms for word sense alignment as a classification problem. despite the increasing popularity of deep learning methods in providing state-of-the-art results in various nlp fields, we believe that evaluating the performance of feature-engineered approaches is an initial and essential step to reflect the difficulties of the task and also, the expectations from the future approaches. ## related work the alignment of lexical resources has been previously of interest both to create resources and propose alignment approaches. in this section, we only focus on wsa techniques in the related literature. graph-based approaches have been widely used for the wsa task. @xcite propose a graph-based approach, called dijkstra-wsa, for aligning lexical-semantic resources, namely wordnet, omegawiki, wiktionary and wikipedia. in this approach, senses are represented as the nodes of a graph where the edges represent the semantic relation between them. assuming that monosemous lemmata have a more specific meaning and therefore less ambiguous to match, a semantic relation is created among the senses of such lemmata when they appear in a sense of a polysemous lemma. using dijkstra's shortest path algorithm along with semantic similarity scores and without requiring any external data or corpora, a set of possible sense matches are retrieved. in the same vein, ahmadi et al. @xcite model the alignment task as a bipartite-graph where an optimal alignment solution is selected among the combination of possible sense matches in two resources. although this algorithm performs competitively with the dijkstra-wsa technique on the same datasets, no viable solution is provided regarding the tuning of the matching algorithm. similarly, other authors (nancy and @xcite @xcite focus on linking senses without considering semantic relationships. beyond aligning lexical resources, there has been much effort in inducing semantic relationships, particularly within more generic fields such as taxonomy extraction @xcite , hypernym discovery @xcite and semantic textual similarity @xcite . although in these tasks the focus is on the relationship within words, there are a few works exploring how to induce semantic relationships between definitions. @xcite introduce an algorithm using a directed acyclic graph to construct a wordnet based on the wiktionary data and enriched with synonym and antonym relationships. using the semantic relationship annotations provided in wiktionary, the method induces a semantic hierarchy by identifying a subset within each sense that can relate two lemmas together. in addition to graphbased methods, there are various other closelyrelated fields, such as word sense disambiguation @xcite and sense embeddings @xcite , which can potentially contribute to the task of wsa. however, we could not find any previous work exploring those approaches. one major limitation regarding previous work is with respect to the nature of the data used for the wsa task. expert-made resources, such as the oxford english dictionary, require much effort to create and therefore, are not as widely available as collaboratively-curated ones like wiktionary 1 due to copyright restrictions. on the other hand, the latter resources lack domain coverage and descriptive senses. to address this, ahmadi 1 www.wiktionary.org et al. (2020) present a set of 17 datasets containing monolingual dictionaries in 15 languages, annotated by language experts with five semantic relationships according to the simple knowledge organization system reference (skos) @xcite , namely, broader, narrower, related, exact and none. our objective within this project is to explore the alignment of these opensource datasets using classification methods. ## problem definition ignoring the differences in dictionary structures and formats such as xml, lmf @xcite and @xcite , there are different lexicographic and logical ways for describing senses in a dictionary @xcite . as an example, we define our task of wsa and semantic induction as the detection of the semantic relationship between a pair of senses in two monolingual resources, as follows: assuming that the textual representation of senses as in definitions can be useful to align them, we define a few features which use the lengths of senses along with their textual and semantic similarities. in addition, we incorporate word-level semantic relationships to determine the type of relation that two senses may possibly have. to this end, we use conceptnet @xcite , an openly-available and multilingual semantic network with relational knowledge from various other resources, such as wiktionary and wordnet @xcite . a similar approach has been previously proposed for aligning bilingual with monolingual dictionaries @xcite . ## experiments ## conclusion and future work this paper presents a preliminary study on the task of word sense alignment using monolingual lexicographic datasets from 15 languages. the task is modeled as a classification task where data instances are extracted using various manuallydefined features. the classification task aims at classifying sense matches across dictionaries and also, prediction of the semantic relationship between two given senses, namely narrower, broader, exact and related. the results indicate a better performance of the proposed approach with respect to the baselines reported previously. one major limitation of the current approach is the usage of crafted features. we believe that as a future work further techniques can be used, particularly thanks to the current advances in word representations and neural networks. in addition, incorporating knowledge bases and external language resources such as corpora can be beneficial in improving to address sense ambiguity for polysemous entries.