A Methodology for Large-Scale, Disambiguated and Unbiased Lexical Knowledge Acquisition Based on Multilingual Word Alignment
Authors: Francesca Grasso, Luigi Di Caro (Università di Torino, 2021)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In order to be concretely effective, many NLP applications require the availability of lexical resources providing varied, broadly shared, and language-unbounded lexical information. However, state-of-the-art knowledge models rarely adopt such a comprehensive and cross-lingual approach to semantics. In this paper, we propose a novel automatable methodology for knowledge modeling based on a multilingual word alignment mechanism that enhances the encoding of unbiased and naturally disambiguated lexical knowledge. Results from a simple implementation of the proposal show relevant outcomes that are not found in other resources.

Introduction
Lexical resources constitute a key instrument for many NLP tasks such as Word Sense Disambiguation and Machine Translation. However, their potential may vary widely depending on the nature of the lexical-semantic knowledge they encode, as well as on how the linguistic data are stored and linked within the network <cit.>. The resources that are presently available, such as WordNet <cit.>, typically encode lexical-semantic knowledge mainly in terms of word senses, defined by textual (i.e. dictionary) definitions, and lexical entries are linked and put in context through lexical-semantic relations. These relations, being only of a paradigmatic nature, are characterized by a sharing of the same defining properties between the words and a requirement that the words be of the same syntactic class <cit.>.  Typically related words are therefore not represented due to the absence of syntagmatic links.  Additionally, word senses suffer from a lack of explicit common-sense knowledge and context-dependent information. Finally, the well-known fine granularity of word senses in WordNet <cit.> is due to the lack of a meaning encoding system capable of representing concepts in a flexible way.  Other kinds of resources such as FrameNet <cit.> and ConceptNet <cit.> present the same issue, while returning different types and degrees of structural semantic information and disambiguation capabilities.    In this contribution, we provide a novel methodology for the retrieval and representation of unbiased and naturally disambiguated lexical information  that relies on a multilingual word alignment mechanism. In particular, we exploit textual resources in different languages[In this work, we start with the combination of three languages: English, German and Italian.] in order to acquire and align varied lexical-semantic material of the form <target-concept, {related words}^k> that are common and shared by all the k languages involved. As we demonstrate through a simple implementation, our method allows to create new lexical-semantic relations between words that are not always available in other resources, as well as to perform an automatic word sense disambiguation process. This system therefore enhances the encoding of prototypical semantic information of concepts that is also likely to be free from strong cultural-linguistic and lexicographic biases.   The benefits provided by our novel multilingual word alignment mechanism are thus fourfold: (i) a linguistic and lexicographic de-biasing of lexical knowledge; (ii) naturally-disambiguated aligned lexical entries; (iii) the discovery of novel lexical-semantic relations; and (iv) the representation of prototypical semantic information of concepts in different languages.

Conclusion
In this paper we proposed an original methodology for acquiring and encoding lexical knowledge through a novel yet simple mechanism of multilingual alignment. The aim was to represent varied, disambiguated, and language-unbounded lexical knowledge by minimizing strong linguistic and lexicographic biases. A simple implementation and experimentation on 456 concepts carried to unveil around 76K aligned lexical-semantic features, of which more than 80% resulted new when compared with a current state-of-the-art resource such as BabelNet. Future directions include the use of more languages and large-scale runs over thousands of main concepts <cit.>