Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation

Tripodi, Rocco; Navigli, Roberto

doi:10.18653/v1/d19-1009

Cited by 27 publications

(13 citation statements)

References 44 publications

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“…Word Sense Disambiguation (WSD) is a core task in lexical semantics and has mainly been tackled by two kinds of approach: knowledge-based and supervised ones. Knowledge-based methods build upon lexical knowledge bases, such as WordNet (Miller et al, 1990) and BabelNet (Navigli and Ponzetto, 2012), and employ algorithms on graphs to address the word ambiguity in texts (Moro et al, 2014;Agirre et al, 2014;Tripodi and Navigli, 2019;. These approaches do not rely on semantically-tagged training data and are hence able to scale over all the languages supported by their underlying knowledge base.…”

Section: Related Workmentioning

confidence: 99%

With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation

Scarlini¹,

Pasini²,

Navigli³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Contextualized word embeddings have been employed effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. However, it is still hard to link them to structured sources of knowledge. In this paper we present ARES (context-AwaRe Embeddings of Senses), a semi-supervised approach to producing sense embeddings for the lexical meanings within a lexical knowledge base that lie in a space that is comparable to that of contextualized word vectors. ARES representations enable a simple 1-Nearest-Neighbour algorithm to outperform state-of-the-art models, not only in the English Word Sense Disambiguation task, but also in the multilingual one, whilst training on sense-annotated data in English only. We further assess the quality of our embeddings in the Word-in-Context task, where, when used as an external source of knowledge, they consistently improve the performance of a neural model, leading it to compete with other more complex architectures. ARES embeddings for all WordNet concepts and the automatically-extracted contexts used for creating the sense representations are freely available at http://sensembert.org/ares.

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Section: Related Workmentioning

confidence: 99%

With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation

Scarlini¹,

Pasini²,

Navigli³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…Word Sense Disambiguation (WSD) is one of the challenging problems in natural language processing. Typical WSD models (Lesk, 1986;Zhong and Ng, 2010;Yuan et al, 2016;Raganato et al, 2017;Le et al, 2018;Tripodi and Navigli, 2019) are trained for a general domain. Recent works (Li and Jurafsky, 2015;Mekala et al, 2016;Gupta et al, 2019) also showed that machine-interpretable representations of words considering its senses, improve document classification.…”

Section: Word Sense Disambiguationmentioning

confidence: 99%

Contextualized Weak Supervision for Text Classification

Mekala¹,

Shang²

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers. Existing methods mainly generate pseudo-labels in a context-free manner (e.g., string matching), therefore, the ambiguous, context-dependent nature of human language has been long overlooked. In this paper, we propose a novel framework ConWea, providing contextualized weak supervision for text classification. Specifically, we leverage contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of the same word, and thus create a contextualized corpus. This contextualized corpus is further utilized to train the classifier and expand seed words in an iterative manner. This process not only adds new contextualized, highly label-indicative keywords but also disambiguates initial seed words, making our weak supervision fully contextualized. Extensive experiments and case studies on real-world datasets demonstrate the necessity and significant advantages of using contextualized weak supervision, especially when the class labels are fine-grained.

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“…Word Sense Disambiguation (WSD) -the task of assigning the correct meaning to a target word in a context -is considered to be a fundamental step towards natural language understanding (Navigli, 2018). As with many other tasks, WSD has benefited greatly from the recent advances in other fields, such as language modelling (Scarlini et al, 2020b), game theory (Tripodi and Navigli, 2019), structured knowledge integration , definition modelling and label propagation (Barba et al, 2020;, inter alia. Our experiments show that Conception can be used to create state-of-the-art sense embeddings, demonstrating empirically that our approach provides high-quality knowledge that is still not captured by recent language models.…”

Section: Word Sense Disambiguationmentioning

confidence: 99%

Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations

Conia

Navigli

2020

Proceedings of the 28th International Conference on Computational Linguistics

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To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amount of data available across languages. In this paper we address this issue and propose Conception, a novel technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. Our approach results in highcoverage representations that outperform the state of the art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation, proving particularly robust on lowresource languages. Conception -its software and the complete set of representations -is available at https://github.com/SapienzaNLP/conception.

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Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation

Cited by 27 publications

References 44 publications

With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation

With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation

Contextualized Weak Supervision for Text Classification

Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations

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