SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations

Maru, Marco; Scozzafava, Federico; Martelli, Federico; Navigli, Roberto

doi:10.18653/v1/d19-1359

Cited by 40 publications

(31 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As future work, we plan to extend our approach to cover the other main POS tags, i.e., verbs, adjectives and adverbs, by exploiting other knowledge resources, such as VerbAtlas (Di Fabio, Conia, and Navigli 2019) and SyntagNet (Maru et al 2019). Moreover, we plan to leverage the sense embeddings provided by SENSEMBERT to create high-quality silver data for WSD in multiple languages.…”

Section: Resultsmentioning

confidence: 99%

SensEmBERT: Context-Enhanced Sense Embeddings for Multilingual Word Sense Disambiguation

Scarlini

Pasini

Navigli

2020

AAAI

Self Cite

101

View full text Add to dashboard Cite

Contextual representations of words derived by neural language models have proven to effectively encode the subtle distinctions that might occur between different meanings of the same word. However, these representations are not tied to a semantic network, hence they leave the word meanings implicit and thereby neglect the information that can be derived from the knowledge base itself. In this paper, we propose SensEmBERT, a knowledge-based approach that brings together the expressive power of language modelling and the vast amount of knowledge contained in a semantic network to produce high-quality latent semantic representations of word meanings in multiple languages. Our vectors lie in a space comparable with that of contextualized word embeddings, thus allowing a word occurrence to be easily linked to its meaning by applying a simple nearest neighbour approach.We show that, whilst not relying on manual semantic annotations, SensEmBERT is able to either achieve or surpass state-of-the-art results attained by most of the supervised neural approaches on the English Word Sense Disambiguation task. When scaling to other languages, our representations prove to be equally effective as their English counterpart and outperform the existing state of the art on all the Word Sense Disambiguation multilingual datasets. The embeddings are released in five different languages at http://sensembert.org.

show abstract

Section: Resultsmentioning

confidence: 99%

SensEmBERT: Context-Enhanced Sense Embeddings for Multilingual Word Sense Disambiguation

Scarlini

Pasini

Navigli

2020

AAAI

Self Cite

101

View full text Add to dashboard Cite

show abstract

“…As future work, we plan to take full advantage of the novel semantic features available in VerbAtlas, such as wide-coverage selectional preferences and synset-level information, by exploiting them in multilingual SRL and Word Sense Disambiguation tasks. Our plans include integrating the selectional preferences from SyntagNet (Maru et al, 2019), a new, large-scale lexical-semantic combination resource. We also plan to extend our methodology to nouns and adjectives, in a similar fashion to (O'Gorman et al, 2018) and connect the resulting frames to those in VerbAtlas.…”

Section: Discussionmentioning

confidence: 99%

VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling

Fabio¹,

Conia

Navigli

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

Self Cite

View full text Add to dashboard Cite

We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from Word-Net into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments. We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to improvements on both the in-domain and out-ofdomain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org.

show abstract

“…SyntagNet (Maru et al, 2019) is a repository containing approximately 88K lexical-semantic collocations, i.e., pairs of WordNet synsets that co-occur more frequently than would be expected. 1 For example, the concepts {coach, bus, autobus} (A vehicle carrying many passengers) and {driver, motorist} (The operator of a motor vehicle) appear in SyntagNet as they form a collocation.…”

Section: Preliminariesmentioning

confidence: 99%

“…As for the number of sentences t and ξ, we ranged them between 50 and 300 with a 50 step 9 and selected the values that maximized the performance in terms of F1 of ARES on SemEval-07, 10 i.e., t = 150 and ξ = 50. As regards the window size w, we followed Maru et al (2019) and set w = 3.…”

Section: Ares Configurationmentioning

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)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations

Cited by 40 publications

References 17 publications

SensEmBERT: Context-Enhanced Sense Embeddings for Multilingual Word Sense Disambiguation

SensEmBERT: Context-Enhanced Sense Embeddings for Multilingual Word Sense Disambiguation

VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling

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

Contact Info

Product

Resources

About