Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information

Abstract

Neural architectures are the current state of the art in Word Sense Disambiguation (WSD). However, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKB). We present Enhanced WSD Integrating Synset Embeddings and Relations (EWISER), a neural supervised architecture that is able to tap into this wealth of knowledge by embedding information from the LKB graph within the neural architecture, and to exploit pretrained synset embeddings, enabling the network to predict synsets that are not in the training set. As a result, we set a new state of the art on almost all the evaluation settings considered, also breaking through, for the first time, the 80% ceiling on the concatenation of all the standard allwords English WSD evaluation benchmarks. On multilingual all-words WSD, we report state-of-the-art results by training on nothing but English.

XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization

“…The ability to identify the intended sense of a polysemous word in a given context is one of the fundamental problems in lexical semantics. It is usually addressed with two different kinds of approaches relying on either sense-annotated corpora (Bevilacqua and Navigli, 2020;Scarlini et al, 2020;Blevins and Zettlemoyer, 2020) or knowledge bases (Moro et al, 2014;Agirre et al, 2014;Scozzafava et al, 2020). Both are usually evaluated on dedicated benchmarks, including at least five WSD tasks in Senseval and SemEval series, from 2001 (Edmonds and Cotton, 2001) to 2015 (Moro and Navigli, 2015a) that are included in the Raganato et al (2017)'s test suite.…”

Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations

“…Kumar et al (2019) proposed the EWISE approach which constructs sense definition embeddings also relying on the network structure of Word-Net for performing zero-shot WSD in order to handle words without any sense-annotated occurrence in the training data. Bevilacqua and Navigli (2020) introduces EWISER as an improvement over the EWISE approach by providing a hybrid knowledgebased and supervised approach via the integration of explicit relational information from WordNet. Our approach differs from both (Kumar et al, 2019) and (Bevilacqua and Navigli, 2020) in that we are not exploiting the structural properties of WordNet.…”

“…Bevilacqua and Navigli (2020) introduces EWISER as an improvement over the EWISE approach by providing a hybrid knowledgebased and supervised approach via the integration of explicit relational information from WordNet. Our approach differs from both (Kumar et al, 2019) and (Bevilacqua and Navigli, 2020) in that we are not exploiting the structural properties of WordNet.…”

Implementing an End-to-End Treebank-Informed Pipeline for Bulgarian

“…The spaCy framework does not provide off-the-shelf WSD functionality, therefore a different solution had to be adapted. The EWISER system (Bevilacqua and Navigli, 2020) was chosen, due to several considerations: superior accuracy compared to other systems, easy integration with spaCy, and multilingual support. EWISER improves the state-of-the-art in WSD on the popular evaluation framework for English (Raganato et al, 2017), "breaking through the 80% glass ceiling".…”