Delving into the uncharted territories of Word Sense Disambiguation

The automatic disambiguation of word senses, i.e. Word Sense Disambiguation, is a long-standing task in the field of Natural Language Processing; an AI-complete problem that took its first steps more than half a century ago, and which, to date, has apparently attained human-like performances on standard evaluation benchmarks. Unfortunately, the steady evolution that the task experienced over time in terms of sheer performance has not been followed hand in hand by adequate theoretical support, nor by careful error analysis. Furthermore, we believe that the lack of an exhaustive bird’s eye view which accounts for the sort of high-end and unrealistic computational architectures that systems will soon need in order to further refine their performances could lead the field to a dead angle in a few years. In essence, taking advantage of the current moment of great accomplishments and renewed interest in the task, we argue that Word Sense Disambiguation is mature enough for researchers to really observe the extent of the results hitherto obtained, evaluate what is actually missing, and answer the much sought for question: “are current state-of-the-art systems really able to effectively solve lexical ambiguity?” Driven by the desire to become both architects and participants in this period of pondering, we have identified a few macro-areas representatives of the challenges of automatic disambiguation. From this point of view, in this thesis, we propose experimental solutions and empirical tools so as to bring to the attention of the Word Sense Disambiguation community unusual and unexplored points of view. We hope these will represent a new perspective through which to best observe the current state of disambiguation, as well as to foresee future paths for the task to evolve on. Specifically, 1q) prompted by the growing concern about the rise in performance being closely linked to the demand for more and more unrealistic computational architectures in all areas of application of Deep Learning related techniques, we 1a) provide evidence for the undisclosed potential of approaches based on knowledge-bases, via the exploitation of syntagmatic information. Moreover, 2q) driven by the dissatisfaction with the use of cognitively-inaccurate, finite inventories of word senses in Word Sense Disambiguation, we 2a) introduce an approach based on Definition Modeling paradigms to generate contextual definitions for target words and phrases, hence going beyond the limits set by specific lexical-semantic inventories. Finally, 3q) moved by the desire to analyze the real implications beyond the idea of “machines performing disambiguation on par with their human counterparts” we 3a) put forward a detailed analysis of the shared errors affecting current state-of-the-art systems based on diverse approaches for Word Sense Disambiguation, and highlight, by means of a novel evaluation dataset tailored to represent common and critical issues shared by all systems, performances way lower than those usually reported in the current literature

A Unified multilingual semantic representation of concepts

Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot be used effectively for the representation of individual word senses. We put forward a novel multilingual concept representation, called MUFFIN , which not only enables accurate representation of word senses in different languages, but also provides multiple advantages over existing approaches. MUFFIN represents a given concept in a unified semantic space irrespective of the language of interest, enabling cross-lingual comparison of different concepts. We evaluate our approach in two different evaluation benchmarks, semantic similarity and Word Sense Disambiguation, reporting state-of-the-art performance on several standard datasets

Generationary or “How We Went beyond Word Sense Inventories and Learned to Gloss”

Mainstream computational lexical semantics embraces the assumption that word senses can be represented as discrete items of a predefined inventory. In this paper we show this needs not be the case, and propose a unified model that is able to produce contextually appropriate definitions. In our model, Generationary, we employ a novel span-based encoding scheme which we use to fine-tune an English pre-trained Encoder-Decoder system to generate glosses. We show that, even though we drop the need of choosing from a predefined sense inventory, our model can be employed effectively: not only does Generationary outperform previous approaches in the generative task of Definition Modeling in many settings, but it also matches or surpasses the state of the art in discriminative tasks such as Word Sense Disambiguation and Word-inContext. Finally, we show that Generationary benefits from training on data from multiple inventories, with strong gains on various zeroshot benchmarks, including a novel dataset of definitions for free adjective-noun phrases. The software and reproduction materials are available at http://generationary.org

Recent Trends in Word Sense Disambiguation : A Survey

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Vec2Gloss: definition modeling leveraging contextualized vectors with Wordnet gloss

Contextualized embeddings are proven to be powerful tools in multiple NLP tasks. Nonetheless, challenges regarding their interpretability and capability to represent lexical semantics still remain. In this paper, we propose that the task of definition modeling, which aims to generate the human-readable definition of the word, provides a route to evaluate or understand the high dimensional semantic vectors. We propose a `Vec2Gloss' model, which produces the gloss from the target word's contextualized embeddings. The generated glosses of this study are made possible by the systematic gloss patterns provided by Chinese Wordnet. We devise two dependency indices to measure the semantic and contextual dependency, which are used to analyze the generated texts in gloss and token levels. Our results indicate that the proposed `Vec2Gloss' model opens a new perspective to the lexical-semantic applications of contextualized embeddings