175 research outputs found
Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information
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 all-words English WSD evaluation benchmarks. On multilingual all-words WSD, we report state-of-the-art results by training on nothing but English
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
Investigations into the value of labeled and unlabeled data in biomedical entity recognition and word sense disambiguation
Human annotations, especially in highly technical domains, are expensive and time consuming togather, and can also be erroneous. As a result, we never have sufficiently accurate data to train andevaluate supervised methods. In this thesis, we address this problem by taking a semi-supervised approach to biomedical namedentity recognition (NER), and by proposing an inventory-independent evaluation framework for supervised and unsupervised word sense disambiguation. Our contributions are as follows: We introduce a novel graph-based semi-supervised approach to named entity recognition(NER) and exploit pre-trained contextualized word embeddings in several biomedical NER tasks. We propose a new evaluation framework for word sense disambiguation that permits a fair comparison between supervised methods trained on different sense inventories as well as unsupervised methods without a fixed sense inventory
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