Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
نویسندگان
چکیده
In this paper, we propose a method that employs sentences similarities from context word embeddings for supervised word sense disambiguation. In particular, if N example sentences exist in training data, an N-dimensional vector with N similarities between each pair of example sentences is added to a basic feature vector. This new feature vector is used to train a classifier and identification. We evaluated the proposed method using the feature vectors based on Bag-of-Words, SemEval-2 baseline as basic feature vectors and SemEval-2 Japanese task. The experimental results suggest that the method is more effective than the method with only basic vectors.
منابع مشابه
Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation
Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identif...
متن کاملSupervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms
This paper compares two approaches to word sense disambiguation using word embeddings trained on unambiguous synonyms. The first one is an unsupervised method based on computing log probability from sequences of word embedding vectors, taking into account ambiguous word senses and guessing correct sense from context. The second method is supervised. We use a multilayer neural network model to l...
متن کاملSemi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
One of the weaknesses of current supervised word sense disambiguation (WSD) systems is that they only treat a word as a discrete entity. However, a continuous-space representation of words (word embeddings) can provide valuable information and thus improve generalization accuracy. Since word embeddings are typically obtained from unlabeled data using unsupervised methods, this method can be see...
متن کاملContext clustering for Word Sense Disambiguation based on modeling pairwise context similarities
Traditionally, word sense disambiguation (WSD) involves a different context model for each individual word. This paper presents a new approach to WSD using weakly supervised learning. Statistical models are not trained for the contexts of each individual word, but for the similarities between context pairs at category level. The insight is that the correlation regularity between the sense disti...
متن کاملA Supervised Word Sense Disambiguation Method Using Ontology and Context Knowledge
Word Sense Disambiguation is one of the basic tasks in Natural language processing. It is the method of selecting the correct sense of the word in the given context. It is applied whenever a semantic understanding of text is needed. In order to disambiguate a word, two resources are necessary: a context in which the word has been used, and some kind of knowledge related to the word. This paper ...
متن کامل