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.
Using a technique that introduces additional sense ambiguity into a collection, this paper presents research that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an IR system.
T1 - Word sense induction disambiguation using hierarchical random graphs. AU - Klapaftis, Ioannis P. AU - Manandhar, Suresh. PY - 2010. Y1 - 2010. N2 - Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extraction and others. Most.
Using a technique that introduces additional sense ambiguity into a collection, this paper presents research that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an B2 system when it is retrieving from very short queries. In addition we argue that if.
Research Word Sense Disambiguation. Performance on head and tail of WSD (code. we describe a set of experiments to analyze properties such as the volume, provenance, and balancing of training data in the framework of a state-of-the-art WSD system when evaluated on the SemEval-2013 English all-words dataset. The role of unannotated data (replication, demo) This paper presents a reproduction.
Word Sense Disambiguation Using Selectional Restriction Prity Bala Apaji Institute, Banasthali Vidhyapith Newai,Rajesthan,India Abstract- Word sense disambiguation (WSD) is still an open research area in natural language processing and computational linguistics. It is from both theoretical and practical point of view. Here, the problem is to find the sense for word in given a context, It is a.
Word Sense Disambiguation (WSD) consists of identifying the correct sense of an ambiguous word occurring in a given context. Most of Arabic WSD systems are based generally on the information extracted from the local context of the word to be disambiguated. This information is not usually sufficient for a best disambiguation. To overcome this limit, we propose an approach that takes into.
This paper introduces a form of Hierarchical Learning that permits highly relevant association rules to be extracted from data items ambiguously related to a hierarchy. Addressing the problem of word sense disambiguation in natural language processing, this paper shows how references between words and hypernymy hierarchies may be used to generate highly relevant general rules representing.