Results 1 to 10 of about 292 (107)
Word Sense Disambiguation Using Clustered Sense Labels
Sequence labeling models for word sense disambiguation have proven highly effective when the sense vocabulary is compressed based on the thesaurus hierarchy.
Jeong Yeon Park +2 more
exaly +3 more sources
Word sense disambiguation with pictures
AbstractWe introduce using images for word sense disambiguation, either alone, or in conjunction with traditional text based methods. The approach is based on a recently developed method for automatically annotating images by using a statistical model for the joint probability for image regions and words. The model itself is learned from a data base of
Kobus Barnard
exaly +2 more sources
Word sense disambiguation of acronyms in clinical narratives
Clinical narratives commonly use acronyms without explicitly defining their long forms. This makes it difficult to automatically interpret their sense as acronyms tend to be highly ambiguous.
Pádraig Corcoran +2 more
exaly +3 more sources
Efficient estimation of Hindi WSD with distributed word representation in vector space
Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language ...
Archana Kumari, D.K. Lobiyal
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Role of Genetic Algorithm in Optimization of Hindi Word Sense Disambiguation
The Word Sense Disambiguation system is widely used in many fields, including business, research, education, and government organizations. The availability of natural language data on the internet has grown in tandem with the rapid advancement of ...
Surbhi Bhatia +2 more
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Word domain disambiguation via word sense disambiguation [PDF]
Word subject domains have been widely used to improve the performance of word sense disambiguation algorithms. However, comparatively little effort has been devoted so far to the disambiguation of word subject domains. The few existing approaches have focused on the development of algorithms specific to word domain disambiguation.
Sanfilippo, Antonio P. +2 more
openaire +2 more sources
A Method of Word Sense Disambiguation with Recurrent Netural Networks
Word sense disambiguation is an important research problem in natural language processing field. For the phenomenon that a Chinese word has many senses, recurrent neural network (RNN) is used to determine true meaning of ambiguous word with its context ...
ZHANG Chunxiang +2 more
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Chinese Word Sense Disambiguation Based on Word translation and Part of speech
For vocabulary ambiguity problem in Chinese, CNN (Convolution Neural Network) is adopted to determine true meaning of ambiguous vocabulary where word, part of speech and translation around its left and right adjacent words are used.
ZHANG Chunxiang +2 more
doaj +1 more source
Subjectivity word sense disambiguation [PDF]
This paper investigates a new task, subjectivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses. We provide empirical evidence that SWSD is more feasible than full word sense disambiguation, and that it can be ...
Akkaya, Cem +2 more
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Word sense disambiguation in queries [PDF]
This paper presents a new approach to determine the senses of words in queries by using WordNet. In our approach, noun phrases in a query are determined first. For each word in the query, information associated with it, including its synonyms, hyponyms, hypernyms, definitions of its synonyms and hyponyms, and its domains, can be used for word sense ...
Shuang Liu, Clement T. Yu, Weiyi Meng
openaire +1 more source

