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Semantic information is important for precise word sense disambiguation system and the kind of semantic analysis used in sophisticated natural language processing such as machine translation, question answering, etc. There are at least two kinds of semantic information: lexical semantics for words and phrases and structural semantics for phrases an...
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The present paper accomplishes an analysis of the multiple classifications that exist for the word sense disambiguation algorithms (WSD); also it displays the lexical fundamental resource employed for the implementation of these. It shows the functioning of the algorithm proposed by Lesk in 1986, applied over an electronic dictionary, as well as hi...
In order to use semantics more effectively in natural language processing, a word sense disambiguation method for Chinese based on semantics calculation was proposed. The word sense disambiguation for a Chinese clause could be achieved by solving the semantic model of the natural language; each step of the word sense disambiguation process was disc...
We present a methodology to wikify learning objects. Our proposal is focused on two processes: word sense disambiguation and relevant phrase selection. The disambiguation process involves the use of the learning object's metadata as either additional or alternative context. This increases the probability of success when a learning object has a low...
Word Sense Disambiguation (WSD) is the task to determine the sense of an ambiguous word in a given context. Previous approaches for WSD have focused on supervised and knowledge-based methods, but inter-sense interactions patterns or regularities for disambiguation remain to be found. We argue the following cause as one of the major difficulties beh...
Within the space of question answering (QA) systems, the most critical module to improve overall performance is question analysis processing. Extracting the lexical semantic of a Natural Language (NL) question presents challenges at syntactic and semantic levels for most QA systems. This is due to the difference between the words posed by a user an...
Citations
... In the first phase, we built a treebank based on the Japanese semantic database Lexeed (Kasahara et al. 2004) and constructed a thesaurus from it (Bond et al. 2004b). In the second phase, we have tagged the definition sentences with senses (Tanaka et al. 2006) and are using the lexical semantic information and the thesaurus to build a model that combines syntactic and semantic information. In phase three, we will look at ways of combining the lexical and structural semantics and extending our lexicon and ontology to less familiar words. ...
... In this section we discuss the (lexical) semantic annotation for the Hinoki project (Tanaka et al. 2006). Each word was annotated by five annotators (15 annotators, divided into 3 groups). ...
In this paper we describe the current state of a new Japanese lexical resource: the Hinoki treebank. The treebank is built
from dictionary definitions, examples and news text, and uses an HPSG based Japanese grammar to encode both syntactic and
semantic information. It is combined with an ontology based on the definition sentences to give a detailed sense level description
of the most familiar 28,000 words of Japanese.
