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In Natural Language Processing, Word Sense Disambiguation is defined as the task to assign a suitable sense of words in a certain context. Word Sense Disambiguation takes an important role and considered as the core research problem in computational linguistics. In this research, we conduct an experiment with Adapted Lesk Algorithm compared to original Lesk Algorithm to improve the performance of...
Word Sense Disambiguation (WSD) is an important and challenging task in the area of Natural Language Processing (NLP) where the task is to find the correct sense of an ambiguous word given its context. There have been very few attempts on WSD in Bengali or in Indian languages. The k-Nearest-Neighbor (k-NN) algorithm is a very well known and popular method for text classification. The k-NN algorithm...
Word Sense Disambiguation (WSD) is the process of selecting the correct sense for a word in a context. WSD has become a growing research area in the field of Natural Language Processing (NLP). Over the decades, lot of studies had been carried out to suggest different approaches for WSD process. A break-through in this field would have a significant impact on many relevant web-based applications, such...
Context word of the ambiguous word is an important basis for word sense disambiguation (WSD). Knowledge-based WSD computes overall semantic relatedness between context words and each sense of ambiguous word. Each of context words is assigned different WSD weight based on its distance with ambiguous word. Power function and exponential function can be used to compute WSD weight. In the paper, the performances...
For the existing disadvantage of Word Sense Disambiguation(WSD) research methods, we have analyzed the computability and computational complexity of knowledge Dictionaries with different structure, and chosen ??The Grammatical knowledge-base of Contemporary Chinese?? and ??the Semantic Knowledge-base of Contemporary Chinese?? which written by Institute of Computational Linguistics of Peking University,...
A lexical ontology is useful as the basic knowledge base in artificial intelligence and computational linguistics application. However, it is insufficient to recognize only existing instances for each concept. Adding new instances into the lexical ontology will expand knowledge in the system. In this paper, we propose an efficient unsupervised instance population system that classifies new instances...
The on-going project aiming at the creation of the National Corpus of Polish assumes several levels of linguistic annotation. We present the technical environment and methodological background developed for the three upper annotation levels: the level of syntactic words and groups, and the level of named entities. We show how knowledge-based platforms Spejd and Sprout are used for the automatic pre-annotation...
A methodology for construction of models using genuine expressions of natural language, namely the so called evaluative linguistic expressions is presented. A sophisticated formal theory of their meaning provides us a tool with a great application potential. These expressions occur also in conditional clauses of natural language which are gathered into linguistic descriptions, for example, of complex...
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