Abstract
For most English words, dictionaries give various senses: e.g., "bank" can stand for a financial institution, shore, set, etc. Automatic selection of the sense intended in a given text has crucial importance in many applications of text processing, such as information retrieval or machine translation: e.g., "(my account in the) bank" is to be translated into Spanish as "(mi cuenta en et) banco" whereas "(on the) bank (of the lake)" as "(en la) orilla (del logo)." To choose the optimal combination of the intended senses of all words, Lesk suggested to consider the global coherence of the text, i.e., which we mean the average relatedness between the chosen senses for all words in the text. Due to high dimensionality of the search space, heuristics are to be used to find a near-optimal configuration. In this paper, we discuss several such heuristics that differ in terms of complexity and quality of the results. In particular, we introduce a dimensionality reduction algorithm that reduces the complexity of computationally expensive approaches such as genetic algorithms.
Original language | English |
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Pages (from-to) | 402-405 |
Number of pages | 4 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 3513 |
DOIs | |
State | Published - 2005 |
Event | 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005: Natural Language Processing and Information Systems - Alicante, Spain Duration: 15 Jun 2005 → 17 Jun 2005 |