From Word Alignment to Word Senses, via Multilingual Wordnets
Most of the successful commercial applications in language processing (text and/or speech) dispense with any explicit concern on semantics, with the usual motivations stemming from the computational high costs required for dealing with semantics, in case of large volumes of data. With recent advance...
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| Format: | Article |
| Language: | English |
| Published: |
Vladimir Andrunachievici Institute of Mathematics and Computer Science
2006-05-01
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| Series: | Computer Science Journal of Moldova |
| Online Access: | http://www.math.md/files/csjm/v14-n1/v14-n1-(pp3-33).pdf |
| _version_ | 1798024405551415296 |
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| author | Dan Tufis |
| author_facet | Dan Tufis |
| author_sort | Dan Tufis |
| collection | DOAJ |
| description | Most of the successful commercial applications in language processing (text and/or speech) dispense with any explicit concern on semantics, with the usual motivations stemming from the computational high costs required for dealing with semantics, in case of large volumes of data. With recent advances in corpus linguistics and statistical-based methods in NLP, revealing useful semantic features of linguistic data is becoming cheaper and cheaper and the accuracy of this process is steadily improving. Lately, there seems to be a growing acceptance of the idea that multilingual lexical ontologisms might be the key towards aligning different views on the semantic atomic units to be used in characterizing the general meaning of various and multilingual documents. Depending on the granularity at which semantic distinctions are necessary, the accuracy of the basic semantic processing (such as word sense disambiguation) can be very high with relatively low complexity computing. The paper substantiates this statement by presenting a statistical/based system for word alignment and word sense disambiguation in parallel corpora. We describe a word alignment platform which ensures text pre-processing (tokenization, POS-tagging, lemmatization, chunking, sentence and word alignment) as required by an accurate word sense disambiguation. |
| first_indexed | 2024-04-11T18:01:53Z |
| format | Article |
| id | doaj.art-5fa66b4f736e49d28dc5f9f4c3bdd061 |
| institution | Directory Open Access Journal |
| issn | 1561-4042 |
| language | English |
| last_indexed | 2024-04-11T18:01:53Z |
| publishDate | 2006-05-01 |
| publisher | Vladimir Andrunachievici Institute of Mathematics and Computer Science |
| record_format | Article |
| series | Computer Science Journal of Moldova |
| spelling | doaj.art-5fa66b4f736e49d28dc5f9f4c3bdd0612022-12-22T04:10:26ZengVladimir Andrunachievici Institute of Mathematics and Computer ScienceComputer Science Journal of Moldova1561-40422006-05-01141(40)333From Word Alignment to Word Senses, via Multilingual WordnetsDan Tufis0Institute for Artificial Intelligence, 13, "13 Septembrie", 050711, Bucharest 5, Romania Most of the successful commercial applications in language processing (text and/or speech) dispense with any explicit concern on semantics, with the usual motivations stemming from the computational high costs required for dealing with semantics, in case of large volumes of data. With recent advances in corpus linguistics and statistical-based methods in NLP, revealing useful semantic features of linguistic data is becoming cheaper and cheaper and the accuracy of this process is steadily improving. Lately, there seems to be a growing acceptance of the idea that multilingual lexical ontologisms might be the key towards aligning different views on the semantic atomic units to be used in characterizing the general meaning of various and multilingual documents. Depending on the granularity at which semantic distinctions are necessary, the accuracy of the basic semantic processing (such as word sense disambiguation) can be very high with relatively low complexity computing. The paper substantiates this statement by presenting a statistical/based system for word alignment and word sense disambiguation in parallel corpora. We describe a word alignment platform which ensures text pre-processing (tokenization, POS-tagging, lemmatization, chunking, sentence and word alignment) as required by an accurate word sense disambiguation.http://www.math.md/files/csjm/v14-n1/v14-n1-(pp3-33).pdf |
| spellingShingle | Dan Tufis From Word Alignment to Word Senses, via Multilingual Wordnets Computer Science Journal of Moldova |
| title | From Word Alignment to Word Senses, via Multilingual Wordnets |
| title_full | From Word Alignment to Word Senses, via Multilingual Wordnets |
| title_fullStr | From Word Alignment to Word Senses, via Multilingual Wordnets |
| title_full_unstemmed | From Word Alignment to Word Senses, via Multilingual Wordnets |
| title_short | From Word Alignment to Word Senses, via Multilingual Wordnets |
| title_sort | from word alignment to word senses via multilingual wordnets |
| url | http://www.math.md/files/csjm/v14-n1/v14-n1-(pp3-33).pdf |
| work_keys_str_mv | AT dantufis fromwordalignmenttowordsensesviamultilingualwordnets |