Svetlin Nakov, Preslav Nakov, Elena Paskaleva
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Unsupervised Extraction of False Friends from Parallel Bi-Texts Using the Web as a Corpus (RANLP 2009)
Series:
Unknown
Year:
Unknown
Raiting:
2.5/5
Scientific article: False friends are pairs of words in two languages that are perceived as similar, but have different meanings, e.g., Gift in German means poison in English. In this paper, we present several unsupervised algorithms for acquiring such pairs from a sentence-aligned bi-text. First, we try different ways of exploiting simple statistics about monolingual word occurrences and cross-lingual word co-occurrences in the bi-text. Second, using
methods from statistical machine translation, we induce word alignments in an unsupervised way, from which we estimate lexical translation probabilities,
which we use to measure cross-lingual semantic similarity. Third, we experiment with a semantic similarity measure that uses the Web as a corpus to extract local contexts from text snippets returned by a search engine, and a bilingual
glossary of known word translation pairs, used as bridges. Finally, all measures are combined and applied to the task of identifying likely false friends. The evaluation for Russian and Bulgarian shows a significant improvement over previously-proposed algorithms.
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Cognate or False Friend? Ask the Web! Distinguish between Cognates and False Friends Using Google-based Semantic Similarity Measure (RANLP2007 Workshop)
Series:
Unknown
Year:
Unknown
Raiting:
3.5/5
Scientific article: We propose a novel unsupervised semantic method for distinguishing cognates from false friends. The basic intuition is that if two words are cognates, then most of the words in their respective local contexts should be translations of each other. The idea is formalised using the Web as a corpus, a glossary of known word translations used as cross-linguistic âbridgesâ, and the vector space model. Unlike traditional orthographic similarity measures, our method can easily handle words with identical spelling. The evaluation on 200 Bulgarian-Russian word pairs
shows this is a very promising approach.
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Book list
Unsupervised Extraction of False Friends from Parallel Bi-Texts Using the Web as a Corpus (RANLP 2009)
Series:
Unknown
Year:
Unknown
Raiting:
2.5/5
Scientific article: False friends are pairs of words in two languages that are perceived as similar, but have different meanings, e.g., Gift in German means poison in English. In this paper, we present several unsupervised algorithms for acquiring such pairs from a sentence-aligned bi-text. First, we try different ways of exploiting simple statistics about monolingual word occurrences and cross-lingual word co-occurrences in the bi-text. Second, using
methods from statistical machine translation, we induce word alignments in an unsupervised way, from which we estimate lexical translation probabilities,
which we use to measure cross-lingual semantic similarity. Third, we experiment with a semantic similarity measure that uses the Web as a corpus to extract local contexts from text snippets returned by a search engine, and a bilingual
glossary of known word translation pairs, used as bridges. Finally, all measures are combined and applied to the task of identifying likely false friends. The evaluation for Russian and Bulgarian shows a significant improvement over previously-proposed algorithms.
Show more
add to favoritesadd In favorites
Cognate or False Friend? Ask the Web! Distinguish between Cognates and False Friends Using Google-based Semantic Similarity Measure (RANLP2007 Workshop)
Series:
Unknown
Year:
Unknown
Raiting:
3.5/5
Scientific article: We propose a novel unsupervised semantic method for distinguishing cognates from false friends. The basic intuition is that if two words are cognates, then most of the words in their respective local contexts should be translations of each other. The idea is formalised using the Web as a corpus, a glossary of known word translations used as cross-linguistic âbridgesâ, and the vector space model. Unlike traditional orthographic similarity measures, our method can easily handle words with identical spelling. The evaluation on 200 Bulgarian-Russian word pairs
shows this is a very promising approach.
Show more
add to favoritesadd In favorites
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write a commentif you like Svetlin Nakov, Preslav Nakov, Elena Paskaleva try:
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write a commentif you like Svetlin Nakov, Preslav Nakov, Elena Paskaleva try:
readers also enjoyed
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