Translation disambiguation for cross-language information retrieval using context-based translation probability

Kishida, Kazuaki; Ishita, Emi

doi:10.1177/0165551509103599

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…Instead of using the higher-order models, some previous studies [8,11] have used a simpler approach in which a sentence is converted to a sequence of term pairs. For example, a sentence "A helicopter gets its power from rotors or blades" is converted to (helicopter-gets) (gets-power) (power-rotors) (rotors-blades) to estimate the translation probabilities between adjacent term pairs.…”

Section: A Key Concept-based Translation Modelmentioning

confidence: 99%

Using Key Concepts in a Translation Model for Retrieval

Park

Croft

2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Many queries, especially those in the form of longer questions, contain a subset of terms representing key concepts that describe the most important part of the user's information need. Detecting the key concepts in a query can be used as the basis for more effective weighting of query terms, but in this paper, we focus on a method of using the key concepts in a translation model for query expansion and retrieval. Translation models have been used previously in community-based question answering (CQA) systems in order to bridge the semantic gap between questions and the corresponding answer documents. Our method uses the key concepts of a question as the translation context and selectively applies the translation model to the secondary (non-key) parts of the question. We evaluate the proposed method using a CQA collection and show that selectively translating key and secondary concepts can significantly improve the retrieval performance compared to a baseline that applies the translation model without considering key concepts.

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Section: A Key Concept-based Translation Modelmentioning

confidence: 99%

Using Key Concepts in a Translation Model for Retrieval

Park

Croft

2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Since ambiguity is a context dependent problem and Nic et al' approach is a dictionary characteristic-based solution, the degree of ambiguity is not an effective solution. Sense disambiguation upon context terms proposed by Kishida [12]. Despite Kishida's attempts to disambiguate between multiple translations, he uses a sentence aligned corpora.…”

Section: Previous Workmentioning

confidence: 99%

A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages

Dadashkarimi¹,

Shakery²,

Faili³

2014

Preprint

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Translation ambiguity, out of vocabulary words and missing some translations in bilingual dictionaries make dictionary-based Crosslanguage Information Retrieval (CLIR) a challenging task. Moreover, in agglutinative languages which do not have reliable stemmers, missing various lexical formations in bilingual dictionaries degrades CLIR performance. This paper aims to introduce a probabilistic translation model to solve the ambiguity problem, and also to provide most likely formations of a dictionary candidate. We propose Minimum Edit Support Candidates (MESC) method that exploits a monolingual corpus and a bilingual dictionary to translate users' native language queries to documents' language. Our experiments show that the proposed method outperforms state-of-the-art dictionary-based English-Persian CLIR.

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“…An optimization was proposed by Zhong and Ng [11], who found Chinese synonyms in the English–Chinese parallel corpus and bilingual lexicon. Similarly, Kishida and Ishita [12] used a sentence-aligned bilingual corpus, which they used to build a context-based cross-language transition probability model to deal with WSD tasks.…”

Section: Related Workmentioning

confidence: 99%

Word sense disambiguation based on positional weighted context

Huang

Zheng

Kang

et al. 2012

Journal of Information Science

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Word sense disambiguation (WSD) is a key factor in solving natural language processing problems. The purpose of WSD is to make computers automatically determine the specific meaning of a word in a specific context. In this regard, state-of-art studies have focussed on the co-occurrences of words to measure context similarity. However, a problem with these approaches is that they consider all the words within a certain range to have equal influence on the ambiguous word. In this paper, we propose a position-based algorithm for measuring context similarity. By assigning positional weights to context words, we compared the context similarity between a new instance and pre-labelled instances to determine the appropriate sense of the ambiguous word. Experiments on the Senseval-2 English lexical sample task showed that our algorithm can achieve good precision and recall. Even in a minimally supervised state, it performs well with few training instances.

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Translation disambiguation for cross-language information retrieval using context-based translation probability

Cited by 7 publications

References 24 publications

Using Key Concepts in a Translation Model for Retrieval

Using Key Concepts in a Translation Model for Retrieval

A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages

Word sense disambiguation based on positional weighted context

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