Mixed language query disambiguation

Fung, Pascale; Liu, Xiaohu; Shun, Cheung Chi

doi:10.3115/1034678.1034732

Cited by 18 publications

(18 citation statements)

References 25 publications

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“…Co-occurrence information has been utilized by several recent studies (e.g., Ballesteros and Croft [1998]; Bian and Chen [1998]; Fung et al [1999]; Jang et al [1999]; Peters and Picchi [1996]; Mandala et al [1999]; Liu et al [2005]) to deal with the selection of the correct translation terms from a bilingual dictionary. A term similarity is determined by the mutual information (or its variants) between terms.…”

Section: Remarks On the Co-occurrence Modelmentioning

confidence: 99%

Statistical query translation models for cross-language information retrieval

Gao

Nie

Zhou

2006

ACM Transactions on Asian Language Information Processing

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Query translation is an important task in cross-language information retrieval (CLIR), which aims to determine the best translation words and weights for a query. This article presents three statistical query translation models that focus on the resolution of query translation ambiguities. All the models assume that the selection of the translation of a query term depends on the translations of other terms in the query. They differ in the way linguistic structures are detected and exploited. The co-occurrence model treats a query as a bag of words and uses all the other terms in the query as the context for translation disambiguation. The other two models exploit linguistic dependencies among terms. The noun phrase (NP) translation model detects NPs in a query, and translates each NP as a unit by assuming that the translation of a term only depends on other terms within the same NP. Similarly, the dependency translation model detects and translates dependency triples, such as verb-object, as units. The evaluations show that linguistic structures always lead to more precise translations. The experiments of CLIR on TREC Chinese collections show that all three models have a positive impact on query translation and lead to significant improvements of CLIR performance over the simple dictionary-based translation method. The best results are obtained by combining the three models.

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Section: Remarks On the Co-occurrence Modelmentioning

confidence: 99%

Statistical query translation models for cross-language information retrieval

Gao

Nie

Zhou

2006

ACM Transactions on Asian Language Information Processing

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“…In the same context, a mixed or a multilingual document can be defined as any document that is written in more than one language (Fung et al, 1999), even if it contains only one or more words. In such a document, there is a primary language and a secondary language, which is mostly English, as well, whose text is often presented/scattered in terms of terms/portions/snippets/phrases/paragraphs.…”

Section: Fig 11mentioning

confidence: 99%

Mixed Language Arabic-English Information Retrieval

Mustafa

Suleman

2015

Computational Linguistics and Intelligent Text Processing

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My special gratitude goes to Dr. Hussein Suleman, my supervisor, who was very helpful and a considerable advisor. He spares no effort maintaining my ideas and views to this research. I owe him a huge thank you for his tireless recommendations and encouragement and for his gentle personal guidance. He was patient and cooperative-without him, I would never have got to the completion of this thesis. He was always helpful, honest and inspiring in criticism. He was much more than just the supervisor-he inspired me with his way of doing research. I want to acknowledge also Prof. Douglas Oard at the University of Maryland, College Park, for his keen guidance and support. I have especially benefitted from his comments and from different discussion sessions with him. He spares no time answering my doubts and inquiries. I am also thankful to him for organizing a colloquium on this research at the Computational Linguistics and Information Processing (CLIP) laboratory, Institute for Advanced Computer Studies (UMIACS) at the University of Maryland, College Park-USA. I really feel indebted to Prof Doug.

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“…We carried out a crosslingual routing experiment to verify the hypothesis of one language one flow. In our experiments, we first translate [Fung et al 1999;Gao et al 2002] an unknown story label into English. The translated document is then routed into English stories with Hidden Markov Story Models trained in English.…”

Section: One Language One Flowmentioning

confidence: 99%

One story, one flow

Fung

Ngai

2006

ACM Trans. Speech Lang. Process.

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This article presents a multidocument, multilingual, theme-based summarization system based on modeling text cohesion (story flow). Conventional extractive summarization systems which pick out salient sentences to include in a summary often disregard any flow or sequence that might exist between these sentences. We argue that such inherent text cohesion exists and is (1) specific to a particular story and (2) specific to a particular language. Documents within the same story, and in the same language, share a common story flow, and this flow differs across stories, and across languages. We propose using Hidden Markov Models (HMMs) as story models. An unsupervised segmental K-means method is used to iteratively cluster multiple documents into different topics (stories) and learn the parameters of parallel Hidden Markov Story Models (HMSM), one for each story. We compare story models within and across stories and within and across languages (English and Chinese). The experimental results support our "one story, one flow" and "one language, one flow" hypotheses. We also propose a Naïve Bayes classifier for document summarization. The performance of our summarizer is superior to conventional methods that do not incorporate text cohesion information. Our HMSM method also provides a simple way to compile a single metasummary for multiple documents from individual summaries via state labeled sentences.

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Mixed language query disambiguation

Cited by 18 publications

References 25 publications

Statistical query translation models for cross-language information retrieval

Statistical query translation models for cross-language information retrieval

Mixed Language Arabic-English Information Retrieval

One story, one flow

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