Resource selection for domain-specific cross-lingual IR

Rogati, Monica; Yang, Yiming

doi:10.1145/1008992.1009021

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…For medical terminology, as well as for other sublanguages, non-specialized multilingual lexicons (based on WORD-NET) or commercial machine translation systems offer limited support only [6,16]. We optimize the lexical coverage by limiting the dictionary to semantically relevant subwords of the medical domain.…”

Section: Discussionmentioning

confidence: 99%

Bootstrapping dictionaries for cross-language information retrieval

Markó

Schulz

Medelyan

et al. 2005

Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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The bottleneck for dictionary-based cross-language information retrieval is the lack of comprehensive dictionaries, in particular for many different languages. We here introduce a methodology by which multilingual dictionaries (for Spanish and Swedish) emerge automatically from simple seed lexicons. These seed lexicons are automatically generated, by cognate mapping, from (previously manually constructed) Portuguese and German as well as English sources. Lexical and semantic hypotheses are then validated and new ones iteratively generated by making use of co-occurrence patterns of hypothesized translation synonyms in parallel corpora. We evaluate these newly derived dictionaries on a large medical document collection within a cross-language retrieval setting.

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Section: Discussionmentioning

confidence: 99%

Bootstrapping dictionaries for cross-language information retrieval

Markó

Schulz

Medelyan

et al. 2005

Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…tasks, such as feature selection [Yang and Lui 1999;Dumais and Chen 2000] and bilingual term correspondence [Utsuro et al 2003;Rogati and Yang 2004]. Let us take a look at the Reuters '96 hierarchy.…”

Section: Estimating Category Correspondencesmentioning

confidence: 99%

Integrating Cross-Language Hierarchies and Its Application to Retrieving Relevant Documents

Fukumoto

Suzuki

2008

ACM Transactions on Asian Language Information Processing

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Internet directories such as Yahoo! are an approach to improve the efficacy and efficiency of Information Retrieval (IR) on the Web, as pages (documents) are organized into hierarchical categories, and similar pages are grouped together. Most of the search engines on the Web service find documents that are assigned to a single classification hierarchy. Categories in the hierarchy are carefully defined by human experts and documents are well organized. However, a single hierarchy in one language is often insufficient to find all relevant material, as each hierarchy tends to have some bias in both defining hierarchical structure and classifying documents. Moreover, documents written in a language other than the user's native language often include large amounts of information related to the user's request. In this article, we propose a method of integrating cross-language (CL) category hierarchies, that is, Reuters '96 hierarchy and UDC code hierarchy of Japanese by estimating category similarities. The method does not simply merge two different hierarchies into one large hierarchy but instead extracts sets of similar categories, where each element of the sets is relevant with each other. It consists of three steps. First, we classify documents from one hierarchy into categories with another hierarchy using a cross-language text classification (CLTC) technique, and extract category pairs of two hierarchies. Next, we apply χ 2 statistics to these pairs to obtain similar category pairs, and finally we apply the generating function of the Apriori algorithm (Apriori-Gen) to the category pairs, and find sets of similar categories. Moreover, we examined whether integrating hierarchies helps to support retrieval of documents with similar contents. The retrieval results showed a 42.7% improvement over the baseline nonhierarchy model, and a 21.6% improvement over a single hierarchy.

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“…In general, a parallel corpus will be most useful if it is used to implement cross-language retrieval of documents that are in a similar domain to the parallel corpus. Recent work shows that significant effectiveness can be obtained ifthe correct domain is selected [Rogati and Yang, 2004]. We discuss parallel corpus CUR techniques in Section 4.3.2.1.…”

Section: Resourcesmentioning

confidence: 99%