Disambiguation strategies for cross-language information retrieval

This paper gives an overview of tools and methods for Cross-Language Information Retrieval (CLIR) that are developed within the Twenty-One project. The tools and methods are evaluated with the TREC CLIR task document collection using Dutch queries on the English document base. The main issue addressed here is an evaluation of two approaches to disambiguation. The underlying question is whether a lot of effort should be put in finding the correct translation for each query term before searching, or whether searching with more than one possible translation leads to better results? The experimental study suggests that the quality of search methods is more important than the quality of disambiguation methods. Good retrieval methods are able to disambiguate translated queries implicitly during searching

Cross-language Information Retrieval

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for CLIR and outlines some open research questions.Comment: 49 pages, 0 figure

User-centred interface design for cross-language information retrieval

This paper reports on the user-centered design methodology and techniques used for the elicitation of user requirements and how these requirements informed the first phase of the user interface design for a Cross-Language Information Retrieval System. We describe a set of factors involved in analysis of the data collected and, finally discuss the implications for user interface design based on the findings

Implementasi Model Ruang Vektor Sebagai Penerjemah Query Pada Cross-Language Information Retrieval Sistem Implementation of Vector Space Model as Query Translation For Cross-Language Information Retrieval System

ABSTRAKSI: Pada Tugas Akhir ini dibuat sistem yang dapat mengembalikan informasi dalam lintas bahasa. Sistem ini diharapkan dapat menerjemahkan query dalam bahasa lain, selain itu diharapkan dapat melakukan pengindeksan dan pencarian dokumen dalam bahasa yang berbeda.Sistem ini mengimplementasikan model ruang vektor yaitu salah satu model pada information retrieval yang menentukan kemiripan (similarity) antara dokumen dengan query dengan cara merepresentasikan dokumen dan query dalam bentuk vektor. Sistem ini diharapkan dapat mengakomodasikan kebutuhan user untuk mendapatkan dokumen yang relevan dari bahasa yang berbeda dengan bahasa query. Koleksi dokumen yang digunakan yaitu dokumen berbahasa Indonesia dan dokumen berbahasa Inggris.Pada penerjemahan query, penerjemahan dengan menggunakan teknik nilai kemiripan statistik lebih baik dibandingkan pengambilan terjemahan pertama dan pengambilan semua terjemahan. Sedangkan untuk sistem Monolingual Information Retrieval mempunyai performansi lebih baik dibandingkan dengan Cross-Language Information Retrieval.Kata Kunci : nilai kemiripan statistik, cross language information retrievalABSTRACT: The final project creates a system that can retrieval information in cross language. This system wished can translating query to the other language, and than wished can indexing and to find out the document in the different language.This system implemented vector space model namely a type of model in information retrieval whose decide similarity betwen document with query that representating document and query into a vector. Cross-language information rertrieval system wished can accomodate the user need to get a relevant document from different language with the query language. The document collection that used in this work are indonesian language document and english language document.In query translation, translation used a similarity value statistic technique better than first translation and all translation. Performance of Monolingual Information Retrieval system better than Cross-Language Information Retrieval.Keyword: similarity value statistic, cross-language information retrieva

Applying Machine Translation to Two-Stage Cross-Language Information Retrieval

Cross-language information retrieval (CLIR), where queries and documents are in different languages, needs a translation of queries and/or documents, so as to standardize both of them into a common representation. For this purpose, the use of machine translation is an effective approach. However, computational cost is prohibitive in translating large-scale document collections. To resolve this problem, we propose a two-stage CLIR method. First, we translate a given query into the document language, and retrieve a limited number of foreign documents. Second, we machine translate only those documents into the user language, and re-rank them based on the translation result. We also show the effectiveness of our method by way of experiments using Japanese queries and English technical documents.Comment: 13 pages, 1 Postscript figur

Explicit versus Latent Concept Models for Cross-Language Information Retrieval

Cimiano P, Schultz A, Sizov S, Sorg P, Staab S. Explicit versus Latent Concept Models for Cross-Language Information Retrieval. In: Boutilier C, ed. IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press; 2009: 1513-1518

Structured Translation for Cross-Language Information Retrieval

The paper introduces a query translation model that re ects the structure of the cross-language information retrieval task. The model is based on a structured bilingual dictionary in which the translations of each term are clustered into groups with distinct meanings. Query translation is modeled as a two-stage process, with the system rst determining the intended meaning of a query term and then selecting translations appropriate to that meaning that might appear in the document collection. An implementation of structured translation based on automatic dictionary clustering is described and evaluated by using Chinese queries to retrieve English documents. Structured translation achieved an average precision that was statistically indistinguishable from Pirkola's technique for very short queries, but Pirkola's technique outperformed structured translation on long queries. The paper concludes with some observations on future work to improve retrieval e ectiveness and on other potential uses of structured translation in interactive cross-language retrieval applications. 1