Cross-lingual information retrieval (CLIR) task at the NTCIR workshop 3

Kishida, Kazuaki; Chen, Kuang-Hua; Lee, Sukhoon; Chen, Hsin-Hsi; Kando, Noriko; Kuriyama, Kazuko; Myaeng, Sung Hyon; Eguchi, Koichi

doi:10.1145/986278.986281

Cited by 10 publications

(6 citation statements)

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“…In discussing cross-lingual searching (‘CLIR’ or ‘cross-lingual information retrieval’), three types of search tasks have been identified (Kishida et al, 2004): SLIR (single language IR); BLIR (bilingual CLIR); and MLIR (multilingual CLIR), as follows: SLIR is where the language of the search topics (usually determined by the linguistic capacity of the human searcher) is identical to that of the documents (i.e. this is not a cross-lingual task).BLIR denotes that a document set in a single language is searched using topics in a different language (for example, using English topics to search Chinese documents).MLIR denotes a search task where the target collection consists of documents in two or more languages (for example, searching a multilingual collection for Chinese topics).…”

Section: Unresolved Issues and Future Workmentioning

confidence: 99%

Searching Legal Information in Multiple Asian Languages

Chung¹,

Mowbray²,

Greenleaf³

2012

Legal Information Management

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In this article Philip Chung, Andrew Mowbray, and Graham Greenleaf, the Co-Directors of the Australasian Legal Information Institute (AustLII), explain the need for an open source search engine which can search simultaneously over legal materials in European languages and also in Asian languages, particularly those that require a ‘double byte’ representation, and the difficulties this task presents. A solution is proposed; the ‘u16a’ modifications to AustLII's open source search engine (Sino) which is used by many legal information institutes. Two implementations of the Sino u16A approach, on the Hong Kong Legal Information Institute (HKLII), for English and Chinese, and on the Asian Legal Information Institute (AsianLII), for multiple Asian languages, are described. The implementations have been successful, though many challenges (discussed briefly) remain before this approach will provide a full multi-lingual search facility.

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Section: Unresolved Issues and Future Workmentioning

confidence: 99%

Searching Legal Information in Multiple Asian Languages

Chung¹,

Mowbray²,

Greenleaf³

2012

Legal Information Management

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“…In TREC corpus, set 2 is “clone Dolly sheep.” It discusses the feasibility of the gene cloning and the perspectives of the authority. For NTCIR corpus, an additional topic, “animal cloning,” is selected from NTCIR 3 (Kishida et al, 2004). The blog is a new rising community for expressing opinions.…”

Section: Evaluation Designmentioning

confidence: 99%

Mining opinions from the Web: Beyond relevance retrieval

Chen

2007

J. Am. Soc. Inf. Sci.

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156

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Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.

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“…Test collections for cross-language retrieval 7), 8) , open-domain question answering 9), 10) , and text summarization 11) have also been constructed. A test collection for spoken document retrieval (SDR) is usually based on a broadcast news corpus.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Test Collection for Spoken Document Retrieval from Lecture Audio Data

Akiba

Aikawa

Itoh

et al. 2009

Journal of Information Processing

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The lecture is one of the most valuable genres of audiovisual data. Though spoken document processing is a promising technology for utilizing the lecture in various ways, it is difficult to evaluate because the evaluation require a subjective judgment and/or the verification of large quantities of evaluation data. In this paper, a test collection for the evaluation of spoken lecture retrieval is reported. The test collection consists of the target spoken documents of about 2,700 lectures (604 hours) taken from the Corpus of Spontaneous Japanese (CSJ), 39 retrieval queries, the relevant passages in the target documents for each query, and the automatic transcription of the target speech data. This paper also reports the retrieval performance targeting the constructed test collection by applying a standard spoken document retrieval (SDR) method, which serves as a baseline for the forthcoming SDR studies using the test collection.

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Cross-lingual information retrieval (CLIR) task at the NTCIR workshop 3

Cited by 10 publications

References 0 publications

Searching Legal Information in Multiple Asian Languages

Searching Legal Information in Multiple Asian Languages

Mining opinions from the Web: Beyond relevance retrieval

Construction of a Test Collection for Spoken Document Retrieval from Lecture Audio Data

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