IRAICLNov 10, 2021

Cross-language Information Retrieval

arXiv:2111.05988v228 citations
Originality Synthesis-oriented
AI Analysis

This addresses the challenge of information access across language barriers for users, but it is incremental as it reviews existing work rather than presenting new findings.

The paper tackles the problem of retrieving documents in a language unknown to the searcher, where standard assumptions about query formulation and document recognition fail, and reviews the state of the art in Cross-Language Information Retrieval (CLIR) along with open research questions.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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