Cross-language Information Retrieval

Abstract

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.

49 pages, 0 figures

References in corpus (8)

• A Deep Relevance Matching Model for Ad-hoc Retrieval
• End-to-End Neural Ad-hoc Ranking with Kernel Pooling
• Deeper Text Understanding for IR with Contextual Neural Language Modeling
• Offline bilingual word vectors, orthogonal transformations and the inverted softmax
• A Study of Neural Matching Models for Cross-lingual IR
• Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval
• Exploiting Neural Query Translation into Cross Lingual Information Retrieval
• Cross-Lingual Relevance Transfer for Document Retrieval