Cross-Lingual Information Retrieval (CLIR) enables a user to query in a language which is different from the target documents language. CLIR incorporates a translation technique based on either a manual dictionary or a probabilistic dictionary which is generated from a parallel corpus. The translation techniques for Hindi language suffer from a translation mis-mapped issue which is due to the morphological richness of Hindi language. In addition, a word may have multiple translations in a dictionary leading to word translation disambiguation issue. This paper addresses two key findings, i.e., Semantic Morphological Variant Selection (SMVS), and Hybrid Word Translation Disambiguation (HWTD), the former resolves translation mis-mapped issue and the later disambiguates the queries more effectively. The proposed techniques are investigated for FIRE ad-hoc datasets, where SMVS and HWTD at word level achieve better evaluation measures in comparison to the baseline Statistical Machine Translation.

跨语言信息检索 (CLIR) 使用户能够以不同于目标文档语言的语言进行查询。CLIR 结合了基于手动词典或从平行语料库生成的概率词典的翻译技术。由于印地语的形态丰富，印地语的翻译技术存在翻译错误映射的问题。此外，一个词在字典中可能有多个翻译，导致词翻译消歧问题。本文解决了两个关键发现，即语义形态变体选择（SMVS）和混合词翻译消歧（HWTD），前者解决了翻译错误映射问题，后者更有效地消除了查询的歧义。