Phrasal Paraphrase Based Question Reformulation for Archived Question Retrieval.
Phrasal Paraphrase Based Question Reformulation for Archived Question Retrieval.
复制标题
基于短语释义的问题重新重新制定了存档问题检索。
DOI：
10.1371/journal.pone.0064601
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Liu T
中科院分区：
文献类型：
--
作者：
Zhang Y;Zhang WN;Lu K;Ji R;Wang F;Liu T
CQA搜索中的词汇空缺是一种重要而普遍的现象，它是由语言的多样性引起的。针对这一问题，本文提出了一种问题重组方案，通过充分挖掘短语级释义的智能性来增强问题检索模型。它在适当的粒度上弥补了现有的释义研究，这些研究要么属于细粒度的词汇级，要么属于粗粒度的句子级。在给定自然语言问题的情况下，我们的方案首先通过联合整合依赖于语料库的知识和问题感知线索来检测涉及的关键短语。接下来，它利用多个在线翻译引擎自动提取每个识别出的关键短语的释义，然后从一大组问题重写中选择最相关的重写，重写是通过对生成的释义进行全排列和组合形成的。在真实世界数据集上的广泛评估表明，我们的模型能够刻画复杂的问题，并且与最先进的方法相比取得了令人满意的性能。
Lexical gap in cQA search, resulted by the variability of languages, has been recognized as an important and widespread phenomenon. To address the problem, this paper presents a question reformulation scheme to enhance the question retrieval model by fully exploring the intelligence of paraphrase in phrase-level. It compensates for the existing paraphrasing research in a suitable granularity, which either falls into fine-grained lexical-level or coarse-grained sentence-level. Given a question in natural language, our scheme first detects the involved key-phrases by jointly integrating the corpus-dependent knowledge and question-aware cues. Next, it automatically extracts the paraphrases for each identified key-phrase utilizing multiple online translation engines, and then selects the most relevant reformulations from a large group of question rewrites, which is formed by full permutation and combination of the generated paraphrases. Extensive evaluations on a real world data set demonstrate that our model is able to characterize the complex questions and achieves promising performance as compared to the state-of-the-art methods.
影响因子：
10.6
作者：
Gao, Yue;Wang, Meng;Wu, Xindong
通讯作者：
Wu, Xindong
影响因子：
8.6
作者：
CALLAN, JP;CROFT, WB;BROGLIO, J
通讯作者：
BROGLIO, J