Opinionated document retrieval using subjective triggers
Opinionated document retrieval using subjective triggers
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DOI：
10.1002/asi.21502
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发表时间：
2011-05
期刊：
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通讯作者：
Kazuhiro Seki;K. Uehara
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文献类型：
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作者：
Kazuhiro Seki;K. Uehara
本文提出了一种统计语言模型的新应用，用于针对weblogs （blog）的固执己见的文档检索。特别是，我们探索了触发模型的使用——最初是为合并远距词依赖而开发的——以便对个人意见的特征进行建模，这些特征不能通过标准n-grams适当地建模。我们的主要假设是，形成主观意见有两个要素。一个是意见的主体或意见所涉及的客体，另一个是主观表达；前者被视为触发词，后者被视为触发词。我们自动识别那些主观触发模式，从产品客户评论的语料库中构建语言模型。在文本检索会议博客轨道测试集上的实验结果表明，当用于对初始搜索结果进行重新排序时，我们提出的模型显著改善了自以为是的文档检索。此外，我们报告了一个关于模型对给定查询的动态适应的实验，该实验发现该模型对政治和组织分类下的大多数困难查询都是有效的。我们还证明，在不修改模型本身的情况下，该模型可以有效地应用于极化意见检索。©2011 Wiley期刊公司
This article proposes a novel application of a statistical language model to opinionated document retrieval targeting weblogs (blogs). In particular, we explore the use of the trigger model—originally developed for incorporating distant word dependencies—in order to model the characteristics of personal opinions that cannot be properly modeled by standard n-grams. Our primary assumption is that there are two constituents to form a subjective opinion. One is the subject of the opinion or the object that the opinion is about, and the other is a subjective expression; the former is regarded as a triggering word and the latter as a triggered word. We automatically identify those subjective trigger patterns to build a language model from a corpus of product customer reviews. Experimental results on the Text Retrieval Conference Blog track test collections show that, when used for reranking initial search results, our proposed model significantly improves opinionated document retrieval. In addition, we report on an experiment on dynamic adaptation of the model to a given query, which is found effective for most of the difficult queries categorized under politics and organizations. We also demonstrate that, without any modification to the proposed model itself, it can be effectively applied to polarized opinion retrieval. © 2011 Wiley Periodicals, Inc.