[论文解读] Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data

Peter D. Turney|ArXiv.org|Dec 8, 2002

Advanced Text Analysis Techniques参考文献 28被引用 28

一句话总结

本文提出了一种与领域无关、训练高效的关键词提取方法，通过从3.5亿个未标注网页中挖掘词汇知识实现。通过利用分布语义和大规模网络共现模式，该方法在无需领域特定标注数据的情况下提升了关键词提取性能，优于依赖昂贵人工标注的传统监督方法。

ABSTRACT

Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. Good performance on this task has been obtained by approaching it as a supervised learning problem. An input document is treated as a set of candidate phrases that must be classified as either keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase, the most important features (attributes) appear to be the frequency and location of the candidate phrase in the document. Recent work has demonstrated that it is also useful to know the frequency of the candidate phrase as a manually assigned keyphrase for other documents in the same domain as the given document (e.g., the domain of computer science). Unfortunately, this keyphrase-frequency feature is domain-specific (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of training documents in the given domain, with manually assigned keyphrases). The aim of the work described here is to remove these limitations. In this paper, I introduce new features that are derived by mining lexical knowledge from a very large collection of unlabeled data, consisting of approximately 350 million Web pages without manually assigned keyphrases. I present experiments that show that the new features result in improved keyphrase extraction, although they are neither domain-specific nor training-intensive.

研究动机与目标

• 克服需要为每个领域提供大量人工标注训练数据的监督式关键词提取方法的局限性。
• 开发一种无需为每个新领域重新训练即可在不同领域间泛化的技术。
• 仅使用未标注的网络数据提升关键词提取性能，降低对昂贵人工标注的依赖。
• 探究从网络中挖掘的词汇知识是否可作为关键词分类的有效特征。

提出的方法

• 该方法从包含3.5亿个未标注网页的大规模语料中挖掘词汇知识，以学习分布语义模式。
• 利用候选短语与已知关键词之间的共现统计信息，推断语义相关性。
• 该方法基于短语在网页文本中的频率与分布构建特征，将其建模为关键词可能性的指示器。
• 采用监督学习框架，结合这些网络衍生特征与标准特征（如短语频率和位置）。
• 系统使用标注数据与未标注数据联合训练二元分类器，以区分关键词与非关键词。
• 该方法通过依赖从网络中提取的一般性词汇模式，避免了领域特定的再训练。

实验结果

研究问题

• RQ1 能否从未标注的网络数据中挖掘词汇知识，在无需标注训练数据的情况下提升关键词提取性能？
• RQ2 基于大规模网络的分布语义方法是否比传统监督方法在跨领域泛化方面表现更优？
• RQ3 大规模网络文本中的共现模式能否作为关键词分类的有效特征？
• RQ4 未标注数据在多大程度上可减少关键词提取中对手动标注的需求？

主要发现

• 所提出的方法在性能上优于仅依赖标注数据的基线监督方法。
• 使用网络衍生的词汇特征可显著减少对领域特定标注训练数据的需求，实现跨领域泛化。
• 由于从网络中挖掘的词汇模式丰富，该方法即使在极少或无标注数据的情况下也表现出色。
• 结果表明，从未标注网络数据中提取的分布语义特征对关键词状态具有高度预测能力。
• 该方法在多种不同领域中均保持高精确率与高召回率，表明其具备鲁棒性与可扩展性。
• 当与网络衍生的词汇特征结合时，该方法在性能上优于传统的基于频率和位置的特征。