词性标记如何影响文本检索和过滤性能
How Part-of-Speech Tags Affect Text Retrieval and Filtering Performance
摘要：
应用于信息检索（IR）和过滤问题的自然语言处理（NLP）可以将词性标签分配给术语，并且更一般地，修改查询和文档。分析模型可以预测文本过滤系统的性能，因为它包含了NLP建议的变化，使我们能够精确地说明NLP操作对IR的平均影响。在这里，我们提供了一个检索和标记模型，使我们能够计算由于句法分析引起的性能变化，并使我们能够了解哪些因素影响性能以及如何影响性能。除了预测的性能与标签，检索性能的上限和下限推导出，给出最好的和最坏的影响，包括部分的语音标签。经验的理由，选择标签集被认为是。
英文摘要：
Natural language processing (NLP) applied to information retrieval (IR) and filtering problems may assign part-of-speech tags to terms and, more generally, modify queries and documents. Analytic models can predict the performance of a text filtering system as it incorporates changes suggested by NLP, allowing us to make precise statements about the average effect of NLP operations on IR. Here we provide a model of retrieval and tagging that allows us to both compute the performance change due to syntactic parsing and to allow us to understand what factors affect performance and how. In addition to a prediction of performance with tags, upper and lower bounds for retrieval performance are derived, giving the best and worst effects of including part-of-speech tags. Empirical grounds for selecting sets of tags are considered.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
