Focused named entity recognition using machine learning

In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. We propose a statistical model for focused named entity recognition by converting it into a classification problem. We then study the impact of various linguistic features and compare a number of classification algorithms. From experiments on an annotated Chinese news corpus, we demonstrate that the proposed method can achieve near human-level accuracy. Categories and Subject Descriptors I.2.7 [Artificial Intelligence]: Natural language Processing—Text Analysis; H.3.1 [Information Storage And Retrieval]: Content Analysis and Indexing—Linguistic processing General Terms Algorithms, Experimentation Keywords naive Bayes, decision...

• Type: Conference
• Year: 2004
• Where: SIGIR
• Authors: Li Zhang, Yue Pan, Tong Zhang
• Keywords: Named Entity Recognition, Natural Language, SIGIR 2004, Topical Named Entities