Using Encyclopedic Knowledge for Named entity Disambiguation

Using Encyclopedic Knowledge for Named Entity Disambiguation Razvan Bunescu � Department of Computer Sciences University of Texas at Austin Austin, TX 78712-0233 razvan@cs.utexas.edu Marius Pas¸ca Google Inc. 1600 Amphitheatre Parkway Mountain View, CA 94043 mars@google.com Abstract We present a new method for detecting and disambiguating named entities in open do- main text. A disambiguation SVM kernel is trained to exploit the high coverage and rich structure of the knowledge encoded in an online encyclopedia. The resulting model significantly outperforms a less in- formed baseline. 1 Introduction 1.1 Motivation The de-facto web search paradigm defines the re- sult to a user’s query as roughly a set of links to the best-matching documents selected out of billions of items available. Whenever the queries search for pinpointed, factual information, the burden of filling the gap between the output granularity (whole documents) and the targeted information (a set of sentences or relevant phrases) stays with the users, by browsing the returned documents in or- der to find the actually relevant bits of information. A frequent case are queries about named entities, which constitute a significant fraction of popu- lar web queries according to search engine logs. When submitting queries such as John Williams or Python, search engine users could also be pre- sented with a compilation of facts and specific at- tributes about those named entities, rather than a set of best-matching web pages. One of the chal- lenges in creating such an alternative search result page is the inherent ambiguity of the queries, as several instances of the same class (e.g., different people) or different classes (e.g., a type of snake, a programming language, or a movie) may share the same name in the query. As an example, the �Work done during a summer internship at Google. contexts below are part of web documents refer- ring to different people who share the same name John Williams: 1. “John Williams and the Boston Pops con- ducted a summer Star Wars concert at Tan- glewood.” 2. “John Williams lost a Taipei death match against his brother, Axl Rotten.” 3. “John Williams won a Victoria Cross for his actions at the battle of Rorke‘s Drift.” The effectiveness of the search could be greatly improved if the search results were grouped together according to the corresponding sense, rather than presented as a flat, sense-mixed list of items (whether links to full-length documents, or extracted facts). As an added benefit, users would have easier access to a wider variety of re- sults, whenever the top 10 or so results returned by the largest search engines happen to refer to only one particular (arguably the most popular) sense of the query (e.g., the programming language in the case of Python), thus submerging or “hiding” documents that refer to other senses of the query. In various natural language applications, signif- icant performance gains are achieved as a func- tion of data size rather than algorithm complex- ity, as illustrated by the increasingly popular use of the web as a (very …

Content truncated to 500 words