Machine Learning for Information Extraction in Informal Domains
Abstract
Information extraction, the problem of generating structured summaries of human-oriented text documents, has been studied for over a decade now, but the primary emphasis has been on document collections characterized by well-formed prose (e.g., newswire articles). Solutions have often involved the hand-tuning of general natural language processing systems to a particular domain. However, such solutions may be difficult to apply to "informal" do- mains, domains based on genres characterized by syntactically unparsable text and frequent out-of-lexicon terms. With the growth of the Internet, such genres, which include email messages, newsgroup posts, and Web pages, are particularly abundant, and there is no lack of potential information extraction applications. Examples include a program to extract names from personal home pages, or a system that monitors newsgroups where computers are offered for sale in search of one that matches a user's specifications. This thesis asks whether it is possible to design general-purpose machine learning algorithms for such domains.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 1998
- Accession Number
- ADA360816
Entities
People
- Dayne Freitag
Organizations
- Carnegie Mellon University