Natural language information retrieval in digital libraries
Tomek Strzalkowski, José Pérez-Carballo, Mihnea Marinescu · 1996
In this paper we report on some recent developments in joint NYU and GE natural language information retrieval system.The main characteristic of this system is the use of advanced natural language processing to enhance the effectiveness of term-based document retrieval.The system is designed around a traditional statistical backbone consisting of the indexer module, which builds inverted index files from pre-processed documents, and a retrieval engine which searches and ranks the documents in response to user queries.Natural language processing is used to (1) preprocess the documents in order to extract contentcarrying terms, (2) discover inter-term dependencies and build a conceptual hierarchy specific to the database domain, and (3) process user's natural language requests into effective search queries.This system has been used in NIST-sponscx-ed Text Retrieval Conferences (TREC), where we worked with approximately 3.3 GB ytes of text articles including material from the Wall Street Journal, the Associated Press newswire, the Federal Register, Ziff Communications's Computer Library, Department of Energy abstracts, U.S. Patents and the San Jose Mercury News, totaling more than 500 million words of English.The system have been designed to facilitate its scrdabili~y to deal with ever increasing amounts of data.In particular, a randomized index-splitting mechanism has been installed which allows the system to create a number of smaller indexes that can be independently and efficiently searched.As it will be discussed in more detail below, indexing terms were selected from among head-modifier pairs extracted from predicate-argument representations of sentences.abase nyuge3 mbase nyuge4 ibase