Text Retrieval and Filtering: Analytical Models of Performance is the
first book that addresses the problem of analytically computing the
performance of retrieval and filtering systems. The book describes means
by which retrieval may be studied analytically, allowing one to
describe current performance, predict future performance, and to
understand why systems perform as they do. The focus is on retrieving
and filtering natural language text, with material addressing retrieval
performance for the simple case of queries with a single term, the more
complex case with multiple terms, both with term independence and term
dependence, and for the use of grammatical information to improve
performance. Unambiguous statements of the conditions under which one
method or system will be more effective than another are developed.
Text Retrieval and Filtering: Analytical Models of Performance focuses
on the performance of systems that retrieve natural language text,
considering full sentences as well as phrases and individual words. The
last chapter explicitly addresses how grammatical constructs and methods
may be studied in the context of retrieval or filtering system
performance. The book builds toward solving this problem, although the
material in earlier chapters is as useful to those addressing
non-linguistic, statistical concerns as it is to linguists. Those
interested in grammatical information should be cautioned to carefully
examine earlier chapters, especially Chapters 7 and 8, which discuss
purely statistical relationships between terms, before moving on to
Chapter 10, which explicitly addresses linguistic issues.
Text Retrieval and Filtering: Analytical Models of Performance is
suitable as a secondary text for a graduate level course on Information
Retrieval or Linguistics, and as a reference for researchers and
practitioners in industry.
