Christopher D. Manning

Introduction to information retrieval

Hinrich Schütze

Christopher D.. Manning

Hinrich (Uni Stuttgart Schutze, Ims)

An introduction to statistical natural language processing (NLP). The text contains the theory and algorithms needed for building NLP tools. Topics covered include: mathematical and linguistic foundations; statistical methods; collocation finding; word sense disambiguation; and probalistic parsing.

Foundations of Statistical Natural Language Processing

This volume considers and examines some of the phenomena that have led languages to be considered 'ergative'. Languages considered 'ergative' have only been sparsely studied, and many fundamental questions in their analysis seem at best incompletely answered. This volume fills that void by focusing on some of the basic issues: when ergativity should be analysed as syntactic or morphological; whether languages can be divided into two classes of syntactically and morphologically ergative languages, and if so where the division should be drawn; and whether ergative arguments are always core roles or not. Christopher Manning's codification of syntactic approaches to dealing with ergative languages is based on a hypothesis he terms the 'Inverse Grammatical Relations hypothesis'. This hypothesis adopts a framework that decouples prominence at the levels of grammatical relations and argument structure. The result is two notions of subject: grammatical subject and argument structure subject and a uniform analysis of syntactically ergative and Philippine languages. These language groups, the syntactically ergative and Philippine languages, allow an inverse mapping in the prominence of the two highest terms between argument structure and grammatical relations. This volume combines good scholarship with innovative ideas into an important work that will appeal to a wide range of linguists and scholars.

Argument Structure and Grammatical Relations

Ergativity

Prabhakar Raghavan

Introduction to Information Retrieval

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Christopher D. Manning

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