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Computer parsing technology, which breaks down complex linguistic structures into their constituent parts, is a key research area in the automatic processing of human language. This volume is a collection of contributions from leading researchers in the field of natural language processing technology, each of whom detail their recent work which includes new techniques as well as results. The book presents an overview of the state of the art in current research into parsing technologies, focusing on three important themes: dependency parsing, domain adaptation, and deep parsing. The technology, which has a variety of practical uses, is especially concerned with the methods, tools and software that can be used to parse automatically. Applications include extracting information from free text or speech, question answering, speech recognition and comprehension, recommender systems, machine translation, and automatic summarization. New developments in the area of parsing technology are thus widely applicable, and researchers and professionals from a number of fields will find the material here required reading. As well as the other four volumes on parsing technology in this series this book has a breadth of coverage that makes it suitable both as an overview of the field for graduate students, and as a reference for established researchers in computational linguistics, artificial intelligence, computer science, language engineering, information science, and cognitive science. It will also be of interest to designers, developers, and advanced users of natural language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.

Significant advances in natural language processing require the development of adaptive syterns both for spoken and written language: systems that can interact naturally with human users, extend easily to new domains, produce readily usable translations of several languages, search the web rapidly and accurately, surnrnarise news coherently, and detect shifts in moods and emotions. Recent statistical datadriven techniques in natural language processing aim at acquiring the needed adaptivity by modelling the syntactic and lexical properties of large quantities of naturally occurring text.

In chapter 1, I have presented an hypothesis on how to partition the different types of computations that need to be performed to recover the syntactic structure. I have argued that linguistic theory itself, in particular the content of the principles, provides a guideline to perform this partitioning.

The efficient understanding of a sentence of natural language is a difficult task, the solution of which calls into play knowledge derived from several disciplines, such as linguistics, computer science, and, often, psychology. One of the principal sources of difficulty in the solution of the problem is constituted by the complex interactions between different types of information, such as lexical, morphological, syntactic, semantic, pragmatics and word knowledge, to which one can add issues of language use, such as frequency of usage and domain-dependent terminolgy.

In the previous chapter, I have illustrated how configurations and categorial information are encoded in the parser. They are represented as tables which are consulted on-line by an LR algorithm. Not all the principles of the grammar, however, are represented in these tables. In particular the annotation of the nodes by what I have called syntactic features, e.g. θ-role and Case is performed on-line, while building the tree. Feature assignment can be strictly local to one of the configurations defined by X theory, or it might involve sharing features between distant elements. This kind of feature assignment will be discussed in the first sections.

This chapter presents an overview of the parser, and provides an example of how a sentence is run. With at least some high-level knowledge of the current implementation, I then turn to compare this parser to other principle-based architectures. The explanation of the technical parsing terms can be found in the Appendix, section A.3. An overall picture of the organization of the parser is shown in Figure 2.1.