Natural Language Processing The Plnlp Approach
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Natural Language Processing: The PLNLP Approach
Author: Karen Jensen
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Natural language is easy for people and hard for machines. For two generations, the tantalizing goal has been to get computers to handle human languages in ways that will be compelling and useful to people. Obstacles are many and legendary. Natural Language Processing: The PLNLP Approach describes one group's decade of research in pursuit of that goal. A very broad coverage NLP system, including a programming language (PLNLP) development tools, and analysis and synthesis components, was developed and incorporated into a variety of well-known practical applications, ranging from text critiquing (CRITIQUE) to machine translation (e.g. SHALT). This books represents the first published collection of papers describing the system and how it has been used. Twenty-six authors from nine countries contributed to this volume. Natural language analysis, in the PLNLP approach, is done is six stages that move smoothly from syntax through semantics into discourse. The initial syntactic sketch is provided by an Augmented Phrase Structure Grammar (APSG) that uses exclusively binary rules and aims to produce some reasonable analysis for any input string. Its `approximate' analysis passes to the reassignment component, which takes the default syntactic attachments and adjusts them, using semantic information obtained by parsing definitions and example sentences from machine-readable dictionaries. This technique is an example of one facet of the PLNLP approach: the use of natural language itself as a knowledge representation language -- an innovation that permits a wide variety of online text materials to be exploited as sources of semantic information. The next stage computes the intrasential argument structure and resolves all references, both NP- and VP-anaphora, that can be treated at this point in the processing. Subsequently, additional components, currently not so well developed as the earlier ones, handle the further disambiguation of word senses, the normalization of paraphrases, and the construction of a paragraph (discourse) model by joining sentential semantic graphs. Natural Language Processing: The PLNLP Approach acquaints the reader with the theory and application of a working, real-world, domain-free NLP system, and attempts to bridge the gap between computational and theoretical models of linguistic structure. It provides a valuable resource for students, teachers, and researchers in the areas of computational linguistics, natural processing, artificial intelligence, and information science.
Reversible Grammar in Natural Language Processing
Author: T. Strzalkowski
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Reversible grammar allows computational models to be built that are equally well suited for the analysis and generation of natural language utterances. This task can be viewed from very different perspectives by theoretical and computational linguists, and computer scientists. The papers in this volume present a broad range of approaches to reversible, bi-directional, and non-directional grammar systems that have emerged in recent years. This is also the first collection entirely devoted to the problems of reversibility in natural language processing. Most papers collected in this volume are derived from presentations at a workshop held at the University of California at Berkeley in the summer of 1991 organised under the auspices of the Association for Computational Linguistics. This book will be a valuable reference to researchers in linguistics and computer science with interests in computational linguistics, natural language processing, and machine translation, as well as in practical aspects of computability.
Natural Language Processing for the Semantic Web
This book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications. Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data to meaningful representations, which benefit enormously from the use of NLP technologies, thereby enabling applications such as connecting text to Linked Open Data, connecting texts to each other, semantic searching, information visualization, and modeling of user behavior in online networks. The first half of this book describes the basic NLP processing tools: tokenization, part-of-speech tagging, and morphological analysis, in addition to the main tools required for an information extraction system (named entity recognition and relation extraction) which build on these components. The second half of the book explains how Semantic Web and NLP technologies can enhance each other, for example via semantic annotation, ontology linking, and population. These chapters also discuss sentiment analysis, a key component in making sense of textual data, and the difficulties of performing NLP on social media, as well as some proposed solutions. The book finishes by investigating some applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future.