Foundations of statistical natural language processing

@inproceedings{Manning2002FoundationsOS,
  title={Foundations of statistical natural language processing},
  author={Christopher D. Manning and Hinrich Sch{\"u}tze},
  booktitle={SGMD},
  year={2002}
}
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own… Expand

Figures, Tables, and Topics from this paper

Natural Language Processing
TLDR
This chapter provides a survey of the main Natural Language Processing tasks and presents some related techniques, along with lexical resources of interest to the research community. Expand
On Statistical Methods in Natural Language Processing
TLDR
A definition of NLP is started as concerned with the design and implementation of effective natural language input and output components for computational systems and it is argued that the apparent dichotomy between “rule-based” and “statistical” methods is an over-simplification at best. Expand
Natural language processing
  • G. Chowdhury
  • Computer Science
  • Annu. Rev. Inf. Sci. Technol.
  • 2003
TLDR
This chapter presents the challenges of NLP, progress so far made in this field, NLP applications, components of N LP, and grammar of English language—the way machine requires it. Expand
In Defense of Symbolic NLP
TLDR
The paper concludes that best results are obtained using a combination of the two approaches when statistical methods are applied to the output of a deterministic parser. Expand
Automated Construction of Database Interfaces: Intergrating Statistical and Relational Learning for Semantic Parsing
TLDR
This work presents a method for integrating statistical and relational learning techniques for this task which exploits the strength of both approaches and suggests that such an approach is more robust than a previous purely logic-based approach. Expand
Robust Automated Natural Language Processing with Multiword Expressions and Collocations
This tutorial aims to provide attendees with a clear notion of the linguistic and distributional characteristics of multiword expressions (MWEs), their relevance for robust automated natural languageExpand
Speech and language processing - an introduction to natural language processing, computational linguistics, and speech recognition
TLDR
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora, to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Expand
Statistical Language Models and Information Retrieval: Natural Language Processing Really Meets Retrieval
TLDR
This article introduces a new formal model of information retrieval based on the application of statistical language models and gives an introductory overview of simple natural language processing techniques that are often used for information retrieval and can be modeled by the new language modeling approach. Expand
Linguistic Structure Prediction
  • Noah A. Smith
  • Computer Science
  • Synthesis Lectures on Human Language Technologies
  • 2011
TLDR
This work considers statistical, computational approaches to modeling linguistic structure and approaches to decoding and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. Expand
Implementation of sentence parser for Hungarian language in natural language processing
  • L. Kovács, P. Barabás
  • Computer Science
  • 2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI)
  • 2010
TLDR
An ECG-based sentence analyzer is proposed that translates the incoming natural language sentences into API level function calls and analyses the importance of semantic in understanding of a language. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 294 REFERENCES
Corpus-Based Approaches to Semantic Interpretation in Natural Language Processing
TLDR
An introduction to some of the emerging research in the application of corpusbased learning techniques to problems in semantic interpretation, namely, word-sense disambiguation and semantic parsing. Expand
Natural Language in Information Retrieval
It seems the time is ripe for the two to meet: NLP has grown out of prototypes and IR is having hard time trying to improve precision. Two examples of possible approaches are considered below.Expand
Statistical language learning
TLDR
Eugene Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages and is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning. Expand
Natural Language Parsing as Statistical Pattern Recognition
TLDR
This work proposes an automatic method for acquiring a statistical parser from a set of parsed sentences which takes advantage of some initial linguistic input, but avoids the pitfalls of the iterative and seemingly endless grammar development process. Expand
Progress in the Application of Natural Language Processing to Information Retrieval Tasks
TLDR
This paper will examine and review recent progress in using the lexical, syntactic, semantic and discourse levels of the language analysis for tasks like automatic and semi-automatic indexing of text, text retrieval, text abstracting and summarisation, thesaurus generation from text corpus and conceptual information retrieval. Expand
Exploiting Syntactic Structure for Language Modeling
TLDR
A language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies and usable for automatic speech recognition is presented. Expand
A Fully Statistical Approach to Natural Language Interfaces
TLDR
This work presents a natural language interface system which is based entirely on trained statistical models, resulting in an end-to-end system that maps input utterances into meaning representation frames. Expand
Noun-phrase co-occurrence statistics for semi-automatic semantic lexicon construction
TLDR
This paper presents an algorithm for extracting potential entries for a category from an on-line corpus, based upon a small set of exemplars, that could be viewed as an ``enhancer'' of existing broad-coverage resources. Expand
Probabilistic Tree-Adjoining Grammar as a Framework for Statistical Natural Language Processing
TLDR
This paper argues that a purely structural apprach, exemplified by probabilistic context-free grammar, lacks sufficient sensitivity to lexical context, and that lexical co-occurence analyses require a richer notion of locality that is best provided by importing some notion of structure. Expand
Computer Aided Interpretation of Lexical Coocurrences
TLDR
The system presented hereafter, PETRARCA, detects word cooccurrences from a large sample of press agency releases on finance and economics, and uses these associations to build a case-based semantic lexicon. Expand
...
1
2
3
4
5
...