Posts Tagged ‘NLP’

PostHeaderIcon NLP Abbreviations

NLP Abbreviations

ATN – Augmented Transition Network

CART – Classification And Regression Tree
CAT – Computer Aided Translation
CF – Context Free
CFG – Context Free Grammar
CG – Constraint Grammar
CL – Controlled Language
CS – Context Sensitive

DCG – Definite Clause Grammar
DET – Determiner
DLT – Distributed Language Translation
DM – Data Mining

EBMT – Examble-Based Machine Translation

FA – Finite Automation
FS – Feature Structures
FUG – Functional Unification Grammar

GPSG – Generalized Phrase Structure Grammar

HLT – Human Language Technology
HMM – Hidden Markov Model
HPSG – Head-Driven Phrase Structure Grammar

IE – Information Extraction
IV – Intransitive Verb

KBMT – Knowledge Based Machine Translation
KNN – K Nearest Neighbour

LFG – Lexical Functional Grammar
LHMM – Logical Hidden Markov Model
LM – Language Model

MAHT – Machine-Aided Human Translation
ME – Maximum Entropy
MT – Machine Translation
MUC – Message Understanding Conference
MWE- Multi-Word Entry

N – Noun
NER – Named Entity Recognition
NLG – Natural Language Generation
NLI – Natural Language Interface
NLP – Natural Language Processing
NP – Nondeterministic Polynomial, Noun Phrase

OT – Optimality Theory

PCFG – Probabilistic Context Free Grammar
PCM – Parallel Correspondence Model
PDA – Push Down Automata
POS – Part Of Speech

QA – Question Answering

RTN – Recursive Transition Network

S – Sentence
SFG – Systemic Functional Grammar

TAG – Tree Adjoining Grammar
TL – Target Language
TM – Translation Memory
TTS – Text-To-Speech

V – Verb
VP – Verb Phrase

WSD – Word Sense Disambiguation



PostHeaderIcon NLP References

Books For Reference

Ruslan Mitkov, The Oxford Handbook Of Computational Linguistics, Oxford Universitty Press, 2003.

Robert Dale, Hermani Moisi, Harold Somers, Handbook Of Natural Language Processing, Markcel Dekker Inc.

James Allen, Natural Language Processing, Pearson Education, 2003.

Christopher D.Manning & Henrich Schutze, Foundations Of Statistical Natural Language Processing, The MIT Press, 2001

Douglas Biber, Susan Conrad, Randi Reppen, Corpus Linguistics – Investigating Language Structure And Use, Cambridge University Press, 2000.

David Singleton, Language And The Lexicon: An Introduction, Arnold Publishers, 2000.

Andrew Radford, Minimalist Syntax: Exploring The Structure Of English, Cambridge University Press, 2004.

Rebecca Stott & Peter Chapman, Grammar And Writing, Pearson Education, 2001.

A. Athithan, Linguistic Structures In Tamil – A Historical Study, Madurai Kamaraj University, 1989.

R.Kothandaraman, Tamil Syntax: New Perspectives, Pondicherry Institute Of Linguistics And Culture, 1990.

Raymond Hickey, Corpus Presenter: Software for language analysis with a manual and a Corpus of Irish English as sample data, John Benjamins Publishing Company, 2003.

Guy Aston & Lou Burnard, The BNC Handbook: Exploring the British National Corpus with SARA, Edinburgh University Press, 1998.

Donny D. Steinberg, Hiroshi Nagata, David P.Aline, Psycholinguistics: Language, Mind and World, Second Edition, Pearson Education, 2001.

Cyruk Goutte, Nicola Cancedda, Marc Dymetman, and George Foster, Learning Machine Translation, PHI, 2010.

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PostHeaderIcon Natural Language Processing: Overview

Natural Language Processing (NLP) aims to acquire, understand and generate the human languages such as English, French, Tamil, Hindi, etc.

Symbolic Approaches to Natural Language Processing

Symbolic Approaches also known as Rationalist approaches believe that  significant part of the knowledge in the human mind is not derived by the senses but is fixed in advance, presumably by genetic inheritance. Noam Chomsky was the strong advocate of this approach. It was believed that machine can be made to function like human brain by giving some basic knowledge and reasoning mechanisms Linguistic knowledge is explicitly encoded in rule or other forms of representation. This helps automatic process of natural languages.

Natural Language analysis

It runs into many stages, namely tokenization, lexical analysis,  syntactic analysis, semantic analysis, and pragmatic analysis.

Syntactic analysis provides an order and structure of each sentence in the text. Semantic analysis is to find the literal meaning, and pragmatic analysis is to determine the meaning of the text in context. These major tasks are further broken down into, parsing and so on.

Natural Language Generation

This is to generate fluent and coherent multi-sentential texts from an underlying source of information. The kind of text generated ranging from a single word or a phrase as an answer to a question to full-page explanations and even to the extent of speech depending upon the context.

Empirical Approaches to Natural Language Processing

Empirical Approaches focus on the use of large amounts of data and the procedures involving statistical manipulations. Corpus, bulk of data in a particular format, comes handy for analysis. Crucial tasks using these approaches are POS tagging, alignment, collacations, word-sense-disambiguation, etc.

Challenges In Natural Language Processing

Still a perfect natural language processing system is developed. There are many problems like flexibility in the structure of sentences, ambiguity, etc.

Natural language processing applications require the availability of Lexical Resources, Corpora and Computational Models.

For Further Study

Foundations of Statistical Natural Language Processing

Handbook of Natural Language Processing, Second Edition (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Natural Language Understanding (2nd Edition)

Natural Language Processing with Python

Related Articles

Natural Language Understanding

Natural Language Generation

Open Problems

Linguistics: Overview

Tokenization: Overview

Parts Of Speech Tagging

Machine Translation