Introduction to Natural Language Processing

Natural Language Processing Quick Introduction Rohit Nayak Talentica Software

Part 1: Semantic Web, Uses of NLP, Core Concepts, Intro to GATE
Part 2: GATE Detailed Demo

NLP 420
Falling Tree Hits, Kills OR Forest Service Worker
Time flies like an arrow
Choosing a Program to Improve Your Future
Monkeys like bananas when they wake up
Monkeys like bananas when they are ripe

I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘ intelligent agents ’ people have touted for ages will finally materialize. – Tim Berners -Lee , 1999

Disaster Type: earthquake
location: Afghanistan
date: 05/30/1998
magnitude: 6.9
epicenter: a remote part of the country
damage:
human-effect:
victim: Thousands of people
number: Thousands
outcome: dead
physical-effect:
object: entire villages
outcome: damaged
QUAKE IN AFGHANISTAN Thousands of people are feared dead following... (voice-over) ... a powerful earthquake that hit Afghanistan today. The quake registered 6.9 on the Richter scale, centered in a remote part of the country . (on camera) Details now hard to come by, but reports say entire villages were buried by the quake .

Text Categorization Is the document about plants? sports? health and fitness? corporate acquisitions? … stock market? Document

Sentiment Classification Is the overall sentiment in the document positive? negative? In general, sentiment classification appears to be harder than categorizing by topic. Document

Information Extraction Information Extraction System text collection Who: _____ What: _____ Where:_____ When: _____ How: _____ Who: _____ What: _____ Where:_____ When: _____ How: _____ Who: _____ What: _____ Where:_____ When: _____ How: _____

Information Extraction (IE)
Recognition, tagging, and extraction into a structured representation, certain key elements of information, e.g. persons, companies, locations, organizations, from large collections of text.
These extractions can then be utilized for a range of applications including question-answering, visualization, and data mining.

Question-Answering
In contrast to Information Retrieval, which provides a list of potentially relevant documents in response to a user’s query
provides the user with either just the text of the answer itself or answer-providing passages.

Summarization
reduces a larger text into a shorter, yet richly constituted abbreviated narrative representation of the original document.

Machine Translation
perhaps the oldest of all NLP applications, various levels of NLP have been utilized in MT systems, ranging from the ‘word-based’ approach to applications that include higher levels of analysis.

Dialogue Systems
perhaps the omnipresent application of the future, in the systems envisioned by large providers of end-user applications.
Dialogue systems usually focus on a narrowly defined application (e.g. your refrigerator or home sound system),
currently utilize the phonetic and lexical levels of language. It is believed that utilization of all the levels of language processing explained above offer the potential for truly habitable dialogue systems.

Challenge of Semantic Web
Machine processable data to complement hypertext
Attach metadata to documents
Explicit: title, author, creation date
Implicit: deduced information like names of entities and their relation

Ontology
Specification of conceptualisation
Basis of document “understanding”
Creating and populating is very time-consuming, practically impossible

Simple Workflow
Classification
Tokeniser
Gazetteer
Sentence Splitter
Parts Of Speech Tagging
Named Entity Tagging
Final Extraction

Tools
GATE
OpenNLP
NLTK (python)
Stanford Parser
Weka for classification

GATE
General Architecture for Text Engineering
Over 10 years, active development
Most popular NLP platform
Current version 5.0
Built as a framework for both programmers and developers
Powerful GUI and well-documented Java API
Multilingual

GATE
Clean separation of low-level tasks (e.g., data storage) from the NLP components
Separation between linguistic data and algorithms that process it

JAPE
Just A Pleasant Experience
Pattern-Matching over Annotations
Regular Expression like
Can use Java in actions

Rule: Company1 Priority: 25 ( ({Token.orthography == upperInitial})+ {Lookup.kind == companyDesignator} ):companyMatch --> :companyMatch.NamedEntity = {kind = "company", rule = "Company1"}

CREOLE components
GATE plugins uses CREOLE
Collection of Reusable Objects for Language Engineering
Modified JavaBeans with XML configuration
Minimal component: 10 lines of Java, 10 lines of XML

External Slideshow
http://www.authorstream.com/presentation/Esteban-22479-ekaw2006-tutorial-Aims-Terminology-Semantic-Annotation-Motivation-Challenge-Web-Metadata-ext-as-Entertainment-ppt-powerpoint/ (27)

GATE Demo
Quick look
Detailed Demo next SIG

Introduction to Natural Language Processing

by rohitnayak on Dec 27, 2009

Accessibility

Categories

Upload Details

Usage Rights

1 Embed 23

Statistics

Introduction to Natural Language Processing Presentation Transcript