Textmining Information Extraction

Text Mining:Information extraction

Goals of information extraction
“Processing of natural language texts for the extraction of relevant content pieces” (MARTÍ AND CASTELLÓN, 2000)
Raw texts => structured databases
Templates filling
Improving search engines
Auxiliary tool for other language applications

Name Entity Recognition
Named Entities are proper names in texts, i.e. the
names of persons, organizations, locations, times and
quantities.
NER is the task of processing a text and identifying
named entities.

Why is Named Entity Recognition difficult?
-Names too numerous to include in dictionaries
-Variations
e.g. John Smith, Mr Smith, John
-Changing constantly
new names invent unknown words
-Ambiguity
For some proper nouns it is hard to determine the
category
Name

Example
Delimit the named entities in a text and tag them withNE
Categories:
– entity names - ENAMEX
– temporal expressions - TIMEX
– number expressions - NUMEX
Subcategories of tags
– captured by a SGML tag attribute called TYPE

Example
Original text:
The U.K. satellite television broadcaster said its subscriber base grew 17.5 percent during the past year to 5.35 million
• Tagged text:
The <ENAMEX TYPE="LOCATION">U.K.</ENAMEX>
satellite television broadcaster said its subscriber base grew
<NUMEX TYPE="PERCENT">17.5 percent</NUMEX>
during <TIMEX TYPE="DATE">the past year</TIMEX> to
5.35 million
Example

Maximum Entropy for NER
Use the probability distribution that has maximum entropy, or that is maximally uncertain, from those that are consistent with observed evidence
• P = {models consistent with evidence}
• H(p) = entropy of p
• PME = argmax p∈P H(p)

Maximum Entropy for NER
Given a set of answer candidates
Model the probability
Define Features Functions
Decision Rule

Template Filling
A template is a frame (of a record structure), consisting
of slots and fillers. A template denotes an event or a
semantic concept.
After extracting NEs, relations and events, IE fills an
appropriate template

Template filling techniques
Two common approaches for templatefilling:
– Statistical approach
– Finite-state cascade approach

Again, by using a sequence labeling method:
Label sequences of tokens as potential fillers for a particular slot
Train separate sequence classifiers for each slot
Slots are filled with the text segments identified by each slot’s
corresponding classifier
Statistical Approach

Statistical Approach
– Resolve multiple labels assigned to the same/overlapping text
segment by adding weights (heuristic confidence) to the slots
– State-of-the-art performance – F1-measure of 75 to 98
However, those methods are shown to be effective only
for small, homogenous data

Finite-State Template-Filling Systems
Message Understanding Conferences (MUC) – the
genesis of IE
DARPA funded significant efforts in IE in the early to mid 1990’s.
MUC was an annual event/competition where results were
presented.

Finite-State Template-Filling Systems
– Focused on extracting information from news articles:
• Terrorist events (MUC-4, 1992)
• Industrial joint ventures (MUC-5, 1993)
• Company management changes
– Informationextraction of particular interest to the intelligence
community (CIA, NSA). (Note: early ’90’s)

Applications
It has a wide range of application in
search engines
biomedical field
Customer profile analysis
Trend analysis
Information filtering and routing
Event tracks
news stories classification

conclusion
In this presentation we studied about
Goals of information extraction
Entity Extraction: The Maximum Entropy method
Template filling
Applications

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Textmining Information Extraction

by DataminingTools Inc

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