1. Introduction to Information Extraction
Chia-Hui Chang
Dept. of Computer Science and Information Engineering, National Central University, Taiwan

2. Problem Definition The output template of the IE task
Information Extraction (IE) is to identify relevant information from documents, pulling information from a variety of sources and aggregates it into a homogeneous form.
The output template of the IE task
Several fields (slots)
Several instances of a field

3. Difficulties of IE tasks depends on …
Text type
From Wall Street Journal articles, or message, to HTML documents.
Domain
From financial news, or tourist information, to various language.
Scenario

4. Various IE Tasks Free-text IE: Semi-structured IE:
For MUC (Message Understanding Conference)
E.g. terrorist activities, corporate joint ventures
Semi-structured IE:
E.g.: meta-search engines, shopping agents, Bio-integration system

5. Types of IE from MUC Named Entity recognition (NE)
Finds and classifies names, places, etc.
Coreference Resolution (CO)
Identifies identity relations between entities in texts.
Template Element construction (TE)
Adds descriptive information to NE results.
Scenario Template production (ST)
Fits TE results into specified event scenarios.

6. Name Entity Recognition

7. NE Recognition (Cont.)
Spanish: 93%
Japanese: 92%
Chinese: 84.51%

8. Coreference Resolution
Coreference resolution (CO) involves identifying identity relations between entities in texts.
For example, in
Alas, poor Yorick, I knew him well.
Tie “Yorick" with “him“.
The Sheffield system scored 51% recall and 71% precision.

9. Template Element Production
Adds description with named entities
Sheffield system scores 71%

10. Scenario Template Extraction
STs are the prototypical outputs of IE systems
They tie together TE entities into event and relation descriptions.
Performance for Sheffield: 49%
faculty/grishman/ IEtask15.book_2.html

11. Example
The operational domains that user interests are centered around are drug enforcement, money laundering, organized crime, terrorism, ….
1. Input: texts dealing with drug enforcement, money laundering, organized crime, terrorism, and legislation;
2. NE: recognizes entities in those texts and assigns them to one of a number of categories drawn from the set of entities of interest (person, company, );
3. TE: associates certain types of descriptive information with these entities, e.g. the location of companies;
4. ST: identifies a set (relatively small to begin with) of events of interest by tying entities together into event relations.

12. Example Text

13. Output Example (NE, TE)

14. Output (STs)

15. Another IE Example Corporate Management Changes Purpose
which positions in which organizations are changing hands?
who is leaving a position and where the person is going to?
who is appointed to a position and where the person is coming from?
the locations and types of the organizations involved in the succession events;
the names and titles of the persons involved in the succession events

16. Input Text
President Clinton nominated John Rollwagen, the chairman and CEO of Cray Research Inc., as the No. 2 Commerce Department official. Mr. Rollwagen said he wants to push the Clinton administration to aggressively confront U.S. trading partners such as Japan to open their markets, particularly for high-tech industries. In a letter sent throughout the Eagan, Minn.-based company on Friday, Mr. Rollwagen warned: "Whether we like it or not, our country is in an economic war; and we are at a key turning point in that war."
Cray said it has appointed John F. Carlson, its president and chief operating officer, to succeed him

17. Extraction Result Corporate Management Database Person Organization
Position
Transition
John Rollwagen
Cray Research Inc.
chairman
out
CEO
John F. Carlson in
Organization Database
Name
Location
Alias
Type
Cray Research Inc.
Eagan, Minn.
Cray
COMPANY
Commerce Department
GOVERNMENT

18. MUC
Data Set for
MET2
MUC3&4
MUC6&7 from LDC
MUC-6:
MUC-7
proceedings/muc_7_toc.html

19. Summary Evaluation Design Methodology Precision= Recall=
Natural Language Processing
Machine Learning
# of correctly extracted fields
# of extracted fields
# of correctly extracted fields
# of fields to be extracted

20. IE from Semi-structured Documents
Output Template: k-tuple
Multiple instances of a field
Missing data

21. Various IE Tasks for Semi-structured Documents
Multiple-record page extraction
One-record (singular) page extraction

22. Multiple-record page extraction

23. One-record (singular) page extraction

24. Summary Evaluation Design Methodology Precision= Recall=
Machine Learning
Pattern Mining
# of correctly extracted records
# of extracted records
# of correctly extracted records
# of records to be extracted

25. News Group IE
Example: Computer-Related Jobs

26. Output Template Between free-text IE and semi-structured IE
[CaliffRapier 99]

27. Annotated Training Examples
Most systems require annotated training examples (answer keys)
AutoSlog, Rapier, SRV, WIEN, Softmealy, Stalker
Very few systems require unannotated training examples
AutoSlog-TS, IEPAD, OLERA

28. The Type of Extraction Rule
Delimiter-based Rule
WIEN, Stalker
Content-based Rule
Context-based Rule
Rapier, AutoSlog, SRV, IEPAD

29. Background Knowledge For Rule Generalization Example
Implicit or Explicit
Example
Specified format for date, , etc.
Special feature for color, location, etc.

30. Conclusion Define the IE problem Specify the input: training example
with annotation, or
without annotation
Depict the extraction rule
Use necessary background knowledge

31. References
*H. Cunningham, Information Extraction – a User Guide,
*MUC-6, grishman/muc6.html
*I. Muslea, Extraction Patterns for Information Extraction Tasks: A Survey, The AAAI-99 Workshop on Machine Learning for Information Extraction.
Califf, Relational Learning of Pattern-Matching Rule for Information Extraction, AAAI-99.