# INTRODUCTION TO ARTIFICIAL INTELLIGENCE - PowerPoint PPT Presentation

1 / 45

INTRODUCTION TO ARTIFICIAL INTELLIGENCE. Massimo Poesio Supervised Relation Extraction. RE AS A CLASSIFICATION TASK. Binary relations Entities already manually/automatically recognized Examples are generated for all sentences with at least 2 entities

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

## INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Massimo PoesioSupervised Relation Extraction

### RE AS A CLASSIFICATION TASK

• Binary relations

• Examples are generated for all sentences with at least 2 entities

• Number of examples generated per sentence isNC2 – Combination of N distinct entities selected 2 at a time

### THE SUPERVISED APPROACH TO RE

• Most current approaches to RE are kernel-based

• Different information is used

• Sequences of words, e.g., through the GLOBAL CONTEXT / LOCAL CONTEXT kernels of Bunescu and Mooney / GiulianoLavelli & Romano

• Syntactic information through the TREE KERNELS of Zelenko et al / Moschitti et al

• Semantic information in recent work

### KERNEL METHODS: A REMINDER

• Embedding the input data in a feature space

• Using a linear algorithm for discovering non-linear patterns

• Coordinates of images are not needed, only pairwise inner products

• Pairwiseinner products can be efficiently computed directly from X using a kernel function K:X×X→R

### THE WORD-SEQUENCE APPROACH

• Shallow linguistic Information:

• tokenization

• Lemmatization

• sentence splitting

• PoStagging

Claudio Giuliano, Alberto Lavelli, and Lorenza Romano (2007), FBK-IRST: Kernel methods for relation extraction, Proc. Of SEMEVAL-2007

### LINGUISTIC REALIZATION OF RELATIONS

Bunescu & Mooney, NIPS 2005

### WORD-SEQUENCE KERNELS

• Two families of “basic” kernels

• Global Context

• Local Context

• Linear combination of kernels

• Explicit computation

• Extremely sparse input representation

### EXPERIMENTAL RESULTS

• Biomedical data sets

• AIMed

• LLL

• Newspaper articles

• Roth and Yih

• SEMEVAL 2007

### OTHER APPROACHES TO RE

• Using syntactic information

• Using lexical features

### Syntactic information for RE

• Pros:

• more structured information useful when dealing with long-distance relations

• Cons:

• not always robust

• (and not available for all languages)

### Zelenko et al JMLR 2003

• TREE KERNEL defined over a shallow parse tree representation of the sentences

• approach vulnerable to unrecoverable parsing errors

• data set: 200 news articles (not publicly available)

• two types of relations : person-affiliation and organization-location

### CULOTTA & SORENSEN 2004

• generalized version of Zelenko’s kernel based on dependency trees (smallest dependency tree containing the two entities of the relation)

• a bag-of-words kernel is used to compensate syntactic errors

• data set: ACE 2002 & 2003

• results: syntactic information improves performance w.r.t. bag-of-words (good precision but low recall)

### EVALUATION CAMPAIGNS FOR RE

• Much of modern evaluation of methods is done by competing with other teams on evaluation campaigns like MUC and ACE

• Modern evaluation campaigns for RE: SEMEVAL (now *SEM)

• Interesting to look also at the problems of

• DATA CREATION

• EVALUATION METRICS

### SEMEVAL 2007

• 4th International Workshop on Semantic Evaluations

• Task 04: Classification of Semantic Relations between Nominals

• organizers: Roxana Girju, Marti Hearst, PreslavNakov, ViviNastase, Stan Szpakowicz, Peter Turney, DenizYuret

• 14 participating teams

### ACKNOWLEDGMENTS

• Many slides borrowed from

• Roxana Girju

• Alberto Lavelli