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Analysing Crime-Scene Reports. Scene of Crime Information System. Katerina Pastra and Horacio Saggion University of Sheffield. Outline. Project Overview SOCIS Architecture Corpus Linguistic Analysis Pointers. Project Overview. 2000 - 2003.

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analysing crime scene reports

Analysing Crime-Scene Reports

Scene of Crime Information System

Katerina Pastra and Horacio Saggion

University of Sheffield

outline
Outline
  • Project Overview
  • SOCIS Architecture
  • Corpus
  • Linguistic Analysis
  • Pointers
project overview
Project Overview

2000 - 2003

  • Domain: Scene of Crime Investigation (SOC)
  • Main Features :

1. Multimedia briefing

        • · Summarisation of text and images
  • 2. Generation
        • · Of formal reports & of photo albums

3. Intelligent Search

project overview 2
Project Overview (2)
  • Other systems for Crime Investigation:

· Academic R&D Projects

· Governmental agencies’ Systems

· Commercial Systems

BUT: SOCIS brings ‘intelligence’ to CI systems

  • The ‘Digital Evidence in Court’ issue:

· Authenticity has to be verified

·Recently accepted in court

a view of socis

+

Image processing

Text processing

Integrated Knowledge Base

A view of SOCIS
text processing
Text Processing
  • - Text Corpus
  • - Information Extraction system
  • >> Named Entities Recognition
  • >> Co-reference Resolution
  • Need:
  • Linguistic Analysis of the Language at the SOC
  • Lexical Information
  • Morphosyntactic Information
  • Semantic Information
the corpus
The Corpus

4 days spent with a SOCO:

12 scenes visited * 2 complete case files examined * official documentation collected

  • Official documentation :

SOC Reports = 77

Photo Indexes = 300

Witness Statements = 14

  • Reported SOC Information :

Press Association = 792

Washington Post = 233

Crime Watch = 8

  • Reports - Photo indexes Witness statements Photographs
  • For the same case
  • For major crime
  • Of significant quantity

NEEDED

soc language characteristics
SOC Language Characteristics
  • General Characteristics:
    • Telegraphic
    • Descriptive
    • Accurate
    • Objective

Special text type : Reports

lexical information
Lexical Information

Characteristics:

- Extensive use of abbreviations

- Jargon

Creation of Word - Lists (gazetteers):

- Based on PITO’s CDM

- Over 200 lists (domain + general)

Words of interest are assigned a semantic category

morphosyntactic features
Morphosyntactic Features
  • Extensive Ellipsis
  • Simple temporal dimensions
  • Limited co-ordination
  • Sub-ordination avoided
  • POS : NPs, PPs
  • Adjuncts of place - time, Qualifiers

For identifying entities of interest automatically,

we need to write specific rules using:

  • The word lists + Context Information
pointers
Pointers
  • SOCIS Sheffield Web Page

http://www.dcs.shef.ac.uk/nlp/socis



  • SOCIS Surrey Web Page

http://www.computing.surrey.ac.uk/ai/socis



  • NLP Group

http://www.dcs.shef.ac.uk/nlp

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