Automatic extraction and processing of document references

Inhaltsverzeichnis (Table of Contents)

• Introduction
  • Project description
  • Related tasks
  • Problem formalisation
  • Evaluation measures
  • Approaches to named entity recognition
• Machine learning approaches to sequence labelling
  • Classifier-based approaches
  • Probabilistic sequence models
    • Hidden Markov Models
    • Maximum Entropy Markov Models
    • Conditional Random Fields
    • Comparison of sequence models.
    • Motivation for using CRFs
  • Features
    • Lexical features.
    • Linguistic features
    • Orthographical features
    • Formatting
    • Context features
• Implementation
  • Definition of the named entity.
  • Data analysis
  • CRF implementation
  • Data preprocessing
    • File format conversion - class FileConverter
    • Extracting potential candidates - class ContextExtractor
    • Annotation guidelines
    • Generating training data - class Dataset Generator
  • Experiments
    • Initial feature types
    • Tagging scheme and performance measure
    • Number of models
    • Additional features
  • Critical evaluation
  • System overview
  • Processing of the extracted references
• Conclusions and future work
  • Conclusions
  • Future work
    • Additional reference types
    • Improving the model
    • Additional training data.
    • Precision recall trade-off.

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

The objective of this thesis is to develop a system that automatically extracts document names from technical documents, enabling the creation of a "knowledge net" that links related documents within a company's document management system. The system is based on Conditional Random Fields (CRFs) and aims to achieve high precision and acceptable recall scores. Key themes include:

• Automatic document reference extraction
• Conditional Random Fields (CRFs) for sequence labelling
• Named entity recognition
• Data preprocessing and training data generation
• System evaluation and future directions

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1 introduces the project's goal of automatically extracting document references from technical documents. It outlines the problem, formalizes its definition, and discusses evaluation measures. This chapter also explores existing approaches to named entity recognition, providing context for the chosen CRF-based approach.
• Chapter 2 delves into machine learning approaches for sequence labelling, focusing on probabilistic models such as Hidden Markov Models, Maximum Entropy Markov Models, and CRFs. It compares these models and provides a compelling justification for the use of CRFs in this specific project.
• Chapter 3 elaborates on the implementation of the system. It defines the target named entity, analyzes the data, and discusses the CRF implementation using the Java package by Sarawagi & Cohen (2005). The chapter further describes the data preprocessing techniques, including file format conversion, potential candidate extraction, and annotation guidelines. It concludes by discussing the experiments conducted to tune the system, focusing on feature selection, tagging schemes, and performance evaluation.

Schlüsselwörter (Keywords)

This thesis focuses on the automatic extraction and processing of document references using a CRF-based approach. The main keywords include: document reference extraction, named entity recognition, sequence labelling, Conditional Random Fields, data preprocessing, machine learning, supervised learning, evaluation metrics, precision, recall, technical documentation, knowledge net, document management system.