While reading documents, you often encounter text passages advising you to refer to other documents for more information about a specific topic. These references to other documents are particularly common in technical documents, written for the sole purpose of providing the reader with as much relevant information as possible, without rephrasing information that can be found elsewhere. Knowing how the documents in a system are interrelated, i.e. which other documents a document refers to or is referred by, can be extremely helpful when trying to get access to relevant information. A typical
example of such a “knowledge net” providing information about document relations is CiteSeer, a digital library of academic literature. For each document in the library system, CiteSeer displays lists of related documents, such as a list of documents that
the current document cites as well as a list of documents that the current document is cited by. The assumption that inspired this thesis is that such lists are not only helpful when reading academic literature but could also assist a reader of technical documents
stored in a company’s document management system. The idea was thus to extend an existing document management system by displaying, for each document stored in the system, a list of links to documents that the current document refers to. As information about how the documents in this system are interrelated was not available,
the focus of the project underlying this thesis was on the first step towards solving this task: automatically analyzing documents in order to extract names of related documents. Once all document names mentioned in a document have been extracted, the next step would then be to search for these documents in the system’s database and, in case they have been successfully found, create links to the respective documents.
The outcome of the project was a system that performs the extraction task. It is based on Conditional Random Fields, a machine learning technique introduced by Lafferty et al. (2001), and is able to extract document names from unseen documents, achieving high precision scores (88%) and acceptable recall scores (65%) on a test dataset.
The implementation is based on a Java package provided by Sarawagi & Cohen (2005), which was adapted and extended to suit the nature of the task. As the approach is based on supervised learning, the project also involved the generation of appropriate training
data.
Table of Contents
1 Introduction
1.1 Project description
1.2 Related tasks
1.3 Problem formalisation
1.4 Evaluation measures
1.5 Approaches to named entity recognition
2 Machine learning approaches to sequence labelling
2.1 Classifier-based approaches
2.2 Probabilistic sequence models
2.2.1 Hidden Markov Models
2.2.2 Maximum Entropy Markov Models
2.2.3 Conditional Random Fields
2.2.4 Comparison of sequence models
2.2.5 Motivation for using CRFs
2.3 Features
2.3.1 Lexical features
2.3.2 Linguistic features
2.3.3 Orthographical features
2.3.4 Formatting
2.3.5 Context features
3 Implementation
3.1 Definition of the named entity
3.2 Data analysis
3.3 CRF implementation
3.4 Data preprocessing
3.4.1 File format conversion - class FileConverter
3.4.2 Extracting potential candidates - class ContextExtractor
3.4.3 Annotation guidelines
3.4.4 Generating training data - class DatasetGenerator
3.5 Experiments
3.5.1 Initial feature types
3.5.2 Tagging scheme and performance measure
3.5.3 Number of models
3.5.4 Additional features
3.6 Critical evaluation
3.7 System overview
3.8 Processing of the extracted references
4 Conclusions and future work
4.1 Conclusions
4.2 Future work
4.2.1 Additional reference types
4.2.2 Improving the model
4.2.3 Additional training data
4.2.4 Precision recall trade-off
Research Objectives and Key Themes
This thesis aims to develop a system for the automatic extraction of document references from technical documentation to improve information accessibility within a document management system. The research specifically focuses on addressing the extraction task as a named entity recognition problem using machine learning techniques.
- Automatic extraction of document references from text.
- Application of Conditional Random Fields (CRFs) for sequence labelling.
- Definition and evaluation of diverse feature sets including lexical, orthographical, and contextual features.
- System implementation for use in technical document management environments.
- Performance optimization through precision and recall trade-off analysis.
Excerpt from the Book
3.2 Data analysis
The dataset consists of 708 documents, mostly written in English, downloaded from different databases storing documentation issued by the client’s Global Firmware Development department. The references contained in these documents can be divided into two major types:
1. References found in separate, specifically labelled sections (section references)
2. References found within the text body of the document (in-text references)
The difficulties related to the extraction task differ depending on the type of reference. For section references, the first difficulty is to find the section, which can be difficult because there exists no naming convention for reference section headings. Depending on the author of the document, this section can simply be called “References” but section headings like “External Documentation”, “Reference Material”, “Refered Documents” or “Referenced Documentation” are found as well. The references listed in separate reference sections can be considered a kind of semi-structured text, where the references within the same section are usually formatted in a similar way (e.g. <document name> “issued by” <author>). However, these internal standards are subject to the taste of the document’s author and not consistent across documents.
Summary of Chapters
1 Introduction: Provides the project background, formalizes the extraction problem, and outlines the evaluation metrics used for named entity recognition.
2 Machine learning approaches to sequence labelling: Discusses various probabilistic sequence models, including HMMs, MEMMs, and CRFs, and highlights the feature sets commonly used in such tasks.
3 Implementation: Details the practical steps taken to implement the extraction system, including data preprocessing, annotation guidelines, feature engineering, and the resulting experimental performance.
4 Conclusions and future work: Summarizes the key achievements, evaluates the system's performance against requirements, and suggests future improvements like semi-supervised learning and precision-recall tuning.
Keywords
Conditional Random Fields, CRF, Named Entity Recognition, NER, Sequence Labelling, Information Extraction, Machine Learning, Technical Documentation, Document Management System, Feature Engineering, Natural Language Processing, Precision, Recall, F-measure, Text Processing.
Frequently Asked Questions
What is the fundamental goal of this thesis?
The thesis aims to automate the extraction of document references from technical documents to enable easier navigation and information retrieval within an existing document management system.
What are the central research themes?
Key themes include the application of machine learning for named entity recognition, feature engineering for text analysis, and the implementation of probabilistic sequence models in a real-world industrial setting.
What is the primary research question?
The research asks how machine learning techniques, specifically Conditional Random Fields, can be effectively applied to automatically identify and extract document names from unstructured and semi-structured technical texts.
Which scientific methods are applied in this work?
The author utilizes supervised learning, specifically the Conditional Random Field (CRF) framework, supported by various feature extraction methods such as lexical, orthographical, and contextual analysis.
What does the main body of the work cover?
The main body describes the entire pipeline: from data analysis and preprocessing, through the selection and implementation of the CRF model, to the extensive experimental tuning and evaluation of the system.
Which keywords best characterize this work?
Key terms include Conditional Random Fields, Named Entity Recognition, sequence labelling, information extraction, and document management.
How does the system distinguish between document references and other entities?
The system distinguishes them by using specialized features, including domain-specific lexicons (external dictionaries) and contextual patterns learned from the training data, which differentiate document titles from other entities.
What were the final performance results of the system?
The final system achieved an F-measure of 74.9% (87.9% precision and 65.3% recall), which significantly exceeded the 66% requirement set by the client.
- Quote paper
- Kathrin Eichler (Author), 2007, Automatic extraction and processing of document references, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/158610
