Automatic indexing and retrieval as a tool to improve information and technology transfer

During the last 20 years, linguistic data processing mainly has been seen as a tool to develop linguistic regularities (or detect irregularities) of a given natural language, especially to handle large textual databases ("Corpora"). A second motivation to use a computer was to test some theories or models of a language system (or a part of it) using a simulation program. As a result of both strategies, the "Saarbrücken Text Analysis System" has been implemented. At present, a very large lexical database is available to analyse written German texts morphologically and syntactically. The syntactic parser is able to handle every German sentence with more than 90% "correct" results. On the other hand, the development of large (textual) databases within different fields (e.g. law, patent specifications, medicine) is increasing rapidly. Therefore, a computer aided indexing system ("Computergestützte Texterschließung: CTX") has been developed at Regensburg and Saarbrücken University to improve the (even natural language oriented) access to textual data ("free text") applying linguistic strategies to information retrieval processes. Main results of feasibility studies, especially in the field of German Patent Documentation, are presented

Automatic indexing and retrieval as a tool to improve information and technology transfer

During the last 20 years, linguistic data processing mainly has been seen as a tool to develop linguistic regularities (or detect irregularities) of a given natural language, especially to handle large textual databases ("Corpora"). A second motivation to use a computer was to test some theories or models of a language system (or a part of it) using a simulation program. As a result of both strategies, the "Saarbrücken Text Analysis System" has been implemented. At present, a very large lexical database is available to analyse written German texts morphologically and syntactically. The syntactic parser is able to handle every German sentence with more than 90% "correct" results. On the other hand, the development of large (textual) databases within different fields (e.g. law, patent specifications, medicine) is increasing rapidly. Therefore, a computer aided indexing system ("Computergestützte Texterschließung: CTX") has been developed at Regensburg and Saarbrücken University to improve the (even natural language oriented) access to textual data ("free text") applying linguistic strategies to information retrieval processes. Main results of feasibility studies, especially in the field of German Patent Documentation, are presented

Linguistic Refactoring of Business Process Models

In the past decades, organizations had to face numerous challenges due to intensifying globalization and internationalization, shorter innovation cycles and growing IT support for business. Business process management is seen as a comprehensive approach to align business strategy, organization, controlling, and business activities to react flexibly to market changes. For this purpose, business process models are increasingly utilized to document and redesign relevant parts of the organization's business operations. Since companies tend to have a growing number of business process models stored in a process model repository, analysis techniques are required that assess the quality of these process models in an automatic fashion. While available techniques can easily check the formal content of a process model, there are only a few techniques available that analyze the natural language content of a process model. Therefore, techniques are required that address linguistic issues caused by the actual use of natural language. In order to close this gap, this doctoral thesis explicitly targets inconsistencies caused by natural language and investigates the potential of automatically detecting and resolving them under a linguistic perspective. In particular, this doctoral thesis provides the following contributions. First, it defines a classification framework that structures existing work on process model analysis and refactoring. Second, it introduces the notion of atomicity, which implements a strict consistency condition between the formal content and the textual content of a process model. Based on an explorative investigation, we reveal several reoccurring violation patterns are not compliant with the notion of atomicity. Third, this thesis proposes an automatic refactoring technique that formalizes the identified patterns to transform a non-atomic process models into an atomic one. Fourth, this thesis defines an automatic technique for detecting and refactoring synonyms and homonyms in process models, which is eventually useful to unify the terminology used in an organization. Fifth and finally, this thesis proposes a recommendation-based refactoring approach that addresses process models suffering from incompleteness and leading to several possible interpretations. The efficiency and usefulness of the proposed techniques is further evaluated by real-world process model repositories from various industries. (author's abstract

Inquiries into words, constraints and contexts : Festschrift in the honour of Kimmo Koskenniemi on his 60th birthday

Peer reviewe

A corpus-based induction learning approach to natural language processing.

by Leung Chi Hong.Thesis (Ph.D.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 163-171).Chapter Chapter 1. --- Introduction --- p.1Chapter Chapter 2. --- Background Study of Natural Language Processing --- p.9Chapter 2.1. --- Knowledge-based approach --- p.9Chapter 2.1.1. --- Morphological analysis --- p.10Chapter 2.1.2. --- Syntactic parsing --- p.11Chapter 2.1.3. --- Semantic parsing --- p.16Chapter 2.1.3.1. --- Semantic grammar --- p.19Chapter 2.1.3.2. --- Case grammar --- p.20Chapter 2.1.4. --- Problems of knowledge acquisition in knowledge-based approach --- p.22Chapter 2.2. --- Corpus-based approach --- p.23Chapter 2.2.1. --- Beginning of corpus-based approach --- p.23Chapter 2.2.2. --- An example of corpus-based application: word tagging --- p.25Chapter 2.2.3. --- Annotated corpus --- p.26Chapter 2.2.4. --- State of the art in the corpus-based approach --- p.26Chapter 2.3. --- Knowledge-based approach versus corpus-based approach --- p.28Chapter 2.4. --- Co-operation between two different approaches --- p.32Chapter Chapter 3. --- Induction Learning applied to Corpus-based Approach --- p.35Chapter 3.1. --- General model of traditional corpus-based approach --- p.36Chapter 3.1.1. --- Division of a problem into a number of sub-problems --- p.36Chapter 3.1.2. --- Solution selected from a set of predefined choices --- p.36Chapter 3.1.3. --- Solution selection based on a particular kind of linguistic entity --- p.37Chapter 3.1.4. --- Statistical correlations between solutions and linguistic entities --- p.37Chapter 3.1.5. --- Prediction of the best solution based on statistical correlations --- p.38Chapter 3.2. --- First problem in the corpus-based approach: Irrelevance in the corpus --- p.39Chapter 3.3. --- Induction learning --- p.41Chapter 3.3.1. --- General issues about induction learning --- p.41Chapter 3.3.2. --- Reasons of using induction learning in the corpus-based approach --- p.43Chapter 3.3.3. --- General model of corpus-based induction learning approach --- p.45Chapter 3.3.3.1. --- Preparation of positive corpus and negative corpus --- p.45Chapter 3.3.3.2. --- Statistical correlations between solutions and linguistic entities --- p.46Chapter 3.3.3.3. --- Combination of the statistical correlations obtained from the positive and negative corpora --- p.48Chapter 3.4. --- Second problem in the corpus-based approach: Modification of initial probabilistic approximations --- p.50Chapter 3.5. --- Learning feedback modification --- p.52Chapter 3.5.1. --- Determination of which correlation scores to be modified --- p.52Chapter 3.5.2. --- Determination of the magnitude of modification --- p.53Chapter 3.5.3. --- An general algorithm of learning feedback modification --- p.56Chapter Chapter 4. --- Identification of Phrases and Templates in Domain-specific Chinese Texts --- p.59Chapter 4.1. --- Analysis of the problem solved by the traditional corpus-based approach --- p.61Chapter 4.2. --- Phrase identification based on positive and negative corpora --- p.63Chapter 4.3. --- Phrase identification procedure --- p.64Chapter 4.3.1. --- Step 1: Phrase seed identification --- p.65Chapter 4.3.2. --- Step 2: Phrase construction from phrase seeds --- p.65Chapter 4.4. --- Template identification procedure --- p.67Chapter 4.5. --- Experiment and result --- p.70Chapter 4.5.1. --- Testing data --- p.70Chapter 4.5.2. --- Details of experiments --- p.71Chapter 4.5.3. --- Experimental results --- p.72Chapter 4.5.3.1. --- Phrases and templates identified in financial news articles --- p.72Chapter 4.5.3.2. --- Phrases and templates identified in political news articles --- p.73Chapter 4.6. --- Conclusion --- p.74Chapter Chapter 5. --- A Corpus-based Induction Learning Approach to Improving the Accuracy of Chinese Word Segmentation --- p.76Chapter 5.1. --- Background of Chinese word segmentation --- p.77Chapter 5.2. --- Typical methods of Chinese word segmentation --- p.78Chapter 5.2.1. --- Syntactic and semantic approach --- p.78Chapter 5.2.2. --- Statistical approach --- p.79Chapter 5.2.3. --- Heuristic approach --- p.81Chapter 5.3. --- Problems in word segmentation --- p.82Chapter 5.3.1. --- Chinese word definition --- p.82Chapter 5.3.2. --- Word dictionary --- p.83Chapter 5.3.3. --- Word segmentation ambiguity --- p.84Chapter 5.4. --- Corpus-based induction learning approach to improving word segmentation accuracy --- p.86Chapter 5.4.1. --- Rationale of approach --- p.87Chapter 5.4.2. --- Method of constructing modification rules --- p.89Chapter 5.5. --- Experiment and results --- p.94Chapter 5.6. --- Characteristics of modification rules constructed in experiment --- p.96Chapter 5.7. --- Experiment constructing rules for compound words with suffixes --- p.98Chapter 5.8. --- Relationship between modification frequency and Zipfs first law --- p.99Chapter 5.9. --- Problems in the approach --- p.100Chapter 5.10. --- Conclusion --- p.101Chapter Chapter 6. --- Corpus-based Induction Learning Approach to Automatic Indexing of Controlled Index Terms --- p.103Chapter 6.1. --- Background of automatic indexing --- p.103Chapter 6.1.1. --- Definition of index term and indexing --- p.103Chapter 6.1.2. --- Manual indexing versus automatic indexing --- p.105Chapter 6.1.3. --- Different approaches to automatic indexing --- p.107Chapter 6.2. --- Corpus-based induction learning approach to automatic indexing --- p.109Chapter 6.2.1. --- Fundamental concept about corpus-based automatic indexing --- p.110Chapter 6.2.2. --- Procedure of automatic indexing --- p.111Chapter 6.2.2.1. --- Learning process --- p.112Chapter 6.2.2.2. --- Indexing process --- p.118Chapter 6.3. --- Experiments of corpus-based induction learning approach to automatic indexing --- p.118Chapter 6.3.1. --- An experiment evaluating the complete procedures --- p.119Chapter 6.3.1.1. --- Testing data used in the experiment --- p.119Chapter 6.3.1.2. --- Details of the experiment --- p.119Chapter 6.3.1.3. --- Experimental result --- p.121Chapter 6.3.2. --- An experiment comparing with the traditional approach --- p.122Chapter 6.3.3. --- An experiment determining the optimal indexing score threshold --- p.124Chapter 6.3.4. --- An experiment measuring the precision and recall of indexing performance --- p.127Chapter 6.4. --- Learning feedback modification --- p.128Chapter 6.4.1. --- Positive feedback --- p.129Chapter 6.4.2. --- Negative feedback --- p.131Chapter 6.4.3. --- Change of indexed proportions of positive/negative training corpus in feedback iterations --- p.132Chapter 6.4.4. --- An experiment evaluating the learning feedback modification --- p.134Chapter 6.4.5. --- An experiment testing the significance factor in merging process --- p.136Chapter 6.5. --- Conclusion --- p.138Chapter Chapter 7. --- Conclusion --- p.140Appendix A: Some examples of identified phrases in financial news articles --- p.149Appendix B: Some examples of identified templates in financial news articles --- p.150Appendix C: Some examples of texts containing the templates in financial news articles --- p.151Appendix D: Some examples of identified phrases in political news articles --- p.152Appendix E: Some examples of identified templates in political news articles --- p.153Appendix F: Some examples of texts containing the templates in political news articles --- p.154Appendix G: Syntactic tags used in word segmentation modification rule experiment --- p.155Appendix H: An example of semantic approach to automatic indexing --- p.156Appendix I: An example of syntactic approach to automatic indexing --- p.158Appendix J: Samples of INSPEC and MEDLINE Records --- p.161Appendix K: Examples of Promoting and Demoting Words --- p.162References --- p.16