Hybrid tag-set for natural language processing.

Leung Wai Kwong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 90-95).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Objective --- p.3Chapter 1.3 --- Organization of thesis --- p.3Chapter 2 --- Background --- p.5Chapter 2.1 --- Chinese Noun Phrases Parsing --- p.5Chapter 2.2 --- Chinese Noun Phrases --- p.6Chapter 2.3 --- Problems with Syntactic Parsing --- p.11Chapter 2.3.1 --- Conjunctive Noun Phrases --- p.11Chapter 2.3.2 --- De-de Noun Phrases --- p.12Chapter 2.3.3 --- Compound Noun Phrases --- p.13Chapter 2.4 --- Observations --- p.15Chapter 2.4.1 --- Inadequacy in Part-of-Speech Categorization for Chi- nese NLP --- p.16Chapter 2.4.2 --- The Need of Semantic in Noun Phrase Parsing --- p.17Chapter 2.5 --- Summary --- p.17Chapter 3 --- Hybrid Tag-set --- p.19Chapter 3.1 --- Objectives --- p.19Chapter 3.1.1 --- Resolving Parsing Ambiguities --- p.19Chapter 3.1.2 --- Investigation of Nominal Compound Noun Phrases --- p.20Chapter 3.2 --- Definition of Hybrid Tag-set --- p.20Chapter 3.3 --- Introduction to Cilin --- p.21Chapter 3.4 --- Problems with Cilin --- p.23Chapter 3.4.1 --- Unknown words --- p.23Chapter 3.4.2 --- Multiple Semantic Classes --- p.25Chapter 3.5 --- Introduction to Chinese Word Formation --- p.26Chapter 3.5.1 --- Disyllabic Word Formation --- p.26Chapter 3.5.2 --- Polysyllabic Word Formation --- p.28Chapter 3.5.3 --- Observation --- p.29Chapter 3.6 --- Automatic Assignment of Hybrid Tag to Chinese Word --- p.31Chapter 3.7 --- Summary --- p.34Chapter 4 --- Automatic Semantic Assignment --- p.35Chapter 4.1 --- Previous Researches on Semantic Tagging --- p.36Chapter 4.2 --- SAUW - Automatic Semantic Assignment of Unknown Words --- p.37Chapter 4.2.1 --- POS-to-SC Association (Process 1) --- p.38Chapter 4.2.2 --- Morphology-based Deduction (Process 2) --- p.39Chapter 4.2.3 --- Di-syllabic Word Analysis (Process 3 and 4) --- p.41Chapter 4.2.4 --- Poly-syllabic Word Analysis (Process 5) --- p.47Chapter 4.3 --- Illustrative Examples --- p.47Chapter 4.4 --- Evaluation and Analysis --- p.49Chapter 4.4.1 --- Experiments --- p.49Chapter 4.4.2 --- Error Analysis --- p.51Chapter 4.5 --- Summary --- p.52Chapter 5 --- Word Sense Disambiguation --- p.53Chapter 5.1 --- Introduction to Word Sense Disambiguation --- p.54Chapter 5.2 --- Previous Works on Word Sense Disambiguation --- p.55Chapter 5.2.1 --- Linguistic-based Approaches --- p.56Chapter 5.2.2 --- Corpus-based Approaches --- p.58Chapter 5.3 --- Our Approach --- p.60Chapter 5.3.1 --- Bi-gram Co-occurrence Probabilities --- p.62Chapter 5.3.2 --- Tri-gram Co-occurrence Probabilities --- p.63Chapter 5.3.3 --- Design consideration --- p.65Chapter 5.3.4 --- Error Analysis --- p.67Chapter 5.4 --- Summary --- p.68Chapter 6 --- Hybrid Tag-set for Chinese Noun Phrase Parsing --- p.69Chapter 6.1 --- Resolving Ambiguous Noun Phrases --- p.70Chapter 6.1.1 --- Experiment --- p.70Chapter 6.1.2 --- Results --- p.72Chapter 6.2 --- Summary --- p.78Chapter 7 --- Conclusion --- p.80Chapter 7.1 --- Summary --- p.80Chapter 7.2 --- Difficulties Encountered --- p.83Chapter 7.2.1 --- Lack of Training Corpus --- p.83Chapter 7.2.2 --- Features of Chinese word formation --- p.84Chapter 7.2.3 --- Problems with linguistic sources --- p.85Chapter 7.3 --- Contributions --- p.86Chapter 7.3.1 --- Enrichment to the Cilin --- p.86Chapter 7.3.2 --- Enhancement in syntactic parsing --- p.87Chapter 7.4 --- Further Researches --- p.88Chapter 7.4.1 --- Investigation into words that undergo semantic changes --- p.88Chapter 7.4.2 --- Incorporation of more information into the hybrid tag-set --- p.89Chapter A --- POS Tag-set by Tsinghua University (清華大學） --- p.96Chapter B --- Morphological Rules --- p.100Chapter C --- Syntactic Rules for Di-syllabic Words Formation --- p.10

A robust unification-based parser for Chinese natural language processing.

Chan Shuen-ti Roy.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 168-175).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.12Chapter 1.1. --- The nature of natural language processing --- p.12Chapter 1.2. --- Applications of natural language processing --- p.14Chapter 1.3. --- Purpose of study --- p.17Chapter 1.4. --- Organization of this thesis --- p.18Chapter 2. --- Organization and methods in natural language processing --- p.20Chapter 2.1. --- Organization of natural language processing system --- p.20Chapter 2.2. --- Methods employed --- p.22Chapter 2.3. --- Unification-based grammar processing --- p.22Chapter 2.3.1. --- Generalized Phase Structure Grammar (GPSG) --- p.27Chapter 2.3.2. --- Head-driven Phrase Structure Grammar (HPSG) --- p.31Chapter 2.3.3. --- Common drawbacks of UBGs --- p.33Chapter 2.4. --- Corpus-based processing --- p.34Chapter 2.4.1. --- Drawback of corpus-based processing --- p.35Chapter 3. --- Difficulties in Chinese language processing and its related works --- p.37Chapter 3.1. --- A glance at the history --- p.37Chapter 3.2. --- Difficulties in syntactic analysis of Chinese --- p.37Chapter 3.2.1. --- Writing system of Chinese causes segmentation problem --- p.38Chapter 3.2.2. --- Words serving multiple grammatical functions without inflection --- p.40Chapter 3.2.3. --- Word order of Chinese --- p.42Chapter 3.2.4. --- The Chinese grammatical word --- p.43Chapter 3.3. --- Related works --- p.45Chapter 3.3.1. --- Unification grammar processing approach --- p.45Chapter 3.3.2. --- Corpus-based processing approach --- p.48Chapter 3.4. --- Restatement of goal --- p.50Chapter 4. --- SERUP: Statistical-Enhanced Robust Unification Parser --- p.54Chapter 5. --- Step One: automatic preprocessing --- p.57Chapter 5.1. --- Segmentation of lexical tokens --- p.57Chapter 5.2. --- "Conversion of date, time and numerals" --- p.61Chapter 5.3. --- Identification of new words --- p.62Chapter 5.3.1. --- Proper nouns ´ؤ Chinese names --- p.63Chapter 5.3.2. --- Other proper nouns and multi-syllabic words --- p.67Chapter 5.4. --- Defining smallest parsing unit --- p.82Chapter 5.4.1. --- The Chinese sentence --- p.82Chapter 5.4.2. --- Breaking down the paragraphs --- p.84Chapter 5.4.3. --- Implementation --- p.87Chapter 6. --- Step Two: grammar construction --- p.91Chapter 6.1. --- Criteria in choosing a UBG model --- p.91Chapter 6.2. --- The grammar in details --- p.92Chapter 6.2.1. --- The PHON feature --- p.93Chapter 6.2.2. --- The SYN feature --- p.94Chapter 6.2.3. --- The SEM feature --- p.98Chapter 6.2.4. --- Grammar rules and features principles --- p.99Chapter 6.2.5. --- Verb phrases --- p.101Chapter 6.2.6. --- Noun phrases --- p.104Chapter 6.2.7. --- Prepositional phrases --- p.113Chapter 6.2.8. --- """Ba2"" and ""Bei4"" constructions" --- p.115Chapter 6.2.9. --- The terminal node S --- p.119Chapter 6.2.10. --- Summary of phrasal rules --- p.121Chapter 6.2.11. --- Morphological rules --- p.122Chapter 7. --- Step Three: resolving structural ambiguities --- p.128Chapter 7.1. --- Sources of ambiguities --- p.128Chapter 7.2. --- The traditional practices: an illustration --- p.132Chapter 7.3. --- Deficiency of current practices --- p.134Chapter 7.4. --- A new point of view: Wu (1999) --- p.140Chapter 7.5. --- Improvement over Wu (1999) --- p.142Chapter 7.6. --- Conclusion on semantic features --- p.146Chapter 8. --- "Implementation, performance and evaluation" --- p.148Chapter 8.1. --- Implementation --- p.148Chapter 8.2. --- Performance and evaluation --- p.150Chapter 8.2.1. --- The test set --- p.150Chapter 8.2.2. --- Segmentation of lexical tokens --- p.150Chapter 8.2.3. --- New word identification --- p.152Chapter 8.2.4. --- Parsing unit segmentation --- p.156Chapter 8.2.5. --- The grammar --- p.158Chapter 8.3. --- Overall performance of SERUP --- p.162Chapter 9. --- Conclusion --- p.164Chapter 9.1. --- Summary of this thesis --- p.164Chapter 9.2. --- Contribution of this thesis --- p.165Chapter 9.3. --- Future work --- p.166References --- p.168Appendix I --- p.176Appendix II --- p.181Appendix III --- p.18

A natural language based indexing technique for Chinese information retrieval.

Pang Chun Kiu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 101-107).Chapter 1 --- Introduction --- p.2Chapter 1.1 --- Chinese Indexing using Noun Phrases --- p.6Chapter 1.2 --- Objectives --- p.8Chapter 1.3 --- An Overview of the Thesis --- p.8Chapter 2 --- Background --- p.10Chapter 2.1 --- Technology Influences on Information Retrieval --- p.10Chapter 2.2 --- Related Work --- p.13Chapter 2.2.1 --- Statistical/Keyword Approaches --- p.13Chapter 2.2.2 --- Syntactical approaches --- p.15Chapter 2.2.3 --- Semantic approaches --- p.17Chapter 2.2.4 --- Noun Phrases Approach --- p.18Chapter 2.2.5 --- Chinese Information Retrieval --- p.20Chapter 2.3 --- Our Approach --- p.21Chapter 3 --- Chinese Noun Phrases --- p.23Chapter 3.1 --- Different types of Chinese Noun Phrases --- p.23Chapter 3.2 --- Ambiguous noun phrases --- p.27Chapter 3.2.1 --- Ambiguous English Noun Phrases --- p.27Chapter 3.2.2 --- Ambiguous Chinese Noun Phrases --- p.28Chapter 3.2.3 --- Statistical data on the three NPs --- p.33Chapter 4 --- Index Extraction from De-de Conj. NP --- p.35Chapter 4.1 --- Word Segmentation --- p.36Chapter 4.2 --- Part-of-speech tagging --- p.37Chapter 4.3 --- Noun Phrase Extraction --- p.37Chapter 4.4 --- The Chinese noun phrase partial parser --- p.38Chapter 4.5 --- Handling Parsing Ambiguity --- p.40Chapter 4.6 --- Index Building Strategy --- p.41Chapter 4.7 --- The cross-set generation rules --- p.44Chapter 4.8 --- Example 1: Indexing De-de NP --- p.46Chapter 4.9 --- Example 2: Indexing Conjunctive NP --- p.48Chapter 4.10 --- Experimental results and Discussion --- p.49Chapter 5 --- Indexing Compound Nouns --- p.52Chapter 5.1 --- Previous Researches on Compound Nouns --- p.53Chapter 5.2 --- Indexing two-term Compound Nouns --- p.55Chapter 5.2.1 --- About the thesaurus《同義詞詞林》 --- p.56Chapter 5.3 --- Indexing Compound Nouns of three or more terms --- p.58Chapter 5.4 --- Corpus learning approach --- p.59Chapter 5.4.1 --- An Example --- p.60Chapter 5.4.2 --- Experimental Setup --- p.63Chapter 5.4.3 --- An Experiment using the third level of the Cilin --- p.65Chapter 5.4.4 --- An Experiment using the second level of the Cilin --- p.66Chapter 5.5 --- Contextual Approach --- p.68Chapter 5.5.1 --- The algorithm --- p.69Chapter 5.5.2 --- An Illustrative Example --- p.71Chapter 5.5.3 --- Experiments on compound nouns --- p.72Chapter 5.5.4 --- Experiment I: Word Distance Based Extraction --- p.73Chapter 5.5.5 --- Experiment II: Semantic Class Based Extraction --- p.75Chapter 5.5.6 --- Experiments III: On different boundaries --- p.76Chapter 5.5.7 --- The Final Algorithm --- p.79Chapter 5.5.8 --- Experiments on other compounds --- p.82Chapter 5.5.9 --- Discussion --- p.83Chapter 6 --- Overall Effectiveness --- p.85Chapter 6.1 --- Illustrative Example for the Integrated Algorithm --- p.86Chapter 6.2 --- Experimental Setup --- p.90Chapter 6.3 --- Experimental Results & Discussion --- p.91Chapter 7 --- Conclusion --- p.95Chapter 7.1 --- Summary --- p.95Chapter 7.2 --- Contributions --- p.97Chapter 7.3 --- Future Directions --- p.98Chapter 7.3.1 --- Word-sense determination --- p.98Chapter 7.3.2 --- Hybrid approach for compound noun indexing --- p.99Chapter A --- Cross-set Generation Rules --- p.108Chapter B --- Tag set by Tsinghua University --- p.110Chapter C --- Noun Phrases Test Set --- p.113Chapter D --- Compound Nouns Test Set --- p.124Chapter D.l --- Three-term Compound Nouns --- p.125Chapter D.1.1 --- NVN --- p.125Chapter D.1.2 --- Other three-term compound nouns --- p.129Chapter D.2 --- Four-term Compound Nouns --- p.133Chapter D.3 --- Five-term and six-term Compound Nouns --- p.13

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

Peer reviewe

A Study of Chinese Named Entity and Relation Identification in a Specific Domain

This thesis aims at investigating automatic identification of Chinese named entities (NEs) and their relations (NERs) in a specific domain. We have proposed a three-stage pipeline computational model for the error correction of word segmentation and POS tagging, NE recognition and NER identification. In this model, an error repair module utilizing machine learning techniques is developed in the first stage. At the second stage, a new algorithm that can automatically construct Finite State Cascades (FSC) from given sets of rules is designed. As a supplement, the recognition strategy without NE trigger words can identify the special linguistic phenomena. In the third stage, a novel approach - positive and negative case-based learning and identification (PNCBL&I) is implemented. It pursues the improvement of the identification performance for NERs through simultaneously learning two opposite cases and automatically selecting effective multi-level linguistic features for NERs and non-NERs. Further, two other strategies, resolving relation conflicts and inferring missing relations, are also integrated in the identification procedure.Diese Dissertation ist der Forschung zur automatischen Erkennung von chinesischen Begriffen (named entities, NE) und ihrer Relationen (NER) in einer spezifischen Domäne gewidmet. Wir haben ein Pipelinemodell mit drei aufeinanderfolgenden Verarbeitungsschritten für die Korrektur der Fehler der Wortsegmentation und Wortartmarkierung, NE-Erkennung, und NER-Identifizierung vorgeschlagen. In diesem Modell wird eine Komponente zur Fehlerreparatur im ersten Verarbeitungsschritt verwirklicht, die ein machinelles Lernverfahren einsetzt. Im zweiten Stadium wird ein neuer Algorithmus, der die Kaskaden endlicher Transduktoren aus den Mengen der Regeln automatisch konstruieren kann, entworfen. Zusätzlich kann eine Strategie für die Erkennung von NE, die nicht durch das Vorkommen bestimmer lexikalischer Trigger markiert sind, die spezielle linguistische Phänomene identifizieren. Im dritten Verarbeitungsschritt wird ein neues Verfahren, das auf dem Lernen und der Identifizierung positiver und negativer Fälle beruht, implementiert. Es verfolgt die Verbesserung der NER-Erkennungsleistung durch das gleichzeitige Lernen zweier gegenüberliegenden Fälle und die automatische Auswahl der wirkungsvollen linguistischen Merkmale auf mehreren Ebenen für die NER und Nicht-NER. Weiter werden zwei andere Strategien, die Lösung von Konflikten in der Relationenerkennung und die Inferenz von fehlenden Relationen, auch in den Erkennungsprozeß integriert

A Study of Chinese Named Entity and Relation Identification in a Specific Domain

This thesis aims at investigating automatic identification of Chinese named entities (NEs) and their relations (NERs) in a specific domain. We have proposed a three-stage pipeline computational model for the error correction of word segmentation and POS tagging, NE recognition and NER identification. In this model, an error repair module utilizing machine learning techniques is developed in the first stage. At the second stage, a new algorithm that can automatically construct Finite State Cascades (FSC) from given sets of rules is designed. As a supplement, the recognition strategy without NE trigger words can identify the special linguistic phenomena. In the third stage, a novel approach - positive and negative case-based learning and identification (PNCBL&I) is implemented. It pursues the improvement of the identification performance for NERs through simultaneously learning two opposite cases and automatically selecting effective multi-level linguistic features for NERs and non-NERs. Further, two other strategies, resolving relation conflicts and inferring missing relations, are also integrated in the identification procedure.Diese Dissertation ist der Forschung zur automatischen Erkennung von chinesischen Begriffen (named entities, NE) und ihrer Relationen (NER) in einer spezifischen Domäne gewidmet. Wir haben ein Pipelinemodell mit drei aufeinanderfolgenden Verarbeitungsschritten für die Korrektur der Fehler der Wortsegmentation und Wortartmarkierung, NE-Erkennung, und NER-Identifizierung vorgeschlagen. In diesem Modell wird eine Komponente zur Fehlerreparatur im ersten Verarbeitungsschritt verwirklicht, die ein machinelles Lernverfahren einsetzt. Im zweiten Stadium wird ein neuer Algorithmus, der die Kaskaden endlicher Transduktoren aus den Mengen der Regeln automatisch konstruieren kann, entworfen. Zusätzlich kann eine Strategie für die Erkennung von NE, die nicht durch das Vorkommen bestimmer lexikalischer Trigger markiert sind, die spezielle linguistische Phänomene identifizieren. Im dritten Verarbeitungsschritt wird ein neues Verfahren, das auf dem Lernen und der Identifizierung positiver und negativer Fälle beruht, implementiert. Es verfolgt die Verbesserung der NER-Erkennungsleistung durch das gleichzeitige Lernen zweier gegenüberliegenden Fälle und die automatische Auswahl der wirkungsvollen linguistischen Merkmale auf mehreren Ebenen für die NER und Nicht-NER. Weiter werden zwei andere Strategien, die Lösung von Konflikten in der Relationenerkennung und die Inferenz von fehlenden Relationen, auch in den Erkennungsprozeß integriert

Frame semantics for the field of climate change : d iscovering frames based on chinese and english terms

La plupart des dictionnaires spécialisés de termes environnementaux en mandarin sont des dictionnaires papier, compilés et révisés il y a plus de dix ans, et contiennent principalement des termes nominaux. Les informations terminologiques se limitent aux connaissances véhiculées par le terme et son ou ses équivalents anglais. Pour les lecteurs qui souhaitent connaître les propriétés sémantiques ou syntaxiques des termes et pour les lecteurs qui veulent voir l’usage des termes dans des contextes réels de textes spécialisés, les informations fournies par les dictionnaires existants sont insuffisantes. Dans cette recherche, nous avons compilé une ressource terminologique en ligne du mandarin, décrivant les termes verbaux chinois dans le domaine du changement climatique. Cette ressource comble certaines des lacunes des dictionnaires environnementaux mandarin existants, en révélant le(s) sens du terme à travers la(les) structure(s) actantielle(s) et en montrant, à travers des contextes annotés, les propriétés sémantiques et syntaxiques du terme ainsi que ses usages pratiques dans des textes spécialisés. Cette ressource répondra mieux aux besoins du public. La base théorique qui sous-tend cette recherche est la Sémantique des cadres (Fillmore, 1976, 1977, 1982, 1985; Fillmore & Atkins, 1992), et le FrameNet construit à partir de celle-ci. L’objectif principal de cette recherche est de découvrir et de définir des cadres sémantiques chinois dans le domaine du changement climatique, et d’établir des relations entre les cadres chinois définis. Les cadres sémantiques chinois sont découverts à l’aide de la méthodologie du dictionnaire environnemental multilingue DiCoEnviro (et de sa ressource d’accompagnement Framed DiCoEnviro) (L’Homme, 2018; L’Homme et al., 2020). Afin de rendre cette méthodologie applicable à une langue sino-tibétaine, le chinois, nous avons modifié et adapté cette méthodologie pour qu’elle convienne à la description des termes chinois et à la définition des cadres sémantiques chinois. Certaines de ces modifications et adaptations sont basées sur le Chinese FrameNet (CFN) (Liu & You, 2015). Afin de découvrir les cadres sémantiques chinois, un corpus monolingue en chinois mandarin sur le changement climatique (MCCC) a d’abord été compilé. Ce corpus contient 224 textes iv authentiques chinois spécialisés dans le domaine du changement climatique, qui totalisent 1,228,333 caractères chinois, soit 547,592 mots chinois. Puis, les termes candidats ont été automatiquement extraits du MCCC à l’aide du logiciel de gestion et d’analyse de corpus – Sketch Engine. Après une analyse et une validation manuelle, nous avons déterminé quels termes candidats sont des termes réels. Par la suite, la structure actancielle de chaque terme a été écrite en analysant les contextes où le terme apparaît. Ensuite, chaque sens d’un terme polysémique a été placé dans une entrée séparée et 16-20 contextes ont été sélectionnés pour chaque entrée. Puis, chaque contexte a été annoté en fonction de trois couches – structure sémantique, fonction syntaxique et groupe syntaxique. Ensuite, les termes ont été classés en fonction des scénarios qu’ils évoquent. Les termes qui dépeignent la même scène ou situation dans le domaine du changement climatique, qui ont une structure actantielle similaire et qui partagent la majorité des circonstants sont classés dans un seul cadre sémantique (critères basés sur le projet DiCoEnviro (L’Homme, 2018; L’Homme et al., 2020)). Après avoir identifié les cadres sémantiques chinois, chaque cadre a été défini. Enfin, les cadres chinois découverts ont été reliés selon les huit types de relations entre cadres proposés par Ruppenhofer et al. (2016). Pour être affichés en ligne, les entrées de termes et les cadres sémantiques ont été encodés dans des fichiers XML. Guidés par cette méthodologie de recherche, nous avons finalement relevé 23 cadres sémantiques chinois et nous les avons définis. Le résultat final de cette recherche est une ressource terminologique en chinois mandarin basée sur des cadres et spécialisée dans le domaine du changement climatique. Cette ressource terminologique se compose de deux parties. La première partie est la description d’un total de 39 termes verbaux chinois. Chaque sens d’un terme verbal polysémique étant placé dans une entrée séparée, il y a au total 59 entrées (chaque entrée contient la structure actantielle et les contextes annotés). Au total, 1,027 contextes ont été annotés. La deuxième partie de cette ressource présente les 23 cadres sémantiques chinois identifiés ainsi que les relations entre les cadres.Most of the existing Mandarin Chinese specialised dictionaries of environmental terms are paper dictionaries, compiled and revised more than ten years ago, and contain mainly noun terms. Terminological information is restricted to knowledge conveyed by the term and its English equivalent(s). For readers who want to learn about semantic or syntactic properties of terms and for readers who want to see usage of terms in real contexts of specialised texts, information provided in existing dictionaries is insufficient. In this research, we compiled an online Mandarin Chinese terminological resource, describing Chinese verb terms in the field of climate change. This resource makes up for some of the deficiencies of existing Chinese environmental dictionaries, revealing meaning(s) of the term through actantial structure(s) and showing, through annotated contexts, semantic and syntactic properties of the term as well as its practical usages in specialised texts. This resource better meets the needs of the audience. The theoretical basis underpinning this research is Frame Semantics (Fillmore, 1976, 1977, 1982, 1985; Fillmore & Atkins, 1992), and the FrameNet built from it. The main objective of this research is to discover and define Chinese semantic frames in the field of climate change, and to establish relations between the Chinese frames defined. The Chinese semantic frames are discovered with the help of the methodology of the multilingual environmental dictionary DiCoEnviro (and its accompanying resource Framed DiCoEnviro) (L’Homme, 2018; L’Homme et al., 2020). In order to make this methodology applicable to a Sino-Tibetan language, Chinese, we modified and adapted this methodology to suit the description of Chinese terms and definition of Chinese semantic frames. Some of the changes and adaptations are based on the Chinese FrameNet (CFN) (Liu & You, 2015). In order to discover Chinese semantic frames, a monolingual Mandarin (Chinese) Climate Change Corpus (MCCC) was first compiled. This corpus contains 224 authentic Chinese specialised texts in the field of climate change, totaling 1,228,333 Chinese characters, which is 547,592 Chinese words. Following this, candidate terms were automatically extracted from MCCC using the corpus ii management and analysing software – Sketch Engine. After manual analysis and validation, which of the candidate terms are true terms was clarified. Subsequently, the actantial structure of each term was written by analysing the contexts where the term occurs. Next, each sense of a polysemous term was placed in a separate entry and 16-20 contexts were selected for each entry. Then, each context was annotated in terms of three layers – semantic structure, syntactic function and syntactic group. After this, the terms were classified according to the scenarios they evoke. Terms that depict the same scene or situation in the field of climate change, have similar actantial structure, and share the majority of circumstants are categorised into one semantic frame (criteria based on the project DiCoEnviro (L’Homme, 2018; L’Homme et al., 2020)). After Chinese semantic frames were identified, each frame was defined. Finally, the discovered Chinese frames were linked according to the eight types of frame relations proposed by Ruppenhofer et al. (2016). To be displayed online, term entries and semantic frames were encoded in XML files. Guided by this research methodology, we eventually discovered and defined 23 Chinese semantic frames. The end result of this research is a frame-based Mandarin Chinese terminological resource specialised in the field of climate change. This terminological resource consists of two parts. The first part is the description of a total of 39 Chinese verb terms. With each meaning of a polysemous verb term placed in a separate entry, there are a total of 59 entries (each entry contains the actantial structure and annotated contexts). A total of 1,027 contexts were annotated. The second part of this resource presents the 23 Chinese semantic frames identified as well as the relations between frames

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

Lexical and Grammar Resource Engineering for Runyankore & Rukiga: A Symbolic Approach

Current research in computational linguistics and natural language processing (NLP) requires the existence of language resources. Whereas these resources are available for a few well-resourced languages, there are many languages that have been neglected. Among the neglected and / or under-resourced languages are Runyankore and Rukiga (henceforth referred to as Ry/Rk). Recently, the NLP community has started to acknowledge that resources for under-resourced languages should also be given priority. Why? One reason being that as far as language typology is concerned, the few well-resourced languages do not represent the structural diversity of the remaining languages. The central focus of this thesis is about enabling the computational analysis and generation of utterances in Ry/Rk. Ry/Rk are two closely related languages spoken by about 3.4 and 2.4 million people respectively. They belong to the Nyoro-Ganda (JE10) language zone of the Great Lakes, Narrow Bantu of the Niger-Congo language family.The computational processing of these languages is achieved by formalising the grammars of these two languages using Grammatical Framework (GF) and its Resource Grammar Library (RGL). In addition to the grammar, a general-purpose computational lexicon for the two languages is developed. Although we utilise the lexicon to tremendously increase the lexical coverage of the grammars, the lexicon can be used for other NLP tasks.In this thesis a symbolic / rule-based approach is taken because the lack of adequate languages resources makes the use of data-driven NLP approaches unsuitable for these languages