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

Proceedings of the COLING 2004 Post Conference Workshop on Multilingual Linguistic Ressources MLR2004

International audienceIn an ever expanding information society, most information systems are now facing the "multilingual challenge". Multilingual language resources play an essential role in modern information systems. Such resources need to provide information on many languages in a common framework and should be (re)usable in many applications (for automatic or human use). Many centres have been involved in national and international projects dedicated to building har- monised language resources and creating expertise in the maintenance and further development of standardised linguistic data. These resources include dictionaries, lexicons, thesauri, word-nets, and annotated corpora developed along the lines of best practices and recommendations. However, since the late 90's, most efforts in scaling up these resources remain the responsibility of the local authorities, usually, with very low funding (if any) and few opportunities for academic recognition of this work. Hence, it is not surprising that many of the resource holders and developers have become reluctant to give free access to the latest versions of their resources, and their actual status is therefore currently rather unclear. The goal of this workshop is to study problems involved in the development, management and reuse of lexical resources in a multilingual context. Moreover, this workshop provides a forum for reviewing the present state of language resources. The workshop is meant to bring to the international community qualitative and quantitative information about the most recent developments in the area of linguistic resources and their use in applications. The impressive number of submissions (38) to this workshop and in other workshops and conferences dedicated to similar topics proves that dealing with multilingual linguistic ressources has become a very hot problem in the Natural Language Processing community. To cope with the number of submissions, the workshop organising committee decided to accept 16 papers from 10 countries based on the reviewers' recommendations. Six of these papers will be presented in a poster session. The papers constitute a representative selection of current trends in research on Multilingual Language Resources, such as multilingual aligned corpora, bilingual and multilingual lexicons, and multilingual speech resources. The papers also represent a characteristic set of approaches to the development of multilingual language resources, such as automatic extraction of information from corpora, combination and re-use of existing resources, online collaborative development of multilingual lexicons, and use of the Web as a multilingual language resource. The development and management of multilingual language resources is a long-term activity in which collaboration among researchers is essential. We hope that this workshop will gather many researchers involved in such developments and will give them the opportunity to discuss, exchange, compare their approaches and strengthen their collaborations in the field. The organisation of this workshop would have been impossible without the hard work of the program committee who managed to provide accurate reviews on time, on a rather tight schedule. We would also like to thank the Coling 2004 organising committee that made this workshop possible. Finally, we hope that this workshop will yield fruitful results for all participants

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

Specification of knowledge acquisition and modeling of the process of the consensus

zhdanova2004aIn this deliverable, specification of knowledge acquisition and modeling of the process of consensus is provided

Semi-Supervised Named Entity Recognition:\ud Learning to Recognize 100 Entity Types with Little Supervision\ud

Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as proper names, biological species, and temporal expressions. There has been growing interest in this field of research since the early 1990s. In this thesis, we document a trend moving away from handcrafted rules, and towards machine learning approaches. Still, recent machine learning approaches have a problem with annotated data availability, which is a serious shortcoming in building and maintaining large-scale NER systems. \ud \ud In this thesis, we present an NER system built with very little supervision. Human supervision is indeed limited to listing a few examples of each named entity (NE) type. First, we introduce a proof-of-concept semi-supervised system that can recognize four NE types. Then, we expand its capacities by improving key technologies, and we apply the system to an entire hierarchy comprised of 100 NE types. \ud \ud Our work makes the following contributions: the creation of a proof-of-concept semi-supervised NER system; the demonstration of an innovative noise filtering technique for generating NE lists; the validation of a strategy for learning disambiguation rules using automatically identified, unambiguous NEs; and finally, the development of an acronym detection algorithm, thus solving a rare but very difficult problem in alias resolution. \ud \ud We believe semi-supervised learning techniques are about to break new ground in the machine learning community. In this thesis, we show that limited supervision can build complete NER systems. On standard evaluation corpora, we report performances that compare to baseline supervised systems in the task of annotating NEs in texts. \u

Novelty detection in video retrieval: finding new news in TV news stories

Novelty detection is defined as the detection of documents that provide "new" or previously unseen information. "New information" in a search result list is defined as the incremental information found in a document based on what the user has already learned from reviewing previous documents in a given ranked list of documents. It is assumed that, as a user views a list of documents, their information need changes or evolves, and their state of knowledge increases as they gain new information from the documents they see. The automatic detection of "novelty" , or newness, as part of an information retrieval system could greatly improve a searcher’s experience by presenting "documents" in order of how much extra information they add to what is already known, instead of how similar they are to a user’s query. This could be particularly useful in applications such as the search of broadcast news and automatic summary generation. There are many different aspects of information management, however, this thesis, presents research into the area of novelty detection within the content based video domain. It explores the benefits of integrating the many multi modal resources associated with video content those of low level feature detection evidences such as colour and edge, automatic concepts detections such as face, commercials, and anchor person, automatic speech recognition transcripts and manually annotated MPEG7 concepts into a novelty detection model. The effectiveness of this novelty detection model is evaluated on a collection of TV new data

Bootstrapping named entity resources for adaptive question answering systems

Los Sistemas de Búsqueda de Respuestas (SBR) amplían las capacidades de un buscador de información tradicional con la capacidad de encontrar respuestas precisas a las preguntas del usuario. El objetivo principal es facilitar el acceso a la información y disminuir el tiempo y el esfuerzo que el usuario debe emplear para encontrar una información concreta en una lista de documentos relevantes. En esta investigación se han abordado dos trabajos relacionados con los SBR. La primera parte presenta una arquitectura para SBR en castellano basada en la combinación y adaptación de diferentes técnicas de Recuperación y de Extracción de Información. Esta arquitectura está integrada por tres módulos principales que incluyen el análisis de la pregunta, la recuperación de pasajes relevantes y la extracción y selección de respuestas. En ella se ha prestado especial atención al tratamiento de las Entidades Nombradas puesto que, con frecuencia, son el tema de las preguntas o son buenas candidatas como respuestas. La propuesta se ha encarnado en el SBR del grupo MIRACLE que ha sido evaluado de forma independiente durante varias ediciones en la tarea compartida CLEF@QA, parte del foro de evaluación competitiva Cross-Language Evaluation Forum (CLEF). Se describen aquí las participaciones y los resultados obtenidos entre 2004 y 2007. El SBR de MIRACLE ha obtenido resultados moderados en el desempeño de la tarea con tasas de respuestas correctas entre el 20% y el 30%. Entre los resultados obtenidos destacan los de la tarea principal de 2005 y la tarea piloto de Búsqueda de Respuestas en tiempo real de 2006, RealTimeQA. Esta última tarea, además de requerir respuestas correctas incluía el tiempo de respuesta como un factor adicional en la evaluación. Estos resultados respaldan la validez de la arquitectura propuesta como una alternativa viable para los SBR sobre colecciones textuales y también corrobora resultados similares para el inglés y otras lenguas. Por otro lado, el análisis de los resultados a lo largo de las diferentes ediciones de CLEF así como la comparación con otros SBR apunta nuevos problemas y retos. Según nuestra experiencia, los sistemas de QA son más complicados de adaptar a otros dominios y lenguas que los sistemas de Recuperación de Información. Este problema viene heredado del uso de herramientas complejas de análisis de lenguaje como analizadores morfológicos, sintácticos y semánticos. Entre estos últimos se cuentan las herramientas para el Reconocimiento y Clasificación de Entidades Nombradas (NERC en inglés) así como para la Detección y Clasificación de Relaciones (RDC en inglés). Debido a la di cultad de adaptación del SBR a distintos dominios y colecciones, en la segunda parte de esta tesis se investiga una propuesta diferente basada en la adquisición de conocimiento mediante métodos de aprendizaje ligeramente supervisado. El objetivo de esta investigación es adquirir recursos semánticos útiles para las tareas de NERC y RDC usando colecciones de textos no anotados. Además, se trata de eliminar la dependencia de herramientas de análisis lingüístico con el fin de facilitar que las técnicas sean portables a diferentes dominios e idiomas. En primer lugar, se ha realizado un estudio de diferentes algoritmos para NERC y RDC de forma semisupervisada a partir de unos pocos ejemplos (bootstrapping). Este trabajo propone primero una arquitectura común y compara diferentes funciones que se han usado en la evaluación y selección de resultados intermedios, tanto instancias como patrones. La principal propuesta es un nuevo algoritmo que permite la adquisición simultánea e iterativa de instancias y patrones asociados a una relación. Incluye también la posibilidad de adquirir varias relaciones de forma simultánea y mediante el uso de la hipótesis de exclusividad obtener mejores resultados. Como característica distintiva el algoritmo explora la colección de textos con una estrategia basada en indización, que permite adquirir conocimiento de grandes colecciones. La estrategia de selección de candidatos y la evaluación se basan en la construcción de un grafo de instancias y patrones, que justifica nuestro método para la selección de candidatos. Este procedimiento es semejante al frente de exploración de una araña web y permite encontrar las instancias más parecidas a las semillas con las evidencias disponibles. Este algoritmo se ha implementado en el sistema SPINDEL y para su evaluación se ha comenzado con el caso concreto de la adquisición de recursos para las clases de Entidades Nombradas más comunes, Persona, Lugar y Organización. El objetivo es adquirir nombres asociados a cada una de las categorías así como patrones contextuales que permitan detectar menciones asociadas a una clase. Se presentan resultados para la adquisición de dos idiomas distintos, castellano e inglés, y para el castellano, en dos dominios diferentes, noticias y textos de una enciclopedia colaborativa, Wikipedia. En ambos casos el uso de herramientas de análisis lingüístico se ha limitado de acuerdo con el objetivo de avanzar hacia la independencia de idioma. Las listas adquiridas mediante bootstrapping parten de menos de 40 semillas por clase y obtienen del orden de 30.000 instancias de calidad variable. Además se obtienen listas de patrones indicativos asociados a cada clase de entidad. La evaluación indirecta confirma la utilidad de ambos recursos en la clasificación de Entidades Nombradas usando un enfoque simple basado únicamente en diccionarios. La mejor configuración obtiene para la clasificación en castellano una medida F de 67,17 y para inglés de 55,99. Además se confirma la utilidad de los patrones adquiridos que en ambos casos ayudan a mejorar la cobertura. El módulo requiere menor esfuerzo de desarrollo que los enfoques supervisados, si incluimos la necesidad de anotación, aunque su rendimiento es inferior por el momento. En definitiva, esta investigación constituye un primer paso hacia el desarrollo de aplicaciones semánticas como los SBR que requieran menos esfuerzo de adaptación a un dominio o lenguaje nuevo.-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Question Answering (QA) systems add new capabilities to traditional search engines with the ability to find precise answers to user questions. Their objective is to enable easier information access by reducing the time and effort that the user requires to find a concrete information among a list of relevant documents. In this thesis we have carried out two works related with QA systems. The first part introduces an architecture for QA systems for Spanish which is based on the combination and adaptation of different techniques from Information Retrieval (IR) and Information Extraction (IE). This architecture is composed by three modules that include question analysis, relevant passage retrieval and answer extraction and selection. The appropriate processing of Named Entities (NE) has received special attention because of their importance as question themes and candidate answers. The proposed architecture has been implemented as part of the MIRACLE QA system. This system has taken part in independent evaluations like the CLEF@QA track in the Cross-Language Evaluation Forum (CLEF). Results from 2004 to 2007 campaigns as well as the details and the evolution of the system have been described in deep. The MIRACLE QA system has obtained moderate performance with a first answer accuracy ranging between 20% and 30%. Nevertheless, it is important to highlight the results obtained in the 2005 main QA task and the RealTimeQA pilot task in 2006. The last one included response time as an important additional variable of the evaluation. These results back the proposed architecture as an option for QA from textual collection and confirm similar findings obtained for English and other languages. On the other hand, the analysis of the results along evaluation campaigns and the comparison with other QA systems point problems with current systems and new challenges. According to our experience, it is more dificult to tailor QA systems to different domains and languages than IR systems. The problem is inherited by the use of complex language analysis tools like POS taggers, parsers and other semantic analyzers, like NE Recognition and Classification (NERC) and Relation Detection and Characterization (RDC) tools. The second part of this thesis tackles this problem and proposes a different approach to adapting QA systems for di erent languages and collections. The proposal focuses on acquiring knowledge for the semantic analyzers based on lightly supervised approaches. The goal is to obtain useful resources that help to perform NERC or RDC using as few annotated resources as possible. Besides, we try to avoid dependencies from other language analysis tools with the purpose that these methods apply to different languages and domains. First of all, we have study previous work on building NERC and RDC modules with few supervision, particularly bootstrapping methods. We propose a common framework for different bootstrapping systems that help to unify different evaluation functions for intermediate results. The main proposal is a new algorithm that is able to simultaneously acquire instances and patterns associated to a relation of interest. It also uses mutual exclusion among relations to reduce concept drift and achieve better results. A distinctive characteristic is that it uses a query based exploration strategy of the text collection which enables their use for larger collections. Candidate selection and evaluation are based on incrementally building a graph of instances and patterns which also justifies our evaluation function. The discovery approach is analogous to the front of exploration in a web crawler and it is able to find the most similar instances to the available seeds. This algorithm has been implemented in the SPINDEL system. We have selected for evaluation the task of acquiring resources for the most common NE classes, Person, Location and Organization. The objective is to acquire name instances that belong to any of the classes as well as contextual patterns that help to detect mentions of NE that belong to that class. We present results for the acquisition of resources from raw text from two different languages, Spanish and English. We also performed experiments for Spanish in two different collections, news and texts from a collaborative encyclopedia, Wikipedia. Both cases are tackled with limited language analysis tools and resources. With an initial list of 40 instance seeds, the bootstrapping process is able to acquire large name lists containing up to 30.000 instances with a variable quality. Besides, large lists of indicative patterns are obtained too. Our indirect evaluation confirms the utility of both resources to classify NE using a simple dictionary recognition approach. Best results for Spanish obtained a F-score of 67,17 and for English this value is 55,99. The module requires much less development effort than annotation for supervised algorithms although the performance is not in pair yet. This research is a first step towards the development of semantic applications like QA for a new language or domain with no annotated corpora that requires less adaptation effort

Wiktionary: The Metalexicographic and the Natural Language Processing Perspective

Dictionaries are the main reference works for our understanding of language. They are used by humans and likewise by computational methods. So far, the compilation of dictionaries has almost exclusively been the profession of expert lexicographers. The ease of collaboration on the Web and the rising initiatives of collecting open-licensed knowledge, such as in Wikipedia, caused a new type of dictionary that is voluntarily created by large communities of Web users. This collaborative construction approach presents a new paradigm for lexicography that poses new research questions to dictionary research on the one hand and provides a very valuable knowledge source for natural language processing applications on the other hand. The subject of our research is Wiktionary, which is currently the largest collaboratively constructed dictionary project. In the first part of this thesis, we study Wiktionary from the metalexicographic perspective. Metalexicography is the scientific study of lexicography including the analysis and criticism of dictionaries and lexicographic processes. To this end, we discuss three contributions related to this area of research: (i) We first provide a detailed analysis of Wiktionary and its various language editions and dictionary structures. (ii) We then analyze the collaborative construction process of Wiktionary. Our results show that the traditional phases of the lexicographic process do not apply well to Wiktionary, which is why we propose a novel process description that is based on the frequent and continual revision and discussion of the dictionary articles and the lexicographic instructions. (iii) We perform a large-scale quantitative comparison of Wiktionary and a number of other dictionaries regarding the covered languages, lexical entries, word senses, pragmatic labels, lexical relations, and translations. We conclude the metalexicographic perspective by finding that the collaborative Wiktionary is not an appropriate replacement for expert-built dictionaries due to its inconsistencies, quality flaws, one-fits-all-approach, and strong dependence on expert-built dictionaries. However, Wiktionary's rapid and continual growth, its high coverage of languages, newly coined words, domain-specific vocabulary and non-standard language varieties, as well as the kind of evidence based on the authors' intuition provide promising opportunities for both lexicography and natural language processing. In particular, we find that Wiktionary and expert-built wordnets and thesauri contain largely complementary entries. In the second part of the thesis, we study Wiktionary from the natural language processing perspective with the aim of making available its linguistic knowledge for computational applications. Such applications require vast amounts of structured data with high quality. Expert-built resources have been found to suffer from insufficient coverage and high construction and maintenance cost, whereas fully automatic extraction from corpora or the Web often yields resources of limited quality. Collaboratively built encyclopedias present a viable solution, but do not cover well linguistically oriented knowledge as it is found in dictionaries. That is why we propose extracting linguistic knowledge from Wiktionary, which we achieve by the following three main contributions: (i) We propose the novel multilingual ontology OntoWiktionary that is created by extracting and harmonizing the weakly structured dictionary articles in Wiktionary. A particular challenge in this process is the ambiguity of semantic relations and translations, which we resolve by automatic word sense disambiguation methods. (ii) We automatically align Wiktionary with WordNet 3.0 at the word sense level. The largely complementary information from the two dictionaries yields an aligned resource with higher coverage and an enriched representation of word senses. (iii) We represent Wiktionary according to the ISO standard Lexical Markup Framework, which we adapt to the peculiarities of collaborative dictionaries. This standardized representation is of great importance for fostering the interoperability of resources and hence the dissemination of Wiktionary-based research. To this end, our work presents a foundational step towards the large-scale integrated resource UBY, which facilitates a unified access to a number of standardized dictionaries by means of a shared web interface for human users and an application programming interface for natural language processing applications. A user can, in particular, switch between and combine information from Wiktionary and other dictionaries without completely changing the software. Our final resource and the accompanying datasets and software are publicly available and can be employed for multiple different natural language processing applications. It particularly fills the gap between the small expert-built wordnets and the large amount of encyclopedic knowledge from Wikipedia. We provide a survey of previous works utilizing Wiktionary, and we exemplify the usefulness of our work in two case studies on measuring verb similarity and detecting cross-lingual marketing blunders, which make use of our Wiktionary-based resource and the results of our metalexicographic study. We conclude the thesis by emphasizing the usefulness of collaborative dictionaries when being combined with expert-built resources, which bears much unused potential