From Atoms to the Solar System: Generating Lexical Analogies from Text

A lexical analogy is two pairs of words (w1, w2) and (w3, w4) such that the relation between w1 and w2 is identical or similar to the relation between w3 and w4. For example, (abbreviation, word) forms a lexical analogy with (abstract, report), because in both cases the former is a shortened version of the latter. Lexical analogies are of theoretic interest because they represent a second order similarity measure: relational similarity. Lexical analogies are also of practical importance in many applications, including text-understanding and learning ontological relations. This thesis presents a novel system that generates lexical analogies from a corpus of text documents. The system is motivated by a well-established theory of analogy-making, and views lexical analogy generation as a series of three processes: identifying pairs of words that are semantically related, finding clues to characterize their relations, and generating lexical analogies by matching pairs of words with similar relations. The system uses a dependency grammar to characterize semantic relations, and applies machine learning techniques to determine their similarities. Empirical evaluation shows that the system performs remarkably well, generating lexical analogies at a precision of over 90%

Positive and Negative Sentiment Words in a Blog Corpus Written in Hebrew

AbstractIn this research, given a corpus containing blog posts written in Hebrew and two seed sentiment lists, we analyze the positive and negative sentences included in the corpus, and special groups of words that are associated with the positive and negative seed words. We discovered many new negative words (around half of the top 50 words) but only one positive word. Among the top words that are associated with the positive seed words, we discovered various first-person and third-person pronouns. Intensifiers were found for both the positive and negative seed words. Most of the corpus’ sentences are neutral. For the rest, the rate of positive sentences is above 80%. The sentiment scores of the top words that are associated with the positive words are significantly higher than those of the top words that are associated with the negative words.Our conclusions are as follows. Positive sentences more “refer to” the authors themselves (first-person pronouns and related words) and are also more general, e.g., more related to other people (third-person pronouns), while negative sentences are much more concentrated on negative things and therefore contain many new negative words. Israeli bloggers tend to use intensifiers in order to emphasize or even exaggerate their sentiment opinions (both positive and negative). These bloggers not only write much more positive sentences than negative sentences, but also write much longer positive sentences than negative sentences

Process Improvement of LSA for Semantic Relatedness Computing

Tang poetry semantic correlation computing is critical in many applications, such as searching, clustering, automatic generation of poetry and so on. Aiming to increase computing efficiency and accuracy of semantic relatedness, we improved the process of latent semantic analysis (LSA). In this paper, we adopted “representation of words semantic” instead of “words-by-poems” to represent the words semantic, which based on the finding that words having similar distribution in poetry categories are almost always semantically related. Meanwhile, we designed experiment which obtained segmentation words from more than 40000 poems, and computed relatedness by cosine value which calculated from decomposed co-occurrence matrix with Singular Value Decomposition (SVD) method. The experimental result shows that this method is good to analyze semantic and emotional relatedness of words in Tang poetry. We can find associated words and the relevance of poetry categories by matrix manipulation of the decomposing matrices as well

Ontology Localization

Nuestra meta principal en esta tesis es proponer una solución para construir una ontología multilingüe, a través de la localización automática de una ontología. La noción de localización viene del área de Desarrollo de Software que hace referencia a la adaptación de un producto de software a un ambiente no nativo. En la Ingeniería Ontológica, la localización de ontologías podría ser considerada como un subtipo de la localización de software en el cual el producto es un modelo compartido de un dominio particular, por ejemplo, una ontología, a ser usada por una cierta aplicación. En concreto, nuestro trabajo introduce una nueva propuesta para el problema de multilingüismo, describiendo los métodos, técnicas y herramientas para la localización de recursos ontológicos y cómo el multilingüismo puede ser representado en las ontologías. No es la meta de este trabajo apoyar una única propuesta para la localización de ontologías, sino más bien mostrar la variedad de métodos y técnicas que pueden ser readaptadas de otras áreas de conocimiento para reducir el costo y esfuerzo que significa enriquecer una ontología con información multilingüe. Estamos convencidos de que no hay un único método para la localización de ontologías. Sin embargo, nos concentramos en soluciones automáticas para la localización de estos recursos. La propuesta presentada en esta tesis provee una cobertura global de la actividad de localización para los profesionales ontológicos. En particular, este trabajo ofrece una explicación formal de nuestro proceso general de localización, definiendo las entradas, salidas, y los principales pasos identificados. Además, en la propuesta consideramos algunas dimensiones para localizar una ontología. Estas dimensiones nos permiten establecer una clasificación de técnicas de traducción basadas en métodos tomados de la disciplina de traducción por máquina. Para facilitar el análisis de estas técnicas de traducción, introducimos una estructura de evaluación que cubre sus aspectos principales. Finalmente, ofrecemos una vista intuitiva de todo el ciclo de vida de la localización de ontologías y esbozamos nuestro acercamiento para la definición de una arquitectura de sistema que soporte esta actividad. El modelo propuesto comprende los componentes del sistema, las propiedades visibles de esos componentes, las relaciones entre ellos, y provee además, una base desde la cual sistemas de localización de ontologías pueden ser desarrollados. Las principales contribuciones de este trabajo se resumen como sigue: - Una caracterización y definición de los problemas de localización de ontologías, basado en problemas encontrados en áreas relacionadas. La caracterización propuesta tiene en cuenta tres problemas diferentes de la localización: traducción, gestión de la información, y representación de la información multilingüe. - Una metodología prescriptiva para soportar la actividad de localización de ontologías, basada en las metodologías de localización usadas en Ingeniería del Software e Ingeniería del Conocimiento, tan general como es posible, tal que ésta pueda cubrir un amplio rango de escenarios. - Una clasificación de las técnicas de localización de ontologías, que puede servir para comparar (analíticamente) diferentes sistemas de localización de ontologías, así como también para diseñar nuevos sistemas, tomando ventaja de las soluciones del estado del arte. - Un método integrado para construir sistemas de localización de ontologías en un entorno distribuido y colaborativo, que tenga en cuenta los métodos y técnicas más apropiadas, dependiendo de: i) el dominio de la ontología a ser localizada, y ii) la cantidad de información lingüística requerida para la ontología final. - Un componente modular para soportar el almacenamiento de la información multilingüe asociada a cada término de la ontología. Nuestra propuesta sigue la tendencia actual en la integración de la información multilingüe en las ontologías que sugiere que el conocimiento de la ontología y la información lingüística (multilingüe) estén separados y sean independientes. - Un modelo basado en flujos de trabajo colaborativos para la representación del proceso normalmente seguido en diferentes organizaciones, para coordinar la actividad de localización en diferentes lenguajes naturales. - Una infraestructura integrada implementada dentro del NeOn Toolkit por medio de un conjunto de plug-ins y extensiones que soporten el proceso colaborativo de localización de ontologías

Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process

Hans Hjelm. Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process. NEALT Monograph Series, Vol. 1 (2009), 159 pages. © 2009 Hans Hjelm. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/10126

Analysis and implementation of methods for the text categorization

Text Categorization (TC) is the automatic classification of text documents under pre-defined categories, or classes. Popular TC approaches map categories into symbolic labels and use a training set of documents, previously labeled by human experts, to build a classifier which enables the automatic TC of unlabeled documents. Suitable TC methods come from the field of data mining and information retrieval, however the following issues remain unsolved. First, the classifier performance depends heavily on hand-labeled documents that are the only source of knowledge for learning the classifier. Being a labor-intensive and time consuming activity, the manual attribution of documents to categories is extremely costly. This creates a serious limitations when a set of manual labeled data is not available, as it happens in most cases. Second, even a moderately sized text collection often has tens of thousands of terms in that making the classification cost prohibitive for learning algorithms that do not scale well to large problem sizes. Most important, TC should be based on the text content rather than on a set of hand-labeled documents whose categorization depends on the subjective judgment of a human classifier. This thesis aims at facing the above issues by proposing innovative approaches which leverage techniques from data mining and information retrieval. To face problems about both the high dimensionality of the text collection and the large number of terms in a single text, the thesis proposes a hybrid model for term selection which combines and takes advantage of both filter and wrapper approaches. In detail, the proposed model uses a filter to rank the list of terms present in documents to ensure that useful terms are unlikely to be screened out. Next, to limit classification problems due to the correlation among terms, this ranked list is refined by a wrapper that uses a Genetic Algorithm (GA) to retaining the most informative and discriminative terms. Experimental results compare well with some of the top-performing learning algorithms for TC and seems to confirm the effectiveness of the proposed model. To face the issues about the lack and the subjectivity of manually labeled datasets, the basic idea is to use an ontology-based approach which does not depend on the existence of a training set and relies solely on a set of concepts within a given domain and the relationships between concepts. In this regard, the thesis proposes a text categorization approach that applies WordNet for selecting the correct sense of words in a document, and utilizes domain names in WordNet Domains for classification purposes. Experiments show that the proposed approach performs well in classifying a large corpus of documents. This thesis contributes to the area of data mining and information retrieval. Specifically, it introduces and evaluates novel techniques to the field of text categorization. The primary objective of this thesis is to test the hypothesis that: text categorization requires and benefits from techniques designed to exploit document content. hybrid methods from data mining and information retrieval can better support problems about high dimensionality that is the main aspect of large document collections. in absence of manually annotated documents, WordNet domain abstraction can be used that is both useful and general enough to categorize any documents collection. As a final remark, it is important to acknowledge that much of the inspiration and motivation for this work derived from the vision of the future of text categorization processes which are related to specific application domains such as the business area and the industrial sectors, just to cite a few. In the end, it is this vision that provided the guiding framework. However, it is equally important to understand that many of the results and techniques developed in this thesis are not limited to text categorization. For example, the evaluation of disambiguation methods is interesting in its own right and is likely to be relevant to other application fields

Analysis and implementation of methods for the text categorization

Text Categorization (TC) is the automatic classification of text documents under pre-defined categories, or classes. Popular TC approaches map categories into symbolic labels and use a training set of documents, previously labeled by human experts, to build a classifier which enables the automatic TC of unlabeled documents. Suitable TC methods come from the field of data mining and information retrieval, however the following issues remain unsolved. First, the classifier performance depends heavily on hand-labeled documents that are the only source of knowledge for learning the classifier. Being a labor-intensive and time consuming activity, the manual attribution of documents to categories is extremely costly. This creates a serious limitations when a set of manual labeled data is not available, as it happens in most cases. Second, even a moderately sized text collection often has tens of thousands of terms in that making the classification cost prohibitive for learning algorithms that do not scale well to large problem sizes. Most important, TC should be based on the text content rather than on a set of hand-labeled documents whose categorization depends on the subjective judgment of a human classifier. This thesis aims at facing the above issues by proposing innovative approaches which leverage techniques from data mining and information retrieval. To face problems about both the high dimensionality of the text collection and the large number of terms in a single text, the thesis proposes a hybrid model for term selection which combines and takes advantage of both filter and wrapper approaches. In detail, the proposed model uses a filter to rank the list of terms present in documents to ensure that useful terms are unlikely to be screened out. Next, to limit classification problems due to the correlation among terms, this ranked list is refined by a wrapper that uses a Genetic Algorithm (GA) to retaining the most informative and discriminative terms. Experimental results compare well with some of the top-performing learning algorithms for TC and seems to confirm the effectiveness of the proposed model. To face the issues about the lack and the subjectivity of manually labeled datasets, the basic idea is to use an ontology-based approach which does not depend on the existence of a training set and relies solely on a set of concepts within a given domain and the relationships between concepts. In this regard, the thesis proposes a text categorization approach that applies WordNet for selecting the correct sense of words in a document, and utilizes domain names in WordNet Domains for classification purposes. Experiments show that the proposed approach performs well in classifying a large corpus of documents. This thesis contributes to the area of data mining and information retrieval. Specifically, it introduces and evaluates novel techniques to the field of text categorization. The primary objective of this thesis is to test the hypothesis that: text categorization requires and benefits from techniques designed to exploit document content. hybrid methods from data mining and information retrieval can better support problems about high dimensionality that is the main aspect of large document collections. in absence of manually annotated documents, WordNet domain abstraction can be used that is both useful and general enough to categorize any documents collection. As a final remark, it is important to acknowledge that much of the inspiration and motivation for this work derived from the vision of the future of text categorization processes which are related to specific application domains such as the business area and the industrial sectors, just to cite a few. In the end, it is this vision that provided the guiding framework. However, it is equally important to understand that many of the results and techniques developed in this thesis are not limited to text categorization. For example, the evaluation of disambiguation methods is interesting in its own right and is likely to be relevant to other application fields