From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning

Tesis por compendioNatural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human languages. One of its most challenging aspects involves enabling computers to derive meaning from human natural language. To do so, several meaning or context representations have been proposed with competitive performance. However, these representations still have room for improvement when working in a cross-domain or cross-language scenario. In this thesis we study the use of knowledge graphs as a cross-domain and cross-language representation of text and its meaning. A knowledge graph is a graph that expands and relates the original concepts belonging to a set of words. We obtain its characteristics using a wide-coverage multilingual semantic network as knowledge base. This allows to have a language coverage of hundreds of languages and millions human-general and -specific concepts. As starting point of our research we employ knowledge graph-based features - along with other traditional ones and meta-learning - for the NLP task of single- and cross-domain polarity classification. The analysis and conclusions of that work provide evidence that knowledge graphs capture meaning in a domain-independent way. The next part of our research takes advantage of the multilingual semantic network and focuses on cross-language Information Retrieval (IR) tasks. First, we propose a fully knowledge graph-based model of similarity analysis for cross-language plagiarism detection. Next, we improve that model to cover out-of-vocabulary words and verbal tenses and apply it to cross-language document retrieval, categorisation, and plagiarism detection. Finally, we study the use of knowledge graphs for the NLP tasks of community questions answering, native language identification, and language variety identification. The contributions of this thesis manifest the potential of knowledge graphs as a cross-domain and cross-language representation of text and its meaning for NLP and IR tasks. These contributions have been published in several international conferences and journals.El Procesamiento del Lenguaje Natural (PLN) es un campo de la informática, la inteligencia artificial y la lingüística computacional centrado en las interacciones entre las máquinas y el lenguaje de los humanos. Uno de sus mayores desafíos implica capacitar a las máquinas para inferir el significado del lenguaje natural humano. Con este propósito, diversas representaciones del significado y el contexto han sido propuestas obteniendo un rendimiento competitivo. Sin embargo, estas representaciones todavía tienen un margen de mejora en escenarios transdominios y translingües. En esta tesis estudiamos el uso de grafos de conocimiento como una representación transdominio y translingüe del texto y su significado. Un grafo de conocimiento es un grafo que expande y relaciona los conceptos originales pertenecientes a un conjunto de palabras. Sus propiedades se consiguen gracias al uso como base de conocimiento de una red semántica multilingüe de amplia cobertura. Esto permite tener una cobertura de cientos de lenguajes y millones de conceptos generales y específicos del ser humano. Como punto de partida de nuestra investigación empleamos características basadas en grafos de conocimiento - junto con otras tradicionales y meta-aprendizaje - para la tarea de PLN de clasificación de la polaridad mono- y transdominio. El análisis y conclusiones de ese trabajo muestra evidencias de que los grafos de conocimiento capturan el significado de una forma independiente del dominio. La siguiente parte de nuestra investigación aprovecha la capacidad de la red semántica multilingüe y se centra en tareas de Recuperación de Información (RI). Primero proponemos un modelo de análisis de similitud completamente basado en grafos de conocimiento para detección de plagio translingüe. A continuación, mejoramos ese modelo para cubrir palabras fuera de vocabulario y tiempos verbales, y lo aplicamos a las tareas translingües de recuperación de documentos, clasificación, y detección de plagio. Por último, estudiamos el uso de grafos de conocimiento para las tareas de PLN de respuesta de preguntas en comunidades, identificación del lenguaje nativo, y identificación de la variedad del lenguaje. Las contribuciones de esta tesis ponen de manifiesto el potencial de los grafos de conocimiento como representación transdominio y translingüe del texto y su significado en tareas de PLN y RI. Estas contribuciones han sido publicadas en diversas revistas y conferencias internacionales.El Processament del Llenguatge Natural (PLN) és un camp de la informàtica, la intel·ligència artificial i la lingüística computacional centrat en les interaccions entre les màquines i el llenguatge dels humans. Un dels seus majors reptes implica capacitar les màquines per inferir el significat del llenguatge natural humà. Amb aquest propòsit, diverses representacions del significat i el context han estat proposades obtenint un rendiment competitiu. No obstant això, aquestes representacions encara tenen un marge de millora en escenaris trans-dominis i trans-llenguatges. En aquesta tesi estudiem l'ús de grafs de coneixement com una representació trans-domini i trans-llenguatge del text i el seu significat. Un graf de coneixement és un graf que expandeix i relaciona els conceptes originals pertanyents a un conjunt de paraules. Les seves propietats s'aconsegueixen gràcies a l'ús com a base de coneixement d'una xarxa semàntica multilingüe d'àmplia cobertura. Això permet tenir una cobertura de centenars de llenguatges i milions de conceptes generals i específics de l'ésser humà. Com a punt de partida de la nostra investigació emprem característiques basades en grafs de coneixement - juntament amb altres tradicionals i meta-aprenentatge - per a la tasca de PLN de classificació de la polaritat mono- i trans-domini. L'anàlisi i conclusions d'aquest treball mostra evidències que els grafs de coneixement capturen el significat d'una forma independent del domini. La següent part de la nostra investigació aprofita la capacitat\hyphenation{ca-pa-ci-tat} de la xarxa semàntica multilingüe i se centra en tasques de recuperació d'informació (RI). Primer proposem un model d'anàlisi de similitud completament basat en grafs de coneixement per a detecció de plagi trans-llenguatge. A continuació, vam millorar aquest model per cobrir paraules fora de vocabulari i temps verbals, i ho apliquem a les tasques trans-llenguatges de recuperació de documents, classificació, i detecció de plagi. Finalment, estudiem l'ús de grafs de coneixement per a les tasques de PLN de resposta de preguntes en comunitats, identificació del llenguatge natiu, i identificació de la varietat del llenguatge. Les contribucions d'aquesta tesi posen de manifest el potencial dels grafs de coneixement com a representació trans-domini i trans-llenguatge del text i el seu significat en tasques de PLN i RI. Aquestes contribucions han estat publicades en diverses revistes i conferències internacionals.Franco Salvador, M. (2017). A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84285TESISCompendi

Part-Of-Speech Tagging Of Urdu in Limited Resources Scenario

We address the problem of Part-of-Speech (POS) tagging of Urdu. POS tagging is the process of assigning a part-of-speech or lexical class marker to each word in the given text. Tagging for natural languages is similar to tokenization and lexical analysis for computer languages, except that we encounter ambiguities which are to be resolved. It plays a fundamental role in various Natural Language Processing (NLP) applications such as word sense disambiguation, parsing, name entity recognition and chunking. POS tagging, particularly plays very important role in processing free-word-order languages because such languages have relatively complex morphological structure. Urdu is a morphologically rich language. Forms of the verb, as well as case, gender, and number are expressed by the morphology. It shares its morphology, phonology and grammatical structures with Hindi. It shares its vocabulary with Arabic, Persian, Sanskrit, Turkish and Pashto languages. Urdu is written using the Perso-Arabic script. POS tagging of Urdu is a necessary component for most NLP applications of Urdu. Development of an Urdu POS tagger will influence several pipelined modules of natural language understanding system, including machine translation; partial parsing and word sense disambiguation. Our objective is to develop a robust POS tagger for Urdu. We have worked on the automatic annotation of part-of-speech for Urdu. We have defined a tag-set for Urdu. We manually annotated a corpus of 10,000 sentences. We have used different machine learning methods, namely Hidden Markov Model (HMM), Maximum Entropy Model (ME) and Conditional Random Field (CRF). Further, to deal with a small-annotated corpus, we explored the use of semi-supervised learning by using an additional un-annotated corpus. We also explored the use of a dictionary to provide to us all possible POS labeling for a given word. Since Urdu is morphologically productive. Hence we augmented Hidden Markov Model, Maximum Entropy Model and Conditional Random Field with morphological features, word suffixes and POS categories of words to develop robust POS tagger for Urdu in the limited resources scenario

Lexical simplification for the systematic support of cognitive accessibility guidelines

The Internet has come a long way in recent years, contributing to the proliferation of large volumes of digitally available information. Through user interfaces we can access these contents, however, they are not accessible to everyone. The main users affected are people with disabilities, who are already a considerable number, but accessibility barriers affect a wide range of user groups and contexts of use in accessing digital information. Some of these barriers are caused by language inaccessibility when texts contain long sentences, unusual words and complex linguistic structures. These accessibility barriers directly affect people with cognitive disabilities. For the purpose of making textual content more accessible, there are initiatives such as the Easy Reading guidelines, the Plain Language guidelines and some of the languagespecific Web Content Accessibility Guidelines (WCAG). These guidelines provide documentation, but do not specify methods for meeting the requirements implicit in these guidelines in a systematic way. To obtain a solution, methods from the Natural Language Processing (NLP) discipline can provide support for achieving compliance with the cognitive accessibility guidelines for the language. The task of text simplification aims at reducing the linguistic complexity of a text from a syntactic and lexical perspective, the latter being the main focus of this Thesis. In this sense, one solution space is to identify in a text which words are complex or uncommon, and in the case that there were, to provide a more usual and simpler synonym, together with a simple definition, all oriented to people with cognitive disabilities. With this goal in mind, this Thesis presents the study, analysis, design and development of an architecture, NLP methods, resources and tools for the lexical simplification of texts for the Spanish language in a generic domain in the field of cognitive accessibility. To achieve this, each of the steps present in the lexical simplification processes is studied, together with methods for word sense disambiguation. As a contribution, different types of word embedding are explored and created, supported by traditional and dynamic embedding methods, such as transfer learning methods. In addition, since most of the NLP methods require data for their operation, a resource in the framework of cognitive accessibility is presented as a contribution.Internet ha avanzado mucho en los últimos años contribuyendo a la proliferación de grandes volúmenes de información disponible digitalmente. A través de interfaces de usuario podemos acceder a estos contenidos, sin embargo, estos no son accesibles a todas las personas. Los usuarios afectados principalmente son las personas con discapacidad siendo ya un número considerable, pero las barreras de accesibilidad afectan a un gran rango de grupos de usuarios y contextos de uso en el acceso a la información digital. Algunas de estas barreras son causadas por la inaccesibilidad al lenguaje cuando los textos contienen oraciones largas, palabras inusuales y estructuras lingüísticas complejas. Estas barreras de accesibilidad afectan directamente a las personas con discapacidad cognitiva. Con el fin de hacer el contenido textual más accesible, existen iniciativas como las pautas de Lectura Fácil, las pautas de Lenguaje Claro y algunas de las pautas de Accesibilidad al Contenido en la Web (WCAG) específicas para el lenguaje. Estas pautas proporcionan documentación, pero no especifican métodos para cumplir con los requisitos implícitos en estas pautas de manera sistemática. Para obtener una solución, los métodos de la disciplina del Procesamiento del Lenguaje Natural (PLN) pueden dar un soporte para alcanzar la conformidad con las pautas de accesibilidad cognitiva relativas al lenguaje La tarea de la simplificación de textos del PLN tiene como objetivo reducir la complejidad lingüística de un texto desde una perspectiva sintáctica y léxica, siendo esta última el enfoque principal de esta Tesis. En este sentido, un espacio de solución es identificar en un texto qué palabras son complejas o poco comunes, y en el caso de que sí hubiera, proporcionar un sinónimo más usual y sencillo, junto con una definición sencilla, todo ello orientado a las personas con discapacidad cognitiva. Con tal meta, en esta Tesis, se presenta el estudio, análisis, diseño y desarrollo de una arquitectura, métodos PLN, recursos y herramientas para la simplificación léxica de textos para el idioma español en un dominio genérico en el ámbito de la accesibilidad cognitiva. Para lograr esto, se estudia cada uno de los pasos presentes en los procesos de simplificación léxica, junto con métodos para la desambiguación del sentido de las palabras. Como contribución, diferentes tipos de word embedding son explorados y creados, apoyados por métodos embedding tradicionales y dinámicos, como son los métodos de transfer learning. Además, debido a que gran parte de los métodos PLN requieren datos para su funcionamiento, se presenta como contribución un recurso en el marco de la accesibilidad cognitiva.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Antonio Macías Iglesias.- Secretario: Israel González Carrasco.- Vocal: Raquel Hervás Ballestero

Nabra: Syrian Arabic Dialects with Morphological Annotations

This paper presents Nabra, a corpora of Syrian Arabic dialects with morphological annotations. A team of Syrian natives collected more than 6K sentences containing about 60K words from several sources including social media posts, scripts of movies and series, lyrics of songs and local proverbs to build Nabra. Nabra covers several local Syrian dialects including those of Aleppo, Damascus, Deir-ezzur, Hama, Homs, Huran, Latakia, Mardin, Raqqah, and Suwayda. A team of nine annotators annotated the 60K tokens with full morphological annotations across sentence contexts. We trained the annotators to follow methodological annotation guidelines to ensure unique morpheme annotations, and normalized the annotations. F1 and kappa agreement scores ranged between 74% and 98% across features, showing the excellent quality of Nabra annotations. Our corpora are open-source and publicly available as part of the Currasat portal https://sina.birzeit.edu/currasat

Domain adaptation and training data acquisition in wide-coverage word sense disambiguation and its application to information retrieval

Ph.DDOCTOR OF PHILOSOPH

An Urdu semantic tagger - lexicons, corpora, methods and tools

Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as Natural Language Processing (NLP), corpus linguistics, data sciences, etc. An important aspect of such automatic information extraction and analysis is the semantic annotation of language data using semantic annotation tool (a.k.a semantic tagger). Generally, different semantic annotation tools have been designed to carry out various levels of semantic annotations, for instance, sentiment analysis, word sense disambiguation, content analysis, semantic role labelling, etc. These semantic annotation tools identify or tag partial core semantic information of language data, moreover, they tend to be applicable only for English and other European languages. A semantic annotation tool that can annotate semantic senses of all lexical units (words) is still desirable for the Urdu language based on USAS (the UCREL Semantic Analysis System) semantic taxonomy, in order to provide comprehensive semantic analysis of Urdu language text. This research work report on the development of an Urdu semantic tagging tool and discuss challenging issues which have been faced in this Ph.D. research work. Since standard NLP pipeline tools are not widely available for Urdu, alongside the Urdu semantic tagger a suite of newly developed tools have been created: sentence tokenizer, word tokenizer and part-of-speech tagger. Results for these proposed tools are as follows: word tokenizer reports $F_1$ of 94.01\%, and accuracy of 97.21\%, sentence tokenizer shows F$_1$ of 92.59\%, and accuracy of 93.15\%, whereas, POS tagger shows an accuracy of 95.14\%. The Urdu semantic tagger incorporates semantic resources (lexicon and corpora) as well as semantic field disambiguation methods. In terms of novelty, the NLP pre-processing tools are developed either using rule-based, statistical, or hybrid techniques. Furthermore, all semantic lexicons have been developed using a novel combination of automatic or semi-automatic approaches: mapping, crowdsourcing, statistical machine translation, GIZA++, word embeddings, and named entity. A large multi-target annotated corpus is also constructed using a semi-automatic approach to test accuracy of the Urdu semantic tagger, proposed corpus is also used to train and test supervised multi-target Machine Learning classifiers. The results show that Random k-labEL Disjoint Pruned Sets and Classifier Chain multi-target classifiers outperform all other classifiers on the proposed corpus with a Hamming Loss of 0.06\% and Accuracy of 0.94\%. The best lexical coverage of 88.59\%, 99.63\%, 96.71\% and 89.63\% are obtained on several test corpora. The developed Urdu semantic tagger shows encouraging precision on the proposed test corpus of 79.47\%

Automated Translation with Interlingual Word Representations

In dit proefschrift onderzoeken we het gebruik vertaalsystemen die gebruiken maken van een transferfase met interlinguale representaties van woorden. Op deze manier benaderen we het probleem van de lexicale ambiguïteit in de automatische vertaalsystemen als twee afzonderlijke taken: het bepalen van woordbetekenis en lexicale selectie. Eerst worden de woorden in de brontaal op basis van hun betekenis gedesambigueerd, resulterend in interlinguale representaties van woorden. Vervolgens wordt een lexicale selectiemodule gebruikt die het meest geschikte woord in de doeltaal selecteert. We geven een gedetailleerde beschrijving van de ontwikkeling en evaluatie van vertaalsystemen voor Nederlands-Engels. Dit biedt een achtergrond voor de experimenten in het tweede en derde deel van dit proefschrift. Daarna beschrijven we een methode die de betekenis van woorden bepaalt. Deze is vergelijkbaar met het klassieke Lesk-algoritme, omdat het gebruik maakt van het idee dat gedeelde woorden tussen de context van een woord en zijn definitie informatie over de betekenis ervan verschaffen. Wij gebruiken echter, in plaats daarvan, woord- en betekenisvectoren om de overeenkomst te berekenen tussen de definitie van een betekenis en de context van een woord. We gebruiken onze methode bovendien voor het localiseren en -interpreteren van woordgrapjes.Ten slotte presenteren we een model voor lexicale keuze dat lemma's selecteert, gegeven de abstracte representaties van woorden. Dit doen we door de grammaticale bomen te converteren naar hidden Markov bomen. Op deze manier kan de optimale combinatie van lemmas en hun context berekend worden