Multilingual Animacy Classification by Sparse Logistic Regression

This paper presents results from three experiments on automatic animacy classification in Japanese and English. We present experiments that focus on solutions to the problem of reliably classifying a large set of infrequent items using a small number of automatically extracted features. We labeled a set of Japanese nouns as ±animate on the basis of reliable, surface-obvious morphological features, producing an accurately but sparsely labeled data set. To classify these nouns, and to achieve good generalization to other nouns for which we do not have labels, we used feature vectors based on frequency counts of verbargument relations that abstract away from item identity and into class-wide distributional tendencies of the feature set. Grouping items into suffix-based equivalence classes prior to classification increased data coverage and improved classification accuracy. For the items that occur at least once with our feature set, we obtained 95% classification accuracy. We used loanwords to transfer automatically acquired labels from English to classify items that are zerofrequency in the Japanese data set, giving increased precision on inanimate items and increased recall on animate items

Arabic named entity recognition

En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados gracias al enriquecimiento que a~nade al texto. En la literatura existen diversos trabajos que investigan la tarea de REN para un idioma especifico o desde una perspectiva independiente del lenguaje. Sin embargo, hasta el momento, se han publicado muy pocos trabajos que estudien dicha tarea para el arabe. El arabe tiene una ortografia especial y una morfologia compleja, estos aspectos aportan nuevos desafios para la investigacion en la tarea de REN. Una investigacion completa del REN para elarabe no solo aportaria las tecnicas necesarias para conseguir un alto rendimiento, sino que tambien proporcionara un analisis de los errores y una discusion sobre los resultados que benefician a la comunidad de investigadores del REN. El objetivo principal de esta tesis es satisfacer esa necesidad. Para ello hemos: 1. Elaborado un estudio de los diferentes aspectos del arabe relacionados con dicha tarea; 2. Analizado el estado del arte del REN; 3. Llevado a cabo una comparativa de los resultados obtenidos por diferentes tecnicas de aprendizaje automatico; 4. Desarrollado un metodo basado en la combinacion de diferentes clasificadores, donde cada clasificador trata con una sola clase de entidades nombradas y emplea el conjunto de caracteristicas y la tecnica de aprendizaje automatico mas adecuados para la clase de entidades nombradas en cuestion. Nuestros experimentos han sido evaluados sobre nueve conjuntos de test.Benajiba, Y. (2009). Arabic named entity recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8318Palanci

VALICO-UD: annotating an Italian learner corpus

Previous work on learner language has highlighted the importance of having annotated resources to describe the development of interlanguage. Despite this, few learner resources, mainly for English L2, feature error and syntactic annotation. This thesis describes the development of a novel parallel learner Italian treebank, VALICO-UD. Its name suggests two main points: where the data comes from—i.e. the corpus VALICO, a collection of non-native Italian texts elicited by comic strips—and what formalism is used for linguistic annotation—i.e. Universal Dependencies (UD) formalism. It is a parallel treebank because the resource provides for each learner sentence (LS) a target hypothesis (TH) (i.e., parallel corrected version written by an Italian native speaker) which is in turn annotated in UD. We developed this treebank to be exploitable for interlanguage research and comparable with the resources employed in Natural Language Processing tasks such as Native Language Identification or Grammatical Error Identification and Correction. VALICO-UD is composed of 237 texts written by English, French, German and Spanish native speakers, which correspond to 2,234 LSs, each associated with a single TH. While all LSs and THs were automatically annotated using UDPipe, only a portion of the treebank made of 398 LSs plus correspondent THs has been manually corrected and released in May 2021 in the UD repository. This core section features also an explicit XML-based annotation of the errors occurring in each sentence. Thus, the treebank is currently organized in two sections: the core gold standard—comprising 398 LSs and their correspondent THs—and the silver standard—consisting of 1,836 LSs and their correspondent THs. In order to contribute to the computational investigation about the peculiar type of texts included in VALICO-UD, this thesis describes the annotation schema of the resource, provides some preliminary tests about the performance of UDPipe models on this treebank, reports on inter-annotator agreement results for both error and linguistic annotation, and suggests some possible applications

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Computational approaches to semantic change (Volume 6)

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans