Descubrimiento de Colocaciones Utilizando Semántica

Collocations are combinations of two lexically dependent elements, of which one (the base) is freely chosen because of its meaning, and the choice of the other (the collocate) depends on the base. Collocations are difficult to master by language learners. This difficulty becomes evident in that even when learners know the meaning they want to express, they often struggle to choose the right collocate. Collocation dictionaries, in which collocates are grouped into semantic categories, are useful tools. However, they are scarce since they are the result of cost-intensive manual elaboration. In this paper, we present for Spanish an algorithm that automatically retrieves for a given base and a given semantic category the corresponding collocates.Las colocaciones, entendidas como combinaciones de dos elementos entre los cuales existe una dependencia léxica, es decir, donde uno de los elementos (la base) se escoge libremente por su significado, pero el otro (colocativo) depende de la base, suelen ser difíciles de utilizar por los hablantes no nativos de una lengua. Esta dificultad se hace visible en que estos, a menudo, aún sabiendo el significado que quieren expresar, tienen problemas a la hora de elegir el colocativo adecuado. Los diccionarios de colocaciones, donde los colocativos son agrupados en categorías semánticas son una herramienta muy útil, pero son recursos escasos y de costosa elaboración. En este artículo se presenta, para el español, un algoritmo que proporciona, dada una base y una categoría semántica, colocativos pertinentes a dicha categoría.The present work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO), through a predoctoral grant (BES-2012-057036) in the framework of the project HARenES (FFI2011-30219-C02-02) and the Maria de Maeztu Excellence Program (MDM-2015-0502)

A Winnow-Based Approach to Context-Sensitive Spelling Correction

A large class of machine-learning problems in natural language require the characterization of linguistic context. Two characteristic properties of such problems are that their feature space is of very high dimensionality, and their target concepts refer to only a small subset of the features in the space. Under such conditions, multiplicative weight-update algorithms such as Winnow have been shown to have exceptionally good theoretical properties. We present an algorithm combining variants of Winnow and weighted-majority voting, and apply it to a problem in the aforementioned class: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting "to" for "too", "casual" for "causal", etc. We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a statistics-based method representing the state of the art for this task. We find: (1) When run with a full (unpruned) set of features, WinSpell achieves accuracies significantly higher than BaySpell was able to achieve in either the pruned or unpruned condition; (2) When compared with other systems in the literature, WinSpell exhibits the highest performance; (3) The primary reason that WinSpell outperforms BaySpell is that WinSpell learns a better linear separator; (4) When run on a test set drawn from a different corpus than the training set was drawn from, WinSpell is better able than BaySpell to adapt, using a strategy we will present that combines supervised learning on the training set with unsupervised learning on the (noisy) test set.Comment: To appear in Machine Learning, Special Issue on Natural Language Learning, 1999. 25 page

Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods

In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds--- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations

D6.1: Technologies and Tools for Lexical Acquisition

This report describes the technologies and tools to be used for Lexical Acquisition in PANACEA. It includes descriptions of existing technologies and tools which can be built on and improved within PANACEA, as well as of new technologies and tools to be developed and integrated in PANACEA platform. The report also specifies the Lexical Resources to be produced. Four main areas of lexical acquisition are included: Subcategorization frames (SCFs), Selectional Preferences (SPs), Lexical-semantic Classes (LCs), for both nouns and verbs, and Multi-Word Expressions (MWEs)

A Bootstrapping architecture for time expression recognition in unlabelled corpora via syntactic-semantic patterns

In this paper we describe a semi-supervised approach to the extraction of time expression mentions in large unlabelled corpora based on bootstrapping. Bootstrapping techniques rely on a relatively small amount of initial human-supplied examples (termed “seeds”) of the type of entity or concept to be learned, in order to capture an initial set of patterns or rules from the unlabelled text that extract the supplied data. In turn, the learned patterns are employed to find new potential examples, and the process is repeated to grow the set of patterns and (optionally) the set of examples. In order to prevent the learned pattern set from producing spurious results, it becomes essential to implement a ranking and selection procedure to filter out “bad” patterns and, depending on the case, new candidate examples. Therefore, the type of patterns employed (knowledge representation) as well as the ranking and selection procedure are paramount to the quality of the results. We present a complete bootstrapping algorithm for recognition of time expressions, with a special emphasis on the type of patterns used (a combination of semantic and morpho- syntantic elements) and the ranking and selection criteria. Bootstrap- ping techniques have been previously employed with limited success for several NLP problems, both of recognition and classification, but their application to time expression recognition is, to the best of our knowledge, novel. As of this writing, the described architecture is in the final stages of implementation, with experimention and evalution being already underway.Postprint (published version

Etiquetado no supervisado de la polaridad de las palabras utilizando representaciones continuas de palabras

Sentiment analysis is the area of Natural Language Processing that aims to determine the polarity (positive, negative, neutral) contained in an opinionated text. A usual resource employed in many of these approaches are the so-called polarity lexicons. A polarity lexicon acts as a dictionary that assigns a sentiment polarity value to words. In this work we explore the possibility of automatically generating domain adapted polarity lexicons employing continuous word representations, in particular the popular tool Word2Vec. First we show a qualitative evaluation of a small set of words, and then we show our results in the SemEval-2015 task 12 using the presented method.El análisis de sentimiento es un campo del procesamiento del lenguaje natural que se encarga de determinar la polaridad (positiva, negativa, neutral) en los textos en los que se vierten opiniones. Un recurso habitual en los sistemas de análisis de sentimiento son los lexicones de polaridad. Un lexicón de polaridad es un diccionario que asigna un valor predeterminado de polaridad a una palabra. En este trabajo exploramos la posibilidad de generar de manera automática lexicones de polaridad adaptados a un dominio usando representaciones continuas de palabras, en concreto la popular herramienta Word2Vec. Primero mostramos una evaluación cualitativa de la polaridad sobre un pequeño conjunto de palabras, y después mostramos los resultados de nuestra competición en la tarea 12 del SemEval-2015 usando este método.This work has been supported by Vicomtech-IK4

Workshop Proceedings of the 12th edition of the KONVENS conference

The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years