First International Workshop on Lexical Resources

International audienceLexical resources are one of the main sources of linguistic information for research and applications in Natural Language Processing and related fields. In recent years advances have been achieved in both symbolic aspects of lexical resource development (lexical formalisms, rule-based tools) and statistical techniques for the acquisition and enrichment of lexical resources, both monolingual and multilingual. The latter have allowed for faster development of large-scale morphological, syntactic and/or semantic resources, for widely-used as well as resource-scarce languages. Moreover, the notion of dynamic lexicon is used increasingly for taking into account the fact that the lexicon undergoes a permanent evolution.This workshop aims at sketching a large picture of the state of the art in the domain of lexical resource modeling and development. It is also dedicated to research on the application of lexical resources for improving corpus-based studies and language processing tools, both in NLP and in other language-related fields, such as linguistics, translation studies, and didactics

Comparative Analysis of Automatic Term and Collocation Extraction

Monolingual and multilingual terminology and collocation bases, covering a specific domain, used independently or integrated with other resources, have become a valuable electronic resource. Building of such resources could be assisted by automatic term extraction tools, combining statistical and linguistic approaches. In this paper, the research on term extraction from monolingual corpus is presented. The corpus consists of publicly accessible English legislative documents. In the paper, results of two hybrid approaches are compared: extraction using the TermeX tool and an automatic statistical extraction procedure followed by linguistic filtering through the open source linguistic engineering tool. The results have been elaborated through statistical measures of precision, recall, and F-measure

Probabilistic Modelling of Morphologically Rich Languages

This thesis investigates how the sub-structure of words can be accounted for in probabilistic models of language. Such models play an important role in natural language processing tasks such as translation or speech recognition, but often rely on the simplistic assumption that words are opaque symbols. This assumption does not fit morphologically complex language well, where words can have rich internal structure and sub-word elements are shared across distinct word forms. Our approach is to encode basic notions of morphology into the assumptions of three different types of language models, with the intention that leveraging shared sub-word structure can improve model performance and help overcome data sparsity that arises from morphological processes. In the context of n-gram language modelling, we formulate a new Bayesian model that relies on the decomposition of compound words to attain better smoothing, and we develop a new distributed language model that learns vector representations of morphemes and leverages them to link together morphologically related words. In both cases, we show that accounting for word sub-structure improves the models' intrinsic performance and provides benefits when applied to other tasks, including machine translation. We then shift the focus beyond the modelling of word sequences and consider models that automatically learn what the sub-word elements of a given language are, given an unannotated list of words. We formulate a novel model that can learn discontiguous morphemes in addition to the more conventional contiguous morphemes that most previous models are limited to. This approach is demonstrated on Semitic languages, and we find that modelling discontiguous sub-word structures leads to improvements in the task of segmenting words into their contiguous morphemes.Comment: DPhil thesis, University of Oxford, submitted and accepted 2014. http://ora.ox.ac.uk/objects/uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c

Compiling, Processing and Accessing the Collection of Legal Regulations of the Republic of Croatia

Accelerated Internet development and expansion contributed to the growing number of regulations published in an electronic form. Due to that fact, one of the latest research objectives is development of new programs for searching through such documentation and development of digital collections. Croatian Information Documentation Referral Agency (in further text: HIDRA) systematically and regularly gathers publicly available official documentation in print and electronic form, and its goal is to gather documentation to enable simple user access to information from a single point. On HIDRA’s web site there are fully available legal documents of the Republic of Croatia with all changes and amendments, bylaws and other data and relations accessible through hyperlinks, relevant to a regulation in question. Thanks to electronic documents and good information-documentation practice, complete Croatian legislation, including that enacted prior to 1990, is for the first time available on-line in a unique way. This Paper will show how a simple access to a complex content (such as legislation) is developed, and which knowledge, technologies and institutions must be included in its realization

Towards modeling Arabic lexicons compliant LMF in OWL-DL

International audienceElaborating reusable lexical databases and especially making interoperability operational are crucial tasks effecting both Natural Language Processing (NLP) and Semantic Web. With this respect, we consider that modeling Lexical Markup Framework (LMF) in Web Ontology Language Description Logics (OWL-DL) can be a beneficial attempt to reach these aims. This proposal will have large repute since it concerns the reference standard LMF for modeling lexical structures. In this paper, we study the requirement for this suggestion. We first make a quick presentation of the LMF framework. Next, we define the three ontology definition sublanguages that may be easily used by specific users: OWL Lite, OWL-DL and OWL Full. After comparing of the three, we have chosen to work with OWL-DL. We then define the ontology language OWL and describe the steps needed to model LMF in OWL. Finally, we apply this model to develop an instance for an Arabic lexicon

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

Uticaj klasifikacije teksta na primene u obradi prirodnih jezika

The main goal of this dissertation is to put different text classification tasks in the same frame, by mapping the input data into the common vector space of linguistic attributes. Subsequently, several classification problems of great importance for natural language processing are solved by applying the appropriate classification algorithms. The dissertation deals with the problem of validation of bilingual translation pairs, so that the final goal is to construct a classifier which provides a substitute for human evaluation and which decides whether the pair is a proper translation between the appropriate languages by means of applying a variety of linguistic information and methods. In dictionaries it is useful to have a sentence that demonstrates use for a particular dictionary entry. This task is called the classification of good dictionary examples. In this thesis, a method is developed which automatically estimates whether an example is good or bad for a specific dictionary entry. Two cases of short message classification are also discussed in this dissertation. In the first case, classes are the authors of the messages, and the task is to assign each message to its author from that fixed set. This task is called authorship identification. The other observed classification of short messages is called opinion mining, or sentiment analysis. Starting from the assumption that a short message carries a positive or negative attitude about a thing, or is purely informative, classes can be: positive, negative and neutral. These tasks are of great importance in the field of natural language processing and the proposed solutions are language-independent, based on machine learning methods: support vector machines, decision trees and gradient boosting. For all of these tasks, a demonstration of the effectiveness of the proposed methods is shown on for the Serbian language.Osnovni cilj disertacije je stavljanje različitih zadataka klasifikacije teksta u isti okvir, preslikavanjem ulaznih podataka u isti vektorski prostor lingvističkih atributa..