Legal-ES is an open source resource kit for legal Spanish. It consists of a large scale Spanish corpus of open legal texts and different kinds of language models including word embeddings and topic models. The corpus includes over 1000 million words covering a collection of legislative and administrative open access documents in Spanish from different sources representing international, national and regional entities. The corpus is pre-processed and tokenized using Spacy. For the word embeddings, gensim was used on the collection of tokens, producing a representation space that is especially suited to reflect the inherent characteristics of the legal domain. We calculate also topic models to obtain a convenient tool to understand the main topics in the corpus and to navigate through the documents exploiting the semantic similarity among documents. We will analyse the time structure of a dynamic topic model to infer changes in the legal production of Spanish jurisdiction that have occurred over the analysed time framework.

This paper tests two different strategies for medical term extraction in an Arabic Medical Corpus. The experiments and the corpus are developed within the framework of Multimedica project funded by the Spanish Ministry of Science and Innovation and aiming at developing multilingual resources and tools for processing of newswire texts in the Health domain. The first experiment uses a fixed list of medical terms, the second experiment uses a list of Arabic equivalents of very limited list of common Latin prefix and suffix used in medical terms. Results show that using equivalents of Latin suffix and prefix outperforms the fixed list. The paper starts with an introduction, followed by a description of the state-of-art in the field of Arabic Medical Language Resources (LRs). The third section describes the corpus and its characteristics. The fourth and the fifth sections explain the lists used and the results of the experiments carried out on a sub-corpus for evaluation. The last section analyzes the results outlining the conclusions and future work.

Dating of contents is relevant to multiple advanced Natural Language Processing (NLP) applications, such as Information Retrieval or Question Answering. These could be improved by using techniques that consider a temporal dimension in their processes. To achieve it, an accurate detection of temporal expressions in data sources must be firstly done, dealing with them in an appropriated standard format that captures the time value of the expressions once resolved, and allows reasoning without ambiguity, in order to increase the range of search and the quality of the results to be returned. These tasks are completely necessary for NLP applications if an efficient temporal reasoning is afterwards expected. This work presents a typology of time expressions based on an empirical inductive approach, both from a structural perspective and from the point of view of their resolution. Furthermore, a method for the automatic recognition and resolution of temporal expressions in Spanish contents is provided, obtaining promising results when it is tested by means of an evaluation corpus.

Discourse structure and coherence relations are one of the main inferential challenges addressed by computational pragmatics. The present study focuses on discourse markers as key elements in guiding the inferences of the statements in natural language. Through a rule-based approach for the automatic identification, classification and annotation of the discourse markers in a multilingual parallel corpus (Arabic-Spanish-English), this research provides a valuable resource for the community. Two main aspects define the novelty of the present study. First, it offers a multilingual computational processing of discourse markers, grounded on a theoritical framework and implemented in a XML tagging scheme. The XML scheme represents a set of pragmatic and grammatical attributes, considered as basic features for the different kinds of discourse markers. Besides, the scheme provides a typology of discourse markers based on their discursive functions including hypothesis, co-argumentation, cause, consequence, concession, generalization, topicalization, reformulation, enumeration, synthesis, etc. Second, Arabic language is addressed from a computational pragmatic perspective where the identification, classification and annotation processes are carried out using the information provided from the tagging of Spanish discourse markers and the alignments.

This paper presents the results (1st phase) of the on-going research in the Computational Linguistics Laboratory at Autónoma University of Madrid (LLI-UAM) aiming at the development of a multi-lingual parallel corpus (Arabic-Spanish-English) aligned on the sentence level and tagged on the POS level. A multilingual parallel corpus which brings together Arabic, Spanish and English is a new resource for the NLP community that completes the present panorama of parallel corpora. In the first part of this study, we introduce the novelty of our approach and the challenges encountered to create such a corpus. This introductory part highlights the main features of the corpus and the criteria applied during the selection process. The second part focuses on two main stages: basic processing (tokenization and segmentation) and alignment. Methodology of alignment is explained in detail and results obtained in the three different linguistic pairs are compared. POS tagging and tools used in this stage are discussed in the third part. The final output is available in two versions: the non-aligned version and the aligned one. The latter adopts the TMX (Translation Memory Exchange) standard format. At the end, the section dedicated to the future work points out the key stages concerned with extending the corpus and the studies that can benefit, directly or indirectly, from such a resource.

Parallel corpora are considered an important resource for the development of linguistic tools. In this paper our main goal is the development of a bilingual lexicon of verbs. The construction of this lexicon is possible using two main resources: I) a parallel corpus (through the alignment); II) the linguistic tools developed for Spanish (which serve as a starting point for developing tools for Arabic language). At the end, aligned equivalent verbs are detected automatically from a parallel corpus Spanish-Arabic. To achieve this goal, we had to pass through different preparatory stages concerning the assesment of the parallel corpus, the monolingual tokenization of each corpus, a preliminary sentence alignment and finally applying the model of automatic extraction of equivalent verbs. Our method is hybrid, since it combines both statistical and linguistic approaches.