Ondina fragilissima Peñas & Rolán, 2002

Catálogo do Museo de Historia Natural USC. n. inventario 10038

Técnicas aplicadas al reconocimiento de implicación textual.

Tras establecer qué se entiende por implicación textual, se expone la situación actual y el futuro deseable de los sistemas dirigidos a reconocerla. Se realiza una identificación de las técnicas que implementan actualmente los principales sistemas de Reconocimiento de Implicación Textual

Entrenamiento Croslingüe para Búsqueda de Respuestas de Opción Múltiple

In this work we explore to what extent multilingual models can be trained for one language and applied to a different one for the task of Multiple Choice Question Answering. We employ the RACE dataset to fine-tune both a monolingual and a multilingual models and apply these models to another different collections in different languages. The results show that both monolingual and multilingual models can be zero-shot transferred to a different dataset in the same language maintaining its performance. Besides, the multilingual model still performs good when it is applied to a different target language. Additionally, we find that exams that are more difficult to humans are harder for machines too. Finally, we advance the state-of-the-art for the QA4MRE Entrance Exams dataset in several languages.En este trabajo exploramos en qué medida los modelos multilingües pueden ser entrenados para un solo idioma y aplicados a otro diferente para la tarea de respuesta a preguntas de opción múltiple. Empleamos el conjunto de datos RACE para ajustar tanto un modelo monolingüe como multilingüe y aplicamos estos modelos a otras colecciones en idiomas diferentes. Los resultados muestran que tanto los modelos monolingües como los multilingües pueden transferirse a un conjunto de datos diferente en el mismo idioma manteniendo su rendimiento. Además, el modelo multilingüe todavía funciona bien cuando se aplica a un idioma de destino diferente. Asimismo, hemos comprobado que los exámenes que son más difíciles para los humanos también son más difíciles para las máquinas. Finalmente, avanzamos el estado del arte para el conjunto de datos QA4MRE Entrance Exams en varios idiomas.This work has been funded by the Spanish Research Agency under CHIST-ERA LIHLITH project (PCIN-2017-085/AEI) and deepReading (RTI2018-096846-B-C21 /MCIU/AEI/FEDER,UE)

On Concept Lattices as Information Channels

Proceedings of: 11th International Conference on Concept Lattices and Their Applications (CLA 2014). Kosice, Slovakia, October 07-10, 2014.This paper explores the idea that a concept lattice is an information channel between objects and attributes. For this purpose we study the behaviour of incidences in L-formal contexts where L is the range of an information-theoretic entropy function. Examples of such data abound in machine learning and data mining, e.g. confusion matrices of multi-class classifers or document-term matrices. We use a wellmotivated information-theoretic heuristic, the maximization of mutual information, that in our conclusions provides a favour of feature selection providing and information-theory explanation of an established practice in Data Mining, Natural Language Processing and Information Retrieval applications, viz. stop-wording and frequency thresholding. We also introduce a post-clustering class identi cation in the presence of confusions and a favour of term selection for a multi-label document classifcation task.FJVA and AP are supported by EU FP7 project LiMoSINe (contract 288024) for this work. CPM has been supported by the Spanish Government-Comisión Interministerial de Ciencia y Tecnología project TEC2011-26807.Publicad

Techniques for recognizing textual entailment and semantic equivalence

After defining what is understood by textual entailment and semantic equivalence, the present state and the desirable future of the systems aimed at recognizing them is shown. A compilation of the currently implemented techniques in the main Recognizing Textual Entailment and Semantic Equivalence systems is given

Distinción semántica de compuestos léxicos en recuperación de información

La consideración de sintagmas no parece producir mejoras significativas en los modelos clásicos de Recuperación de Información. En general, se acepta que los criterios de proximidad proporcionan mejores resultados que un criterio de adyacencia. El trabajo que se presenta explora la hipótesis de que no todos los compuestos léxicos deben considerarse de la misma forma. Se propone un procedimiento automático de clasificación semántica de los compuestos léxicos de WordNet sobre la base de sus componentes, y se estudia cómo afecta esta distinción a la Recuperación de Información.Este trabajo ha sido parcialmente financiado por el Ministerio de Ciencia y Tecnología a través del proyecto Hermes (TIC2000-0335-C03-01)

UNED at PASCAL RTE-2 challenge

This paper reports the description of the developed system and the results obtained in the participation of the UNED in the Second Recognizing Textual Entailment (RTE) Challenge. New techniques and tools have been added: enriched queries to WordNet, detection of numeric expressions and their entailment, and Support Vector Machine classi cation (SVM) are the more relevant. The accuracy performed is slightly higher than the one from the previous edition system

Terminology Retrieval: Towards a Synergy between Thesaurus and Free Text Searching

Abstract. Multilingual Information Retrieval usually forces a choice between free text indexing or indexing by means of multilingual thesaurus. However, since they share the same objectives, synergy between both approaches is possible. This paper shows a retrieval framework that make use of terminological information in free-text indexing. The Automatic Terminology Extraction task, which is used for thesauri construction, shifts to a searching of terminology and becomes an information retrieval task: Terminology Retrieval. Terminology Retrieval, then, allows cross-language information retrieval through the browsing of morpho-syntactic, semantic and translingual variations of the query. Although terminology retrieval doesn’t make use of them, controlled vocabularies become an appropriate framework for terminology retrieval evaluation.

Grounding proposition stores for question answering over linked data

Grounding natural language utterances into semantic representations is crucial for tasks such as question answering and knowledge base population. However, the importance of the lexicons that are central to this mapping remains unmeasured because question answering systems are evaluated as end-to-end systems. This article proposes a methodology to enable a standalone evaluation of grounding natural language propositions into semantic relations by fixing all the components of a question answering system other than the lexicon itself. Thus, we can explore different configurations trying to conclude which are the ones that contribute better to improve overall system performance. Our experiments show that grounding accounts with close to 80% of the system performance without training, whereas training supposes a relative improvement of 7.6%. Finally we show how lexical expansion using external linguistic resources can consistently improve the results from 0.8% up to 2.5%

Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis

In this paper we fuse together the Landscapes of Knowledge of Wille's and Exploratory Data Analysis by leveraging Formal Concept Analysis (FCA) to support data-induced scientific enquiry and discovery. We use extended FCA first by allowing K-valued entries in the incidence to accommodate other, non-binary types of data, and second with different modes of creating formal concepts to accommodate diverse conceptualizing phenomena. With these extensions we demonstrate the versatility of the Landscapes of Knowledge metaphor to help in creating new scientific and engineering knowledge by providing several successful use cases of our techniques that support scientific hypothesis-making and discovery in a range of domains: semiring theory, perceptual studies, natural language semantics, and gene expression data analysis. While doing so, we also capture the affordances that justify the use of FCA and its extensions in scientific discovery.FJVA and AP were partially supported by EUFP7 project LiMo- SINe (contract288024) for this research. CPM was partially supported by the Spanish Ministry of Economics and Competitiveness projects TEC2014-61729-EXP and TEC2014-53390-P