Exploring the automatic selection of basic level concepts

We present a very simple method for selecting Base Level Concepts using basic structural properties of WordNet. We also empirically demonstrate that these automatically derived set of Base Level Concepts group senses into an adequate level of abstraction in order to perform class-based Word Sense Disambiguation. In fact a very naive Most Frequent classifier using the classes selected is able to perform a semantic tagging with accuracy figures over 75%.Union Europea bajo proyecto QALL-ME (FP6 IST-033860) y el Gobierno Español bajo el proyecto Text-Mess (TIN2006-15265-C06-01) y KNOW (TIN2006-15049-C03-01

Word vs. Class-Based Word Sense Disambiguation

As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. Many authors argue that one possible reason could be the use of inappropriate sets of word meanings. In particular, WordNet has been used as a de-facto standard repository of word meanings in most of these tasks. Thus, instead of using the word senses defined in WordNet, some approaches have derived semantic classes representing groups of word senses. However, the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained semantic class level (also called SuperSenses). We suspect that an appropriate level of abstraction could be on between both levels. The contributions of this paper are manifold. First, we propose a simple method to automatically derive semantic classes at intermediate levels of abstraction covering all nominal and verbal WordNet meanings. Second, we empirically demonstrate that our automatically derived semantic classes outperform classical approaches based on word senses and more coarse-grained sense groupings. Third, we also demonstrate that our supervised WSD system benefits from using these new semantic classes as additional semantic features while reducing the amount of training examples. Finally, we also demonstrate the robustness of our supervised semantic class-based WSD system when tested on out of domain corpus.This work has been partially supported by the NewsReader project (ICT-2011-316404), the Spanish project SKaTer (TIN2012-38584-C06-02)

Consejos para un mejor rendimiento de Hibernate

Presentación para un mejor rendimiento de la herramienta Hibernate

Consultas con Hibernate

Presentació sobre la realització de consultes en Hibernate

Herramientas para Hibernate

Presentación sobre herramientas complementarias para Hibernate

Leveraging Machine Learning to Explain the Nature of Written Genres

The analysis of discourse and the study of what characterizes it in terms of communicative objectives is essential to most tasks of Natural Language Processing. Consequently, research on textual genres as expressions of such objectives presents an opportunity to enhance both automatic techniques and resources. To conduct an investigation of this kind, it is necessary to have a good understanding of what defines and distinguishes each textual genre. This research presents a data-driven approach to discover and analyze patterns in several textual genres with the aim of identifying and quantifying the differences between them, considering how language is employed and meaning expressed in each particular case. To identify and analyze patterns within genres, a set of linguistic features is first defined, extracted and computed by using several Natural Language Processing tools. Specifically, the analysis is performed over a corpora of documents—containing news, tales and reviews—gathered from different sources to ensure an heterogeneous representation. Once the feature dataset has been generated, machine learning techniques are used to ascertain how and to what extent each of the features should be present in a document depending on its genre. The results show that the set of features defined is relevant for characterizing the different genres. Furthermore, the findings allow us to perform a qualitative analysis of such features, so that their usefulness and suitability is corroborated. The results of the research can benefit natural language discourse processing tasks, which are useful both for understanding and generating language.This work was supported in part by the Ministry of Science and Innovation of Spain for the project “Integer: Intelligent Text Generarion” under Grant RTI2018-094649-B-I00, and in part by the Generalitat Valenciana through project “SIIA: Tecnologias del lenguaje humano para una sociedad inclusiva, igualitaria, y accesible" under Grant PROMETEU/2018/089

Modelo Relacional (5/5)

Última parte del tema Modelo Relacional

Hibernate para Object/Relational Mapping (ORM)

Presentación introductoria para el mapeo objetos/relacional

Extracting Narrative Patterns in Different Textual Genres: A Multilevel Feature Discourse Analysis

We present a data-driven approach to discover and extract patterns in textual genres with the aim of identifying whether there is an interesting variation of linguistic features among different narrative genres depending on their respective communicative purposes. We want to achieve this goal by performing a multilevel discourse analysis according to (1) the type of feature studied (shallow, syntactic, semantic, and discourse-related); (2) the texts at a document level; and (3) the textual genres of news, reviews, and children’s tales. To accomplish this, several corpora from the three textual genres were gathered from different sources to ensure a heterogeneous representation, paying attention to the presence and frequency of a series of features extracted with computational tools. This deep analysis aims at obtaining more detailed knowledge of the different linguistic phenomena that directly shape each of the genres included in the study, therefore showing the particularities that make them be considered as individual genres but also comprise them inside the narrative typology. The findings suggest that this type of multilevel linguistic analysis could be of great help for areas of research within natural language processing such as computational narratology, as they allow a better understanding of the fundamental features that define each genre and its communicative purpose. Likewise, this approach could also boost the creation of more consistent automatic story generation tools in areas of language generation.This research work is part of the R&D project “PID2021-123956OB-I00”, funded by MCIN/AEI/10.13039/501100011033/ and by “ERDF A way of making Europe”. Moreover, it was also partially funded by the project “CLEAR.TEXT: Enhancing the modernization public sector organizations by deploying natural language processing to make their digital content CLEARER to those with cognitive disabilities” (TED2021-130707B-I00), by the Generalitat Valenciana through the project “NL4DISMIS: Natural Language Technologies for dealing with dis- and misinformation” with grant reference CIPROM/2021/21, and finally by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231)

A Review in Knowledge Extraction from Knowledge Bases

Generative language models achieve the state of the art in many tasks within natural language processing (NLP). Although these models correctly capture syntactic information, they fail to interpret knowledge (semantics). Moreover, the lack of interpretability of these models promotes the use of other technologies as a replacement or complement to generative language models. This is the case with research focused on incorporating knowledge by resorting to knowledge bases mainly in the form of graphs. The generation of large knowledge graphs is carried out with unsupervised or semi-supervised techniques, which promotes the validation of this knowledge with the same type of techniques due to the size of the generated databases. In this review, we will explain the different techniques used to test and infer knowledge from graph structures with machine learning algorithms. The motivation of validating and inferring knowledge is to use correct knowledge in subsequent tasks with improved embeddings