Boosting terminology extraction through crosslingual resources

Terminology Extraction is an important Natural Language Processing task with multiple applications in many areas. The task has been approached from different points of view using different techniques. Language and domain independent systems have been proposed as well. Our contribution in this paper focuses on the improvements on Terminology Extraction using crosslingual resources and specifically the Wikipedia and on the use of a variant of PageRank for scoring the candidate terms. // La extracción de terminología es una tarea de procesamiento de la lengua sumamente importante y aplicable en numerosas áreas. La tarea se ha abordado desde múltiples perspectivas y utilizando técnicas diversas. También se han propuesto sistemas independientes de la lengua y del dominio. La contribución de este artículo se centra en las mejoras que los sistemas de extracción de terminología pueden lograr utilizando recursos translingües, y concretamente la Wikipedia y en el uso de una variante de PageRank para valorar los candidatos a términoPeer ReviewedPostprint (published version

Concurrent Context-Free Framework for Conceptual Similarity Problem using Reverse Dictionary

Semantic search is one of the most prominent options to search the required and relevant content from the web. But most of them are doing key word and phrase wise similarity search. It may or may not find the relevant information because they directly search with that phrase. But, in most of the cases documents may conceptually equal instead of term wise. Reverse dictionary can solve such type of problems. This will take meaning of the word and it will return related keywords with respective ranks. But main problem here is building such dictionaries is time and memory consuming. Cost effective solutions are required to reduce search time and in-memory requirements. This paper focuses on such aspects by utilizing concurrent programming and efficient index structures and builds a framework to Conceptual similarity problem using reverse dictionary. Simulation results shows that proposed approach can take less time when compared to existing approaches

A Trio Neural Model for Dynamic Entity Relatedness Ranking

Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time. In this work, we propose a neural networkbased approach for dynamic entity relatedness, leveraging the collective attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.Comment: In Proceedings of CoNLL 201

A Runyakitara Culture Wiki

Peterson Asingwire developed a Runyakitara culture framework in 2013, with the aim of using the wiki as a tool for Runyakitara culture documentation, collaboration, sharing, preservation, and revitalization. This paper discusses the implementation of his framework using the wikispaces web hosting service. The Runyakitara culture wiki is entirely presented in Runyakitara, from page titles to introductory information. The wiki currently has three pages: one for proverbs (enfumu), idioms (emiguutuuro), and riddles (ebishaakuzo). Our wiki is available to be read by everyone, though only members are allowed to edit and update the pages. We hope that our wiki will grow as a repository of Runyakitara culture and realize the purposes for which it was created.Keywords: wiki, Runyakitara, indigenous language, culture, collaborative learning, collaborative writin

Distributed Agents For Contextual Online Searches

As semantic web use and research blossoms, automated online searches - whether to answer a simple question, seek specific sensor readings, or investigate research in a particular domain - has raised a number of issues. Simple search tools cannot handle context-specific search problems, but specialist search tools have a narrow domain and applicability. Some online tools circumvent these problems by putting more filter controls into the hands of users, but this leads to more complex interfaces which can raise usability barriers. A distributed approach, where specialised search agents act autonomously to find contextualised information, can provide a useful compromise between a simple, general search interface and specialist searches. This paper outlines work in progress on design and use of specialist search agents, with a case study to find public transportation bus stops within a spatial region. The approach is demonstrated with a case-study web interface, developed to interpret a text query to find and show bus stop locations within a named boundary by coordinating multiple online search agents. Search agents were designed to follow a common model to allow for future development of agent types, including specialist agents used in the case study to search standard open web services and extract spatial features

k-TVT: a flexible and effective method for early depression detection

The increasing use of social media allows the extraction of valuable information to early prevent some risks. Such is the case of the use of blogs to early detect people with signs of depression. In order to address this problem, we describe k-temporal variation of terms (k-TVT), a method which uses the variation of vocabulary along the different time steps as concept space to represent the documents. An interesting particularity of this approach is the possibility of setting a parameter (the k value) depending on the urgency (earliness) level required to detect the risky (depressed) cases. Results on the early detection of depression data set from eRisk 2017 seem to confirm the robustness of k-TVT for different urgency levels using SVM as classifier. Besides, some recent results on an extension of this collection would confirm the effectiveness of k-TVT as one of the state-of-the-art methods for early depression detection.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

k-TVT: a flexible and effective method for early depression detection

The increasing use of social media allows the extraction of valuable information to early prevent some risks. Such is the case of the use of blogs to early detect people with signs of depression. In order to address this problem, we describe k-temporal variation of terms (k-TVT), a method which uses the variation of vocabulary along the different time steps as concept space to represent the documents. An interesting particularity of this approach is the possibility of setting a parameter (the k value) depending on the urgency (earliness) level required to detect the risky (depressed) cases. Results on the early detection of depression data set from eRisk 2017 seem to confirm the robustness of k-TVT for different urgency levels using SVM as classifier. Besides, some recent results on an extension of this collection would confirm the effectiveness of k-TVT as one of the state-of-the-art methods for early depression detection.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Analysing entity context in multilingual Wikipedia to support entity-centric retrieval applications

Representation of influential entities, such as famous people and multinational corporations, on the Web can vary across languages, reflecting language-specific entity aspects as well as divergent views on these entities in different communities. A systematic analysis of language specific entity contexts can provide a better overview of the existing aspects and support entity-centric retrieval applications over multilingual Web data. An important source of cross-lingual information about influential entities is Wikipedia — an online community-created encyclopaedia — containing more than 280 language editions. In this paper we focus on the extraction and analysis of the language-specific entity contexts from different Wikipedia language editions over multilingual data. We discuss alternative ways such contexts can be built, including graph-based and article-based contexts. Furthermore, we analyse the similarities and the differences in these contexts in a case study including 80 entities and five Wikipedia language editions