Graph-based Techniques for Topic Classification of Tweets in Spanish

Cordobés, Héctor; Anta, Antonio Fernández; Chiroque, Luis F.; Pérez, Fernando; Redondo, Teófilo; Santos, Agustín

doi:10.9781/ijimai.2014.254

Cited by 16 publications

(12 citation statements)

References 9 publications

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“…A problem that has been widely studied is how to find the characteristic words of a document (e.g., [44][45][46][47]). ese characteristic words can be used, for instance, to implement keywordbased document search.…”

Section: Characteristic Words Of a Documentmentioning

confidence: 99%

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

Hernández

Pérez

Granados

et al. 2019

Scientific Programming

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This paper addresses the problem of automatically customizing the sending of notifications in a nondisturbing way, that is, by using only implicit-feedback. Then, we build a hybrid filter that combines text mining content filtering and collaborative filtering to predict the notifications that are most interesting for each user. The content-based filter clusters notifications to find content with topics for which the user has shown interest. The collaborative filter increases diversity by discovering new topics of interest for the user, because these are of interest to other users with similar concerns. The paper reports the result of measuring the performance of this recommender and includes a validation of the topics-based approach used for content selection. Finally, we demonstrate how the recommender uses implicit-feedback to personalize the content to be delivered to each user.

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Section: Characteristic Words Of a Documentmentioning

confidence: 99%

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

Hernández

Pérez

Granados

et al. 2019

Scientific Programming

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“…As mentioned above, the base resources used for this task are Hunspell, Twokenize, and NetLingo. These resources were selected because they have been widely and successfully used to pre-process tweets in other research works, such as [26], [27], [33], [34], [35], [36], [37].…”

Section: Text Pre-processingmentioning

confidence: 99%

Automatic detection of satire in Twitter: A psycholinguistic-based approach

Salas-Zrate

Paredes-Valverde

Rodriguez-Garca

et al. 2017

Knowledge-Based Systems

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Knowledge-Based Systems Rights NOTICE: this is the author's version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems, [, , (2017-04-24)]

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“…Many different models have been proposed over the years. The most prominent of these are (HMM), Maximum Entropy Markov Models (MEMM), SVM or even a vector classification model for which the features are not terms, but graph metrics [47] and CRF. Other studies used unsupervised machine learning methods; a class of problems in which one seeks to determine how the data are organized such as clustering; a common technique for statistical data analysis used in many fields as used in [48].…”

Section: Information Extraction and Methodsmentioning

confidence: 99%

Using Local Grammar for Entity Extraction from Clinical Reports

Ghoulam¹,

Barigou²,

Belalem³

et al. 2015

IJIMAI

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-Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyze texts written in natural language to extract structured and useful information such as named entities and semantic relations linking these entities. Information extraction is an important task for many applications such as bio-medical literature mining, customer care, community websites, and personal information management. The increasing information available in patient clinical reports is difficult to access. As it is often in an unstructured text form, doctors need tools to enable them access to this information and the ability to search it. Hence, a system for extracting this information in a structured form can benefits healthcare professionals. The work presented in this paper uses a local grammar approach to extract medical named entities from French patient clinical reports. Experimental results show that the proposed approach achieved an F-Measure of 90. 06%.

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Graph-based Techniques for Topic Classification of Tweets in Spanish

Cited by 16 publications

References 9 publications

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

Automatic detection of satire in Twitter: A psycholinguistic-based approach

Using Local Grammar for Entity Extraction from Clinical Reports

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