Manuel Montes

Manuel Montes
Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) | INAOE · Computational Sciences Coordination

PhD

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326
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3,887
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Publications

Publications (326)
Chapter
In this chapter, we describe the participation of our research team in the eRisk addressing the two editions of the early anorexia detection task. We used two domain-independent approaches to address this task. The first approach is based on a temporal-aware document representation, whereas the second one consists of a simple, interpretable, and no...
Chapter
Currently, self-harm is considered one of the leading causes of death by suicide in young people. Timely detection of self-inflicted injury is important to help people before the illness gets worse, minimizing disabilities and returning them to their normal life. A popular way for people to share information is using social media platforms, where t...
Article
Full-text available
Over the last few years, studies related to the detection of mental disorders in social media have been increasing. The latter because the awareness created by health campaigns that emphasizes the commonness of these disorders among all of us has motivated the creation of new datasets, many of them extracted from social media platforms. In this stu...
Article
The interpretation of medical images is a fundamental process for the diagnosis and treatment of patients. This process contributes determining the causes of symptoms as well as monitoring the effects of any treatment. Although the generation of medical reports from images is a complex task, deep learning strategies have been integrated with models...
Chapter
Thanks to the availability of digital media, users receive daily news reports, opinions and information on a wide variety of topics. These same media allow people to easily share and transmit their own opinions, thus enriching the debate and reflection on topics of public interest. Unfortunately, these circumstances have led to the emergence of fak...
Chapter
The detection of lesions from computed tomography scans is an important and nontrivial task in medical diagnosis. The difficulty of this task is related to the medical data where the appearance of different organs and lesions is not easily distinguished from the background. This paper proposes a One-Stage Lesion Detection method named OSLeD-wA. OSL...
Chapter
The author profiling task refers to extracting as much of an author through what he writes, such as gender, age, nationality, location, among others. Although this task arose a few decades ago, the explosion in social networks has made the task of author profiling mainly focus on digital media. Typically, previous works have used only the text of t...
Article
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Depression is a severe mental health problem. Due to its relevance, the development of computational tools for its detection has attracted increasing attention in recent years. In this context, several research works have addressed the problem using word-based approaches (e.g., a bag of words). This type of representation has shown to be useful, in...
Article
Depression is a common and very important health issue with serious effects in the daily life of people. Recently, several researchers have explored the analysis of user-generated data in social media to detect and diagnose signs of this mental disorder in individuals. In this regard, we tackled the depression detection task in social media conside...
Chapter
Social networks have become the main means of communication and interaction between people. In them, users share information and opinions, but also their experiences, worries, and personal concerns. Because of this, there is a growing interest in analyzing this kind of content to identify people who commit self-harm, which is often one of the first...
Chapter
This paper presents DeepBoSE, a novel deep learning model for depression detection in social media. The model is formulated such that it internally computes a differentiable Bag-of-Features (BoF) representation that incorporates emotional information. This is achieved by a reinterpretation of classical weighting schemes like tf-idf into probabilist...
Article
Full-text available
Taking advantage of the increasing amount of user-generated content in social media, some computational methods have already been proposed for detecting people suffering from depression and anorexia. Such complex tasks have been tackled as a binary classification problem using, in most cases, automatically generated training data. Despite its promi...
Article
Millions of people around the world are affected by one or more mental disorders that interfere in their thinking and behavior. A timely detection of these issues is challenging but crucial, since it could open the possibility to offer help to people before the illness gets worse. One alternative to accomplish this is to monitor how people express...
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Full-text available
Psychologists have used tests and carefully designed survey questions, such as Beck's Depression Inventory (BDI), to identify the presence of depression and to assess its severity level. On the other hand, methods for automatic depression detection have gained increasing interest since all the information available in social media, such as Twitter...
Preprint
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This paper presents the Deep Bag-of-Sub-Emotions (DeepBoSE), a novel deep learning model for depression detection in social media. The model is formulated such that it internally computes a differentiable Bag-of-Features (BoF) representation that incorporates emotional information. This is achieved by a reinterpretation of classical weighting schem...
Article
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This paper summarizes the thesis: "Author Profiling in Social Media with Multimodal Information." Our solution uses a multimodal approach to extracting information from written messages and images shared by users. Previous work has shown the existence of useful information for this task in these modalities; however, our proposal goes further, demon...
Chapter
Different mental disorders affect millions of people around the world, causing significant distress and interference to their daily life. Currently, the increased usage of social media platforms, where people share personal information about their day and problems, opens up new opportunities to actively detect these problems. We present a new appro...
Article
Full-text available
This paper considers the problem of leveraging multiple sources of information or data modalities (e.g., images and text) in neural networks. We define a novel model called gated multimodal unit (GMU), designed as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data...
Article
A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF’s eRisk open tasks. SS3 was created to deal with ERD problems naturally since: it supports incremental t...
Chapter
The increasing propagation of abusive language in social media is a major concern for supplier companies and governments because of its negative social impact. A large number of methods have been developed for its automatic identification, ranging from dictionary-based methods to sophisticated deep learning approaches. A common problem in all these...
Article
Full-text available
Passage retrieval is an important stage of question answering systems. Closed domain passage retrieval, e.g. biomedical passage retrieval presents additional challenges such as specialized terminology, more complex and elaborated queries, scarcity in the amount of available data, among others. However, closed domains also offer some advantages such...
Article
In this study we introduce the k-Strongest Strengths (kSS) Classification Algorithm, a novel approach for classification problems based on the well-known k-Nearest Neighbor (kNN) classifier. The proposed kSS method is motivated by an analogy to the Law of Universal Gravitation. The novelty of kSS resides in that instead of only using the neighbors’...
Article
The facilities provided by social media and computer-mediated communication make easy the dissemination of deceptive behavior, after which different entities or people could be affected. The deception detection by supervised learning has been widely studied; however, the scenario in which there is one domain of interest and the labeled data is in a...
Chapter
Full-text available
This paper describes the participation of the MindLab research group in the BioASQ 2019 Challenge for task 7b, document retrieval and snippet retrieval. For document retrieval, Elastic Search was used for the initial document retrieval step with BM25 as a scoring function. In the second stage, the top 100 retrieved documents were re-ranked with sev...
Article
In this study we propose a novel method to generate Document Embeddings (DEs) by means of evolving mathematical equations that integrate classical term frequency statistics. To accomplish this, we employed a Genetic Programming (GP) strategy to build competitive formulae to weight custom Word Embeddings (WEs), produced by cutting edge feature extra...
Chapter
Automatic systems for the analysis of textual information are highly relevant for the sake of developing completely autonomous assessment and authentication mechanisms in online learning scenarios. Since written texts within e-learning systems are mostly associated with exams and evaluations, it is critical to authenticate the identity of participa...
Preprint
Full-text available
A recently introduced text classifier, called SS3, has obtained state-of-the-art performance on the CLEF's eRisk tasks. SS3 was created to deal with risk detection over text streams and therefore not only supports incremental training and classification but also can visually explain its rationale. However, little attention has been paid to the pote...
Preprint
Full-text available
A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF's eRisk open tasks. SS3 was created to naturally deal with ERD problems since: it supports incremental t...
Chapter
Recent works have shown that it is possible to use information extracted from images to address the task of automatic gender identification. These proposals have validated their solutions using monolingual datasets, i.e., collections where images are shared by users having the same mother tongue. This paper aims to test the usefulness of images col...
Conference Paper
Full-text available
This paper presents the framework and results from the MEX-A3T track at IberLEF 2019. This track considers two tasks, author profiling and aggressiveness detection, both of them using Mexican Spanish tweets. The author profiling task consists on determining the gender, occupation and place of residence of users from their tweets. As a novelty in th...
Chapter
Full-text available
Depression is a mental disorder with strong social and economic implications. Due to its relevance, recently several researches have explored the analysis of social media content to identify and track depressed users. Most approaches follow a supervised learning strategy supported on the availability of labeled training data. Unfortunately, acquiri...
Conference Paper
Full-text available
In this paper, we present our approach to the detection of anorexia at eRisk 2019. The main objective of this shared task is to identify as soon as possible if a user shows signs of anorexia by using their posts on Reddit. For this, we evaluate a representation called Bag of Sub-Emotions (BoSE), a new technique that represents user posts by buildin...
Article
Full-text available
Automatic Image Annotation (AIA) is the task of assigning keywords to images, with the aim to describe their visual content. Recently, an unsupervised approach has been used to tackle this task. Unsupervised AIA (UAIA) methods use reference collections that consist of the textual documents containing images. The aim of the UAIA methods is to extrac...
Preprint
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We present experiments on detecting hyperpartisanship in news using a 'masking' method that allows us to assess the role of style vs. content for the task at hand. Our results corroborate previous research on this task in that topic related features yield better results than stylistic ones. We additionally show that competitive results can be achie...
Conference Paper
Full-text available
Nowadays social media platforms are the most popular way for people to share information, from work issues to personal matters. For example , people with health disorders tend to share their concerns for advice, support or simply to relieve suffering. This provides a great opportunity to proactively detect these users and refer them as soon as poss...
Article
This paper summarizes the thesis: "Identificación del perfil de autores en redes sociales usando nuevos esquemas de pesado que enfatizan información de tipo personal" whose main idea indicates that terms located in phrases exposing personal information are highly valuable for the AP task. Firstly, it is presented an study on the relevance of this i...
Preprint
Full-text available
Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents. Many research has been focused on determining suitable features for modeling writing patterns from authors. Reported results indicate that content-based features continue to be the most relevant and discriminant features f...
Preprint
Full-text available
With the rise of the Internet, there is a growing need to build intelligent systems that are capable of efficiently dealing with early risk detection (ERD) problems on social media, such as early depression detection, early rumor detection or identification of sexual predators. These systems, nowadays mostly based on machine learning techniques, mu...
Article
Patriarchal behavior, such as other social habits, has been transferred online, appearing as misogynistic and sexist comments, posts or tweets. This online hate speech against women has serious consequences in real life, and recently, various legal cases have arisen against social platforms that scarcely block the spread of hate messages towards in...
Article
Full-text available
Several methods have been proposed for determining plagiarism between pairs of sentences, passages or even full documents. However, the majority of these methods fail to reliably detect paraphrase plagiarism due to the high complexity of the task, even for human beings. Paraphrase plagiarism identification consists in automatically recognizing docu...
Article
Full-text available
The Bag-of-Visual-Words (BoVW) representation is a well known strategy to approach many computer vision problems. The idea behind BoVW is similar to the Bag-of-Words (BoW) used in text mining tasks: to build word histograms to represent documents. Regarding computer vision, most of the research has been devoted to obtain better visual words, rather...
Article
With the rise of the Internet, there is a growing need to build intelligent systems that are capable of efficiently dealing with early risk detection (ERD) problems on social media, such as early depression detection, early rumor detection or identification of sexual predators. These systems, nowadays mostly based on machine learning techniques, mu...
Chapter
Full-text available
The kNN algorithm has three main advantages that make it appealing to the community: it is easy to understand, it regularly offers competitive performance and its structure can be easily tuning to adapting to the needs of researchers to achieve better results. One of the variations is weighting the instances based on their distance. In this paper w...
Chapter
Academic competitions and challenges comprise an effective mechanism for rapidly advancing the state of the art in diverse research fields and for solving practical problems arising in industry. In fact, academic competitions are increasingly becoming an essential component of academic events, like conferences. With the proliferation of challenges,...
Chapter
The kNN algorithm has three main advantages that make it appealing to the community: it is easy to understand, it regularly offers competitive performance and its structure can be easily tuning to adapting to the needs of researchers to achieve better results. One of the variations is weighting the instances based on their distance. In this paper w...
Conference Paper
Full-text available
The kNN algorithm has three main advantages that make it appealing to the community: it is easy to understand, it regularly offers competitive performance and its structure can be easily tuning to adapting to the needs of researchers to achieve better results. One of the variations is weighting the instances based on their distance. In this paper w...
Conference Paper
Full-text available
Likability prediction of books has many uses. Readers, writers, as well as the publishing industry , can all benefit from automatic book likability prediction systems. In order to make reliable decisions, these systems need to assimilate information from different aspects of a book in a sensible way. We propose a novel multimodal neural architectur...
Conference Paper
Full-text available
Biomedical Question Answering is concerned with the development of methods and systems that automatically find answers to natural language posed questions. In this work, we describe the system used in the BioASQ Challenge task 6b for document retrieval and snippet retrieval (with particular emphasis in this subtask). The proposed model makes use of...
Chapter
In this work, we propose a variant of a well-known instance-based algorithm: WKNN. Our idea is to exploit task-dependent features in order to calculate the weight of the instances according to a novel paradigm: the Textual Attraction Force, that serves to quantify the degree of relatedness between documents. The proposed method was applied to a cha...
Conference Paper
Full-text available
This paper presents the framework and results from the MEX-A3T track at IberEval 2018. This track considers two tasks, author profiling and aggressiveness detection, both of them using Mexican Spanish tweets. The author profiling task aims to identify the place of residence and occupation of Twitter users. On the other hand, the aggressiveness dete...
Article
According to the World Health Organization, recent years have seen a dramatic increase in the number of car accidents worldwide. In an attempt to ameliorate this situation, the automotive and telematics industry has tried to develop technology that can help drivers make better and safer decisions. One approach is to develop systems that give feedba...
Conference Paper
Full-text available
Nowadays, misogynistic abuse online has become a serious issue due, especially, to anonymity and interactivity of the web that facilitate the increase and the permanence of the offensive comments on the web. In this paper, we present an approach based on stylistic and specific topic information for the detection of misogyny, exploring the several a...
Conference Paper
With the uncontrolled increasing of fake news, untruthful claims, and rumors over the web, recently different approaches have been proposed to address this problem. In this paper, we present a credibility detector of factual claims in presidential debates. Our approach captures the distribution of the results from the search engines to infer the cr...
Conference Paper
Journalists usually work for a long time to investigate presidential debates. Their main role is to extract the sentences in the debates that include information about facts or previous events. These sentences are called claims. The investigation process of these claims is important where it can reveal how credible is the speaker or the other candi...