Publicaciones – SIIA

“I Congreso Internacional ICT 2020”

Beatriz Botella — Wed, 29 Jan 2020 11:49:01 +0000

Beatriz Botella, componente del proyecto SIIA, asistió al I Congreso Internacional ICT 2020 celebrado en la Universidad de Loyola en Dos Hermanas, Sevilla.

En este congreso presentó su trabajo de investigación “ICT & Cyberbullying. Future challenges”, donde se recogen cuales son los elementos que envuelven al ciberbullying y que planes de futuro encontramos ante este creciente y grave problemas entre menores a través de las tecnologías de la comunicación y la información.

Team GPLSI. Approach for automated fact checking

Beatriz Botella — Wed, 13 Nov 2019 10:26:54 +0000

Fever Shared 2.0 Task is a challenge meant for developing automated fact checking systems. Our approach for the Fever 2.0 is based on a previous proposal developed by Team Athene UKP TU Darmstadt. Our proposal modifies the sentence retrieval phase, using statement extraction and representation in the form of triplets (subject, object, action). Triplets are extracted from the claim and compare to triplets extracted from Wikipedia articles using semantic similarity. Our results are satisfactory but there is room for improvement

Congreso: Conference on Empirical Methods in Natural Language Processing (and forerunners) (EMNLP) 2019

Autores: Aimée Alonso-Reina, Robiert Sepúlveda-Torres, Estela Saquete , Manuel Palomar

URL: https://www.aclweb.org/anthology/D19-6617.pdf

Fighting post-truth using natural language processing: A review and open challenge

Beatriz Botella — Wed, 16 Oct 2019 08:47:07 +0000

Post-truth is a term that describes a distorting phenomenon that aims to manipulate public opinion and behavior. One of its key engines is the spread of Fake News. Nowadays most news is rapidly disseminated in written language via digital media and social networks. Therefore, to detect fake news it is becoming increasingly necessary to apply Artificial Intelligence (AI) and, more specifically Natural Language Processing (NLP). This paper presents a review of the application of AI to the complex task of automatically detecting fake news. The review begins with a definition and classification of fake news. Considering the complexity of the fake news detection task, a divide-and-conquer methodology was applied to identify a series of subtasks to tackle the problem from a computational perspective. As a result, the following subtasks were identified: deception detection; stance detection; controversy and polarization; automated fact checking; clickbait detection; and, credibility scores. From each subtask, a PRISMA compliant systematic review of the main studies was undertaken, searching Google Scholar. The various approaches and technologies are surveyed, as well as the resources and competitions that have been involved in resolving the different subtasks. The review concludes with a roadmap for addressing the future challenges that have emerged from the analysis of the state of the art, providing a rich source of potential work for the research community going forward.

Revista: Expert Sysstems with Applications, Volume 141, 1 March 2020, 112943

Autores: Estela Saquete; David Tomás; Paloma Moreda; Patricio Martínez-Barco; ManuelPalomar

URL: https://www.sciencedirect.com/science/article/pii/S095741741930661X?dgcid=author

EmoLabel: Semi-Automatic Methodology for Emotion Annotation of Social Media Text

Beatriz Botella — Wed, 16 Oct 2019 08:44:29 +0000

The exponential growth of the amount of subjective information on the Web 2.0. has caused an increasing interest from researchers willing to develop methods to extract emotion data from these new sources. One of the most important challenges in textual emotion detection is the gathering of data with emotion labels because of the subjectivity of assigning these labels. Basing on this rationale, the main objective of our research is to contribute to the resolution of this important challenge. This is tackled by proposing EmoLabel: a semi-automatic methodology based on pre-annotation, which consists of two main phases: (1) an automatic process to pre-annotate the unlabelled English sentences; and (2) a manual process of refinement where human annotators determine which is the dominant emotion. Our objective is to assess the influence of this automatic pre-annotation method on manual emotion annotation from two points of view: agreement and time needed for annotation. The evaluation performed demonstrates the benefits of pre-annotation processes since the results on annotation time show a gain of near 20% when the pre-annotation process is applied (Pre-ML) without reducing annotator performance. Moreover, the benefits of pre-annotation are higher in those contributors whose performance is low (inaccurate annotators).

Revista: IEEE Transactions on Affective Computing

Autores: Lea Canales ; Walter Daelemans ; Ester Boldrini ; Patricio Martínez-Barco

URL: https://ieeexplore.ieee.org/document/8758380

The Effect of Various Straightforward Summarization Techniques on Informativeness of Abstractive Summaries

Beatriz Botella — Wed, 16 Oct 2019 08:42:50 +0000

In the present paper we describe how a straight forward abstractive text summarization method based on syntactic text simplification, subject-verb-object concept frequency scoring and a set of rules that transform text in to its semantic representation improves informativeness of automatic summaries. We analyze the impact of each individual component of our approach on the quality of generated summaries and test it on DUC 2002 dataset. Our experiments show that our approach outperforms other state-of-the-art abstractive methods while maintaining acceptable linguistic quality and redundancy rate.

Revista: RANLP 2019

Autores: Tatiana Vodolazova and Elena Lloret

URL:

Towards Adapting Automatic Text Summarization for L2 Learners: Does Compression Rate Affect Summary Readability?

Beatriz Botella — Wed, 16 Oct 2019 08:40:07 +0000

This paper addresses the problem of readability of automatically generated summaries in the context of second language learning. For this we experimented with a new corpus of level-annotated simplified English texts. The texts were summarized using a total of 7 extractive and abstractive summarization systems with compression rates of 20%, 40%, 60% and 80%. We analyzed the generated summaries in terms of lexical, syntactic and length-based features of readability, and concluded that summary complexity depends on the compression rate, summarization technique and the nature of the summarized corpus. Our experiments demonstrate the importance of choosing appropriate summarization techniques that align with users needs and language proficiency.

Revista: RANLP 2019

Autores: Tatiana Vodolazova and Elena Lloret

URL:

Tackling the Challenge of Computational Identification of Characters in Fictional Narratives

Beatriz Botella — Wed, 16 Oct 2019 08:38:36 +0000

This paper focuses on the computational identification of characters in fictional narratives, regardless of their nature, i.e., either humans, animals or other type of beings. We approach this problem as a supervised binary classification task, whether or not a noun in a narrative —specifically in a fairy tale— is classified as a character. A wide range of Machine Learning algorithms and configurations were tested in order to come up with the most appropriate model (or setof models) to successfully fulfil this task. Despite the challenges associated with the character identification in the domain of children stories, the best models obtain an F-Measure above 0.80, proving a good performance and broadly out performing the baselines.

Revista: 2019 IEEE International Conference on Cognitive Computing (ICCC)

Autores: Cristina Barros, Marta Vicente and Elena Lloret

URL: https://conferences.computer.org/serviceswp/2019/pdfs/ICCC2019-2zMzc10H4Ll2R40yDgIcLN/63UDGN49F9EPnnsxySuQO4/2kDLlfvCCHO2wCdVKIcEXk.pdf

NATSUM: Narrative abstractive summarization through cross-document timeline generation

Beatriz Botella — Wed, 16 Oct 2019 08:35:52 +0000

A new approach to narrative abstractive summarization (NATSUM) is presented in this paper. NATSUM is centered on generating a narrative chronologically ordered summary about a target entity from several news documents related to the same topic. To achieve this, first, our system creates a cross-document timeline where a time point contains all the event mentions that refer to the same event. This timeline is enriched with all the arguments of the events that are extracted from different documents. Secondly, using natural language generation techniques, one sentence for each event is produced using the arguments involved in the event. Specifically, a hybrid surface realization approach is used, based on over-generation and ranking techniques. The evaluation demonstrates that NATSUM performed better than extractive summarization approaches and competitive abstractive baselines, improving the F1-measure at least by 50%, when a real scenario is simulated.

Revista: Information Processing & Management. 2019, 56(5): 1775-1793

Autores: Barros, Cristina | Lloret, Elena | Saquete Boró, Estela | Navarro Colorado, Borja

URL: https://www.sciencedirect.com/science/article/pii/S0306457318305922

Distributed Architectures for Intensive Urban Computing: A Case Study on Smart Lighting for Sustainable Cities

Beatriz Botella — Wed, 16 Oct 2019 08:33:54 +0000

New information and communication technologies have contributed to the development of the smart city concept. On a physical level, this paradigm is characterized by deploying a substantial number of different devices that can sense their surroundings and generate a large amount of data. The most typical case is image and video acquisition sensors. Recently, these types of sensors are found in abundance in urban spaces and are responsible for producing a large volume of multimedia data. The advanced computer vision methods for this type of multimedia information means that many aspects can be dynamically monitored, which can help implement value-added applications in the city. However, obtaining more elaborate semantic information from these data poses significant challenges related to a large amount of data generated and the processing capabilities required. This paper aims to address these issues by using a combination of cloud computing technologies and mobile computing techniques to design a three-layer distributed architecture for intensive urban computing. The approach consists of distributing the processing tasks among a city’s multimedia acquisition devices, a middle computing layer, known as a cloudlet, and a cloud-computing infrastructure. As a result, each part of the architecture can now focus on a small number of tasks for which they are specially designed, and data transmission communication needs are significantly reduced. To this end, the cloud server can hold and centralize the multimedia analysis of the processed results from the lower layers. Finally, a case study on smart lighting is described to illustrate the benefits of using the proposed model in smart city environments.

Revista: IEEE Access ( Volume: 7 ) 2019

Autores: Higinio Mora ; Jesús Peral ; Antonio Ferrández ; David Gil ; Julian Szymanski

URL: https://ieeexplore.ieee.org/document/8705293

A Review of the Analytics Techniques for an Efficient Management of Online Forums: An Architecture Proposal

Beatriz Botella — Wed, 16 Oct 2019 08:32:06 +0000

E-learning is a response to the new educational needs of society and an important development in information and communication technologies because it represents the future of the teaching and learning processes. However, this trend presents many challenges, such as the processing of online forums which generate a huge number of messages with an unordered structure and a great variety of topics. These forums provide an excellent platform for learning and connecting students of a subject but the difficulty of following and searching the vast volume of information that they generate may be counterproductive. The main goal of this paper is to review the approaches and techniques related to online courses in order to present a set of learning analytics techniques and a general architecture that solve the main challenges found in the state of the art by managing them in a more efficient way: 1) efficient tracking and monitoring of forums generated; 2) design of effective search mechanisms for questions and answers in the forums; and 3) extraction of relevant key performance indicators with the objective of carrying out an efficient management of online forums. In our proposal, natural language processing, clustering, information retrieval, question answering, and data mining techniques will be used.

Revista: IEEE Access, 7: 12220-12240. 2019

Autores: Peral, J.; Ferrández, A.; Mora, H.; Gil, D.; Kauffmann, E.

URL: https://ieeexplore.ieee.org/document/8612904