David Martins de Matos

@ulisboa.pt

Department of Computer Science and Engineering
INESC-ID, Instituto Superior Técnico, Universidade de Lisboa

David Martins de Matos


                        Download Compact PDF  
https://researchid.co/david.m.matos
80

Scopus Publications

1372

Scholar Citations

20

Scholar h-index

41

Scholar i10-index

Scopus Publications

  • Advancing the Prediction and Understanding of Placebo Responses in Chronic Back Pain Using Large Language Models
    Diogo A. P. Nunes, Dan Furrer, Sara Berger, Guillermo Cecchi, Joana Ferreira‐Gomes, Fani Neto, David Martins de Matos, A. Vania Apkarian, Paulo Branco
    European Journal of Pain, 2026
    Background Placebo analgesia is a widely studied clinical phenomenon, yet placebo responses vary widely across individuals. Prior research has identified biopsychosocial factors that determine the likelihood of an individual to respond to placebo, yet generalizability and ecological validity in those studies have been limited due to the inability to account for dynamic personal and treatment effects. Methods We assessed fine‐tuned large language models (LLMs) for the prediction of placebo responses in chronic low‐back pain using contextual features extracted from patient interviews, as they speak about their lifestyle, pain, and treatment history. Interviews were conducted as part of two RCTs designed to study the placebo effect. These interviews were collected after treatment in the first trial (discovery cohort) and prior to treatment in the second trial (validation cohort). Results Semantic features extracted with LLMs can predict which individuals respond to a placebo, with an accuracy of 74% in unseen data, and validating with 70% accuracy in an independent cohort. Furthermore, in contrast to previous work, LLMs eliminated the need for pre‐selecting search terms, enabling a fully data‐driven approach, and provided interpretable insights into psychosocial factors underlying placebo responses. Conclusions These findings expand on prior research by integrating state‐of‐art NLP techniques to address limitations in interpretability and context sensitivity of the traditional methods in related work. This method highlights the role of language models to link language and psychological states, paving the way for a deeper quantitative exploration of biopsychosocial phenomena, and to understand how they relate to treatment outcomes. Significance Statement This study paves the way for a deeper yet quantitative exploration of biopsychosocial phenomena through language, and to understand how they relate to treatment outcomes, namely placebo. In this case it highlights nuanced linguistic patterns linked to responder status, which tap into semantic dimensions such as “anxiety,” “resignation,” and “hope”.
  • Acoustic and Linguistic Biomarkers for Cognitive Impairment Detection from Speech
    Catarina Botelho, David Gimeno-Gómez, Francisco Teixeira, John Mendonça, Patrícia Pereira, Diogo A. P. Nunes, Thomas Rolland, Anna Pompili, Rubén Solera-Ureña, Maria Ponte, David Martins de Matos, Carlos-D. Martínez-Hinarejos, Isabel Trancoso, Alberto Abad
    Proceedings of the Annual Conference of the International Speech Communication Association Interspeech, 2025
  • Automatic Recognition of the General-Purpose Communicative Functions Defined by the ISO 24617-2 Standard for Dialog Act Annotation (Extended Abstract)
    Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
    Ijcai International Joint Conference on Artificial Intelligence, 2023
    From the perspective of a dialog system, the identification of the intention behind the segments in a dialog is important, as it provides cues regarding the information present in the segments and how they should be interpreted. The ISO 24617-2 standard for dialog act annotation defines a hierarchically organized set of general-purpose communicative functions that correspond to different intentions that are relevant in the context of a dialog. In this paper, we explore the automatic recognition of these functions. To do so, we propose to adapt existing approaches to dialog act recognition, so that they can deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Additionally, we rely on transfer learning processes to address the data scarcity problem. Our experiments on the DialogBank show that this approach outperforms both flat and hierarchical approaches based on multiple classifiers and that each of its components plays an important role in the recognition of general-purpose communicative functions.
  • Chronic Pain Patient Narratives Allow for the Estimation of Current Pain Intensity
    Diogo A.P. Nunes, Joana Ferreira-Gomes, Daniela Oliveira, Carlos Vaz, Sofia Pimenta, Fani Neto, David Martins de Matos
    Proceedings IEEE Symposium on Computer Based Medical Systems, 2023
    We demonstrate a proof-of-concept for the analysis of the language of chronic pain for pain intensity estimation. Importantly, we show that focus on specific words/themes is especially correlated with specific pain intensity categories. We interviewed chronic pain patients and collected demographic and clinical data. 65 patients (40 females), averaging $\\mathbf{56.4} \\pm \\mathbf{12.7}$ years of age, participated in the study. Patients reported their current pain intensity on a Visual Analogue Scale, which we discretized into 3 classes: mild, moderate, and severe pain. We extracted language features from the transcribed interview of each patient and used them to classify their pain intensity category. We measured performance with the weighted $\\mathbf{F}_{\\mathbf{1}}$ score. Finally, we analyzed potential confounding variables for internal validity. The best performing model was the Support Vector Machine with an Early Fusion of select language features, with an $\\mathbf{F}_{\\mathbf{1}}$ of 0.60, improving 39.5% upon the baseline. Patients with mild pain focused more on verbs, whilst moderate and severe pain patients focused on adverbs, and nouns and adjectives, respectively. We show that language features from patient narratives indeed convey information relevant for pain intensity estimation, and that our models can take advantage of that.
  • Learning Low-Dimensional Semantics for Music and Language via Multi-Subject fMRI
    Francisco Afonso Raposo, David Martins de Matos, Ricardo Ribeiro
    Neuroinformatics, 2022
    Embodied Cognition (EC) states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience, making this biological semantic machinery noisy with respect to semantics inherent to media, such as music and language. We propose to represent media semantics using low-dimensional vector embeddings by jointly modeling the functional Magnetic Resonance Imaging (fMRI) activity of several brains via Generalized Canonical Correlation Analysis (GCCA). We evaluate the semantic richness of the resulting latent space in appropriate semantic classification tasks: music genres and language topics. We show that the resulting unsupervised representations outperform the original high-dimensional fMRI voxel spaces in these downstream tasks while being more computationally efficient. Furthermore, we show that joint modeling of several subjects increases the semantic richness of the learned latent vector spaces as the number of subjects increases. Quantitative results and corresponding statistical significance testing demonstrate the instantiation of music and language semantics in the brain, thereby providing further evidence for multimodal embodied cognition as well as a method for extraction of media semantics from multi-subject brain dynamics.
  • Automatic Recognition of the General-Purpose Communicative Functions defined by the ISO 24617-2 Standard for Dialog Act Annotation
    Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
    Journal of Artificial Intelligence Research, 2022
    From the perspective of a dialog system, it is important to identify the intention behind the segments in a dialog, since it provides an important cue regarding the information that is present in the segments and how they should be interpreted. ISO 24617-2, the standard for dialog act annotation, defines a hierarchically organized set of general-purpose communicative functions which correspond to different intentions that are relevant in the context of a dialog. We explore the automatic recognition of these communicative functions in the DialogBank, which is a reference set of dialogs annotated according to this standard. To do so, we propose adaptations of existing approaches to flat dialog act recognition that allow them to deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Furthermore, since the amount of dialogs in the DialogBank is small, we rely on transfer learning processes to reduce overfitting and improve performance. The results of our experiments show that our approach outperforms both a flat one and hierarchical approaches based on multiple classifiers and that each of its components plays an important role towards the recognition of general-purpose communicative functions.
  • Assessing kinetic meaning of music and dance via deep cross-modal retrieval
    Francisco Afonso Raposo, David Martins de Matos, Ricardo Ribeiro
    Neural Computing and Applications, 2021
    Music semantics is embodied, in the sense that meaning is biologically mediated by and grounded in the human body and brain. This embodied cognition perspective also explains why music structures modulate kinetic and somatosensory perception. We explore this aspect of cognition, by considering dance as an overt expression of semantic aspects of music related to motor intention, in an artificial deep recurrent neural network that learns correlations between music audio and dance video. We claim that, just like human semantic cognition is based on multimodal statistical structures, joint statistical modeling of music and dance artifacts is expected to capture semantics of these modalities. We evaluate the ability of this model to effectively capture underlying semantics in a cross-modal retrieval task, including dance styles in an unsupervised fashion. Quantitative results, validated with statistical significance testing, strengthen the body of evidence for embodied cognition in music and demonstrate the model can recommend music audio for dance video queries and vice versa.
  • MIRES: Recovering mobile applications based on backend-as-a-service from cyber attacks
    Diogo Vaz, David Matos, Miguel Pardal, Miguel Correia
    ACM International Conference Proceeding Series, 2020
    Many popular mobile applications rely on the Backend-as-a-Service (BaaS) cloud computing model to simplify the development and management of services like data storage, user authentication and notifications. However, vulnerabilities and other issues may lead to malicious operations on the mobile application client-side and malicious requests being sent to the backend, corrupting the state of the application in the cloud. To deal with these attacks after they happen and are successful, it is necessary to remove the immediate effects created by the malicious requests and subsequent effects derived from later requests. In this paper, we present MIRES, an intrusion recovery service for mobile applications based on BaaS. MIRES uses a two-phase recovery process that restores the integrity of the mobile application and minimizes its unavailability. We implemented MIRES in Android and with the Firebase platform and made experiments with 3 mobile applications that showed results of 1000 operations reverted in less than 1 minute and with the mobile application inaccessible only for less than 15 seconds.
  • Semantic frame induction through the detection of communities of verbs and their arguments
    Eugénio Ribeiro, Andreia Sofia Teixeira, Ricardo Ribeiro, David Martins de Matos
    Applied Network Science, 2020
    Resources such as FrameNet, which provide sets of semantic frame definitions and annotated textual data that maps into the evoked frames, are important for several NLP tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame and verb arguments that play the same semantic role. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances or arguments as nodes connected by edges if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of SemEval 2019, we outperformed all of the previous approaches to the task, achieving the current state-of-the-art performance.
  • Pragmatic Aspects of Discourse Production for the Automatic Identification of Alzheimer's Disease
    Anna Pompili, Alberto Abad, David Martins de Matos, Isabel Pavao Martins
    IEEE Journal on Selected Topics in Signal Processing, 2020
    Clinical literature provides convincing evidence that language deficits in Alzheimer's disease (AD) allow for distinguishing patients with dementia from healthy subjects. Currently, computational approaches have widely investigated lexicosemantic aspects of discourse production, while pragmatic aspects like cohesion and coherence, are still mostly unexplored. In this article, we aim at providing a more comprehensive characterization of language abilities for the automatic identification of AD in narrative description tasks by also incorporating pragmatic aspects of speech production. To this end, we investigate the relevance of a recently proposed set of pragmatic features extracted from an automatically generated topic hierarchy graph in combination with a complementary set of state-of-the-art features encoding lexical, syntactic and semantic cues. Experimental results on the DementiaBank corpus show an accuracy improvement from 82.6% to 85.5% in identifying AD patients when pragmatic features are incorporated to the set of lexicosemantic features. Nevertheless, these results are obtained relying on manual transcriptions, which strongly limits the applicability of computational analysis to clinical settings. Thus, in this work we additionally carry out an analysis of the errors introduced by a speech recognition system and the way in which they impact the performance of the proposed method. In spite of the high word error rates obtained on these data (∼40%), automatic AD identification accuracy decreased only to 79.7%, which is considered a remarkable result when compared with solutions based on manual transcriptions.
  • Mapping the dialog act annotations of the LEGO corpus into ISO 24617-2 communicative functions
    Lrec 2020 12th International Conference on Language Resources and Evaluation Conference Proceedings, 2020
  • Semantic Frame Induction as a Community Detection Problem
    Eugénio Ribeiro, Andreia Sofia Teixeira, Ricardo Ribeiro, David Martins de Matos
    Studies in Computational Intelligence, 2020
  • MultiTLS: Secure Communication Channels with Cipher Suite Diversity
    Ricardo Moura, David R. Matos, Miguel L. Pardal, Miguel Correia
    IFIP Advances in Information and Communication Technology, 2020
  • Deep dialog act recognition using multiple token, segment, and context information representations
    Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
    Journal of Artificial Intelligence Research, 2019
  • Learning Multimodal Representations for Sample-efficient Recognition of Human Actions
    Miguel Vasco, Francisco S. Melo, David Martins de Matos, Ana Paiva, Tetsunari Inamura
    IEEE International Conference on Intelligent Robots and Systems, 2019
  • An information-theoretic approach to machine-oriented music summarization
    Francisco Afonso Raposo, David Martins de Matos, Ricardo Ribeiro
    Pattern Recognition Letters, 2019
  • A multilingual and multidomain study on dialog act recognition using character-level tokenization
    Eugénio Ribeiro, Ricardo Ribeiro, David de Matos
    Information Switzerland, 2019
  • Hierarchical multi-label dialog act recognition on Spanish data
    Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
    Linguamatica, 2019
  • Online motion concept learning: A novel algorithm for sample-efficient learning and recognition of human actions
    Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas, 2019
  • L2F/INESC-ID at SemEval-2019 task 2: Unsupervised lexical semantic frame induction using contextualized word representations
    Eugénio Ribeiro, Vânia Mendonça, Ricardo Ribeiro, David Martins de Matos, Alberto Sardinha, Ana Lúcia Santos, Luísa Coheur
    Naacl Hlt 2019 International Workshop on Semantic Evaluation Semeval 2019 Proceedings of the 13th Workshop, 2019
  • RockfS: Cloud-backed file system resilience to client-side attacks
    David R. Matos, Miguel L. Pardal, Georg Carle, Miguel Correia
    Proceedings of the 19th International Middleware Conference Middleware 2018, 2018
  • A 'Deeper' Look at Detecting Cyberbullying in Social Networks
    Hugo Rosa, David Matos, Ricardo Ribeiro, Luisa Coheur, Joao P. Carvalho
    Proceedings of the International Joint Conference on Neural Networks, 2018
  • Securing electronic health records in the cloud
    David R. Matos, Miguel L. Pardal, Pedro Adão, António Rito Silva, Miguel Correia
    Proceedings of the Workshop on Privacy by Design in Distributed Systems P2ds 2018 Co Located with European Conference on Computer Systems Eurosys 2018, 2018
  • End-to-End Multi-Level Dialog Act Recognition
    Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
    4th International Conference Iberspeech 2018, 2018
  • A study on dialog act recognition using character-level tokenization
    Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
  • Topic coherence analysis for the classification of Alzheimer's disease
    Anna Pompili, Alberto Abad, David Martins de Matos, Isabel Pavão Martins
    4th International Conference Iberspeech 2018, 2018
  • Rectify: Black-box intrusion recovery in paas clouds
    David R. Matos, Miguel L. Pardal, Miguel Correia
    Middleware 2017 Proceedings of the 2017 International Middleware Conference, 2017
  • Event-based summarization using a centrality-as-relevance model
    Luís Marujo, Ricardo Ribeiro, Anatole Gershman, David Martins de Matos, João P. Neto, Jaime Carbonell
    Knowledge and Information Systems, 2017
  • A Curiosity Model for Artificial Agents
    Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
    Proceedings of the 14th European Conference on Artificial Life Ecal 2017, 2017
  • NoSQL Undo: Recovering NoSQL databases by undoing operations
    David Matos, Miguel Correia
    Proceedings 2016 IEEE 15th International Symposium on Network Computing and Applications NCA 2016, 2016
  • Using Generic Summarization to Improve Music Information Retrieval Tasks
    Francisco Raposo, Ricardo Ribeiro, David Martins de Matos
    IEEE ACM Transactions on Audio Speech and Language Processing, 2016
  • Evaluating pictogram prediction in a location-aware augmentative and alternative communication system
    Luís Filipe Garcia, Luís Caldas de Oliveira, David Martins de Matos
    Assistive Technology, 2016
  • Summarization of films and documentaries based on subtitles and scripts
    Marta Aparício, Paulo Figueiredo, Francisco Raposo, David Martins de Matos, Ricardo Ribeiro, Luís Marujo
    Pattern Recognition Letters, 2016
  • Exploring events and distributed representations of text in multi-document summarization
    Luís Marujo, Wang Ling, Ricardo Ribeiro, Anatole Gershman, Jaime Carbonell, David Martins de Matos, João P. Neto
    Knowledge Based Systems, 2016
  • Automatic detection of hyperarticulated speech
    Eugénio Ribeiro, Fernando Batista, Isabel Trancoso, Ricardo Ribeiro, David Martins de Matos
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016
  • Assessing user expertise in spoken dialog system interactions
    Eugénio Ribeiro, Fernando Batista, Isabel Trancoso, José Lopes, Ricardo Ribeiro, David Martins de Matos
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016
  • SPA: Web-based Platform for easy Access to Speech Processing Modules
    Proceedings of the 10th International Conference on Language Resources and Evaluation Lrec 2016, 2016
  • Spatial role labeling with convolutional neural networks
    Alexey Mazalov, Bruno Martins, David Matos
    ACM International Conference Proceeding Series, 2015
  • Multicore SIMD ASIP for Next-Generation Sequencing and Alignment Biochip Platforms
    Nuno Neves, Nuno Sebastiao, David Matos, Pedro Tomas, Paulo Flores, Nuno Roma
    IEEE Transactions on Very Large Scale Integration VLSI Systems, 2015
  • Measuring the performance of a location-aware text prediction system
    Luís Filipe Garcia, Luís Caldas De Oliveira, David Martins De Matos
    ACM Transactions on Accessible Computing, 2015
  • On the application of generic summarization algorithms to music
    Francisco Raposo, Ricardo Ribeiro, David Martins de Matos
    IEEE Signal Processing Letters, 2015
  • Textual event detection using fuzzy fingerprints
    Luís Marujo, Joao Paulo Carvalho, Anatole Gershman, Jaime Carbonell, João P. Neto, David Martins de Matos
    Advances in Intelligent Systems and Computing, 2015
  • Extending a single-document summarizer to multi-document: A hierarchical approach
    Luis Marujo, Ricardo Ribeiro, David Martins de Matos, João Neto, Anatole Gershman, Jaime Carbonell
    Proceedings of the 4th Joint Conference on Lexical and Computational Semantics Sem 2015, 2015
  • Automatic keyword extraction on twitter
    Luis Marujo, Wang Ling, Isabel Trancoso, Chris Dyer, Alan W Black, Anatole Gershman, David Martins de Matos, João Neto, Jaime Carbonell
    Acl Ijcnlp 2015 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing Proceedings of the Conference, 2015
  • Word and sentence prediction: Using the best of the two worlds to assist AAC users
    Luís Garcia, Luis de Oliveira, David de Matos
    Technology and Disability, 2014
  • Privacy-preserving important passage retrieval
    Ceur Workshop Proceedings, 2014
  • Revising the annotation of a Broadcast News corpus: A linguistic approach
    Proceedings of the 9th International Conference on Language Resources and Evaluation Lrec 2014, 2014
  • Word and sentence prediction: Using the best of the two worlds to assist AAC users
    Garcia Luís, Oliveira Luis, Matos David
    Assistive Technology Research Series, 2013
  • Revisiting centrality-as-relevance: Support sets and similarity as geometric proximity
    Ijcai International Joint Conference on Artificial Intelligence, 2013
  • Understanding temporal dynamics of ratings in the book recommendation scenario
    Paula Cristina Vaz, Ricardo Ribeiro, David Martins de Matos
    ACM International Conference Proceeding Series, 2013
  • Self reinforcement for important passage retrieval
    Ricardo Ribeiro, Luís Marujo, David Martins de Matos, João P. Neto, Anatole Gershman, Jaime Carbonell
    SIGIR 2013 Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013
  • Understanding the temporal dynamics of recommendations across different rating scales
    Ceur Workshop Proceedings, 2013
  • BioBlaze: Multi-core SIMD ASIP for DNA sequence alignment
    Nuno Neves, Nuno Sebastiao, Andre Patricio, David Matos, Pedro Tomas, Paulo Flores, Nuno Roma
    Proceedings of the International Conference on Application Specific Systems Architectures and Processors, 2013
  • Stylometric relevance-feedback towards a hybrid book recommendation algorithm
    Paula Cristina Vaz, David Martins de Matos, Bruno Martins
    International Conference on Information and Knowledge Management Proceedings, 2012
  • LitRec vs. Movielens: A comparative study
    Kdir 2012 Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, 2012
  • Key phrase extraction of lightly filtered broadcast news
    Luís Marujo, Ricardo Ribeiro, David Martins de Matos, João P. Neto, Anatole Gershman, Jaime Carbonell
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
  • Comparing Onset Detection methods based on spectral features
    Carlos Rosão, Ricardo Ribeiro, David Martins de Matos
    ACM International Conference Proceeding Series, 2012
  • Improving a hybrid literary book recommendation system through author ranking
    Paula Cristina Vaz, David Martins de Matos, Bruno Martins, Pavel Calado
    Proceedings of the ACM IEEE Joint Conference on Digital Libraries, 2012
  • Summarizing speech by contextual reinforcement of important passages
    Ricardo Ribeiro, David Martins de Matos
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
  • Searching a mixed corpus in the light of the new Portuguese orthographic norm
    Gracinda Carvalho, Isabel Falé, David Martins de Matos, Vitor Rocio
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
  • Influence of Peak Selection methods on onset detection
    Proceedings of the 13th International Society for Music Information Retrieval Conference Ismir 2012, 2012
  • Building and exploring semantic equivalences resources
    Proceedings of the 8th International Conference on Language Resources and Evaluation Lrec 2012, 2012
  • Testing lexical approaches in QA4MRE
    Ceur Workshop Proceedings, 2012
  • Revisiting centrality-as-relevance: Support sets and similarity as geometric proximity
    Ricardo Ribeiro, David Martins de Matos
    Journal of Artificial Intelligence Research, 2011
  • Improving domotic services combining a dialog system and a resident tracking system
    David Antunes, David Martins de Matos, Jose Gaspar
    Eurocon 2011 International Conference on Computer as A Tool Joint with Conftele 2011, 2011
  • Multiple Hypothesis Tracking in camera networks
    David M. Antunes, Dario Figueira, David M. Matos, Alexandre Bernardino, Jose Gaspar
    Proceedings of the IEEE International Conference on Computer Vision, 2011
  • Discriminative Phrase-based Lexicalized Reordering Models using Weighted Reordering Graphs
    Ijcnlp 2011 Proceedings of the 5th International Joint Conference on Natural Language Processing, 2011
  • Improving IdSay: A characterization of strengths and weaknesses in question answering systems for Portuguese
    Gracinda Carvalho, David Martins de Matos, Vitor Rocio
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
  • Fairy tale corpus organization using latent semantic mapping and an item-to-item top-n recommendation algorithm
    Proceedings of the 7th International Conference on Language Resources and Evaluation Lrec 2010, 2010
  • High-performance high-volume layered corpora annotation
    Tiago Luís, David Martins de Matos
    Acl Ijcnlp 2009 Law 2009 3rd Linguistic Annotation Workshop Proceedings, 2009
  • IdSay: Question answering for portuguese
    Gracinda Carvalho, David Martins de Matos, Vitor Rocio
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
  • Using prior knowledge to assess relevance in speech summarization
    Ricardo Ribeiro, David Martins de Matos
    2008 IEEE Workshop on Spoken Language Technology Slt 2008 Proceedings, 2008
  • IdSay: Question Answering for Portuguese
    Ceur Workshop Proceedings, 2008
  • Using lexical acquisition to enrich a predicate argument reusable database
    Proceedings of the 6th International Conference on Language Resources and Evaluation Lrec 2008, 2008
  • QA@L2F, first steps at QA@CLEF
    Ana Mendes, Luísa Coheur, Nuno J. Mamede, Ricardo Ribeiro, Fernando Batista, David Martins de Matos
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
  • Extractive summarization of broadcast news: Comparing strategies for European portuguese
    Ricardo Ribeiro, David Martins de Matos
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
  • Document retrieval for question answering: A quantitative evaluation of text preprocessing
    Gracinda Carvalho, David Martins de Matos, Vitor Rocio
    International Conference on Information and Knowledge Management Proceedings, 2007
  • QA@L2F@QA@CLEF
    Ceur Workshop Proceedings, 2007
  • Rethinking reusable resources
    Proceedings of the 4th International Conference on Language Resources and Evaluation Lrec 2004, 2004
  • Managing linguistic resources and tools
    David M. de Matos, Joana L. Paulo, Nuno J. Mamede
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2003

RECENT SCHOLAR PUBLICATIONS

  • GroundCap: A visually grounded image captioning dataset with object and action identification
    DAP Oliveira, L Teodoro, DM de Matos
    Pattern Recognition, 113703 , 2026
    2026
  • StoryMovie: A Dataset for Semantic Alignment of Visual Stories with Movie Scripts and Subtitles
    D Oliveira, DM de Matos
    arXiv preprint arXiv:2602.21829 , 2026
    2026
  • Advancing the prediction and understanding of placebo responses in chronic back pain using large language models
    DAP Nunes, D Furrer, S Berger, G Cecchi, J Ferreira‐Gomes, F Neto, ...
    European Journal of Pain 30 (1), e70184 , 2026
    2026
    Citations: 4
  • Story generation from visual inputs: Techniques, related tasks, and challenges
    DAP Oliveira, E Ribeiro, D Martins de Matos
    Information 16 (9), 812 , 2025
    2025
    Citations: 10
  • Entity Re-identification in Visual Storytelling via Contrastive Reinforcement Learning
    DAP Oliveira, DM de Matos
    arXiv preprint arXiv:2507.07340 , 2025
    2025
  • StoryReasoning dataset: Using chain-of-thought for scene understanding and grounded story generation
    DAP Oliveira, DM de Matos
    arXiv preprint arXiv:2505.10292 , 2025
    2025
    Citations: 5
  • GroundCap: A visually grounded image captioning dataset
    DAP Oliveira, L Teodoro, DM de Matos
    arXiv preprint arXiv:2502.13898 , 2025
    2025
    Citations: 7
  • Acoustic and linguistic biomarkers for cognitive impairment detection from speech
    C Botelho, D Gimeno-Gómez, F Teixeira, J Mendonça, P Pereira, ...
    Proc. Interspeech 2025, 1418-1422 , 2025
    2025
    Citations: 4
  • Tackling cognitive impairment detection from speech: A submission to the process challenge
    C Botelho, D Gimeno-Gómez, F Teixeira, J Mendonça, P Pereira, ...
    arXiv preprint arXiv:2501.00145 , 2024
    2024
    Citations: 3
  • Computational analysis of the language of pain: a systematic review
    DAP Nunes, J Ferreira-Gomes, F Neto, DM de Matos
    arXiv preprint arXiv:2404.16226 , 2024
    2024
    Citations: 3
  • Chronic pain patient narratives allow for the estimation of current pain intensity
    DAP Nunes, J Ferreira-Gomes, D Oliveira, C Vaz, S Pimenta, F Neto, ...
    2023 IEEE 36th International Symposium on Computer-Based Medical Systems … , 2023
    2023
    Citations: 5
  • Modeling chronic pain experiences from online reports using the Reddit reports of chronic pain dataset
    DAP Nunes, J Ferreira-Gomes, F Neto, D Martins de Matos
    Information 14 (4), 237 , 2023
    2023
    Citations: 9
  • Modeling Chronic Pain Experiences from Online Reports Using the Reddit Reports of Chronic Pain Dataset. Information 2023, 14, 237
    DAP Nunes, J Ferreira-Gomes, F Neto, D Martins de Matos
    2023
    Citations: 1
  • Transfer-learning for video classification: Video Swin Transformer on multiple domains
    D Oliveira, DM de Matos
    arXiv preprint arXiv:2210.09969 , 2022
    2022
    Citations: 2
  • Learning low-dimensional semantics for music and language via multi-subject fMRI
    FA Raposo, D Martins de Matos, R Ribeiro
    Neuroinformatics 20 (2), 451-461 , 2022
    2022
    Citations: 3
  • Towards Learning Through Open-Domain Dialog
    E Ribeiro, R Ribeiro, DM de Matos
    arXiv preprint arXiv:2202.03040 , 2022
    2022
  • Automatic recognition of the general-purpose communicative functions defined by the ISO 24617-2 standard for dialog act annotation
    E Ribeiro, R Ribeiro, DM De Matos
    Journal of Artificial Intelligence Research 73, 397–436-397–436 , 2022
    2022
    Citations: 7
  • Active Learning Improves the Teacher's Experience: A Case Study in a Language Grounding Scenario.
    F Reynaud, E Ribeiro, DM de Matos
    IberSPEECH, 141-145 , 2022
    2022
  • Assessing kinetic meaning of music and dance via deep cross-modal retrieval
    FA Raposo, D Martins de Matos, R Ribeiro
    Neural Computing and Applications 33 (21), 14481-14493 , 2021
    2021
    Citations: 9
  • Chronic pain and language: A topic modelling approach to personal pain descriptions
    DAP Nunes, JF Gomes, F Neto, DM de Matos
    arXiv preprint arXiv:2109.00402 , 2021
    2021
    Citations: 8

MOST CITED SCHOLAR PUBLICATIONS

  • A" Deeper" Look at Detecting Cyberbullying in Social Networks.
    H Rosa, DM de Matos, R Ribeiro, L Coheur, JP Carvalho
    IJCNN, 1-8 , 2018
    2018
    Citations: 94
  • Automatic keyword extraction on twitter
    L Marujo, W Ling, I Trancoso, C Dyer, AW Black, A Gershman, ...
    Proceedings of the 53rd Annual Meeting of the Association for Computational … , 2015
    2015
    Citations: 86
  • Improving a hybrid literary book recommendation system through author ranking
    PC Vaz, D Martins de Matos, B Martins, P Calado
    Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries … , 2012
    2012
    Citations: 78
  • Exploring events and distributed representations of text in multi-document summarization
    L Marujo, W Ling, R Ribeiro, A Gershman, J Carbonell, DM De Matos, ...
    Knowledge-Based Systems 94, 33-42 , 2016
    2016
    Citations: 48
  • The influence of context on dialogue act recognition
    E Ribeiro, R Ribeiro, DM de Matos
    arXiv preprint arXiv:1506.00839 , 2015
    2015
    Citations: 42
  • Pragmatic aspects of discourse production for the automatic identification of Alzheimer's disease
    A Pompili, A Abad, DM De Matos, IP Martins
    IEEE Journal of Selected Topics in Signal Processing 14 (2), 261-271 , 2020
    2020
    Citations: 40
  • Multicore SIMD ASIP for next-generation sequencing and alignment biochip platforms
    N Neves, N Sebastião, D Matos, P Tomás, P Flores, N Roma
    IEEE Transactions on Very Large Scale Integration (VLSI) Systems 23 (7 … , 2014
    2014
    Citations: 39
  • Summarization of films and documentaries based on subtitles and scripts
    M Aparício, P Figueiredo, F Raposo, DM De Matos, R Ribeiro, L Marujo
    Pattern Recognition Letters 73, 7-12 , 2016
    2016
    Citations: 34
  • Centrality-as-relevance: Support sets and similarity as geometric proximity
    R Ribeiro, DM de Matos
    Journal of Artificial Intelligence Research 42, 275-308 , 2011
    2011
    Citations: 34
  • Fairy Tale Corpus Organization Using Latent Semantic Mapping and an Item-to-item Top-n Recommendation Algorithm.
    PV Lobo, DM De Matos
    LREC 10, 1472-1475 , 2010
    2010
    Citations: 32
  • Using generic summarization to improve music information retrieval tasks
    F Raposo, R Ribeiro, DM De Matos
    IEEE/ACM Transactions on Audio, Speech, and Language Processing 24 (6), 1119 … , 2016
    2016
    Citations: 29
  • Influence of Peak Selection Methods on Onset Detection.
    C Rosao, R Ribeiro, DM De Matos
    ismir, 517-522 , 2012
    2012
    Citations: 29
  • Understanding temporal dynamics of ratings in the book recommendation scenario
    PC Vaz, R Ribeiro, DM De Matos
    Proceedings of the 2013 international conference on information systems and … , 2013
    2013
    Citations: 25
  • Deep dialog act recognition using multiple token, segment, and context information representations
    E Ribeiro, R Ribeiro, DM De Matos
    Journal of Artificial Intelligence Research 66, 861-899 , 2019
    2019
    Citations: 24
  • L2F/INESC-ID at SemEval-2019 task 2: Unsupervised lexical semantic frame induction using contextualized word representations
    E Ribeiro, V Mendonça, R Ribeiro, DM de Matos, A Sardinha, AL Santos, ...
    Proceedings of the 13th International Workshop on Semantic Evaluation, 130-136 , 2019
    2019
    Citations: 24
  • Spatial role labeling with convolutional neural networks
    A Mazalov, B Martins, D Matos
    Proceedings of the 9th Workshop on Geographic Information Retrieval, 1-7 , 2015
    2015
    Citations: 23
  • Document retrieval for question answering: a quantitative evaluation of text preprocessing
    G Carvalho, DM De Matos, V Rocio
    Proceedings of the ACM first Ph. D. workshop in CIKM, 125-130 , 2007
    2007
    Citations: 23
  • Stylometric relevance-feedback towards a hybrid book recommendation algorithm
    PC Vaz, D Martins de Matos, B Martins
    Proceedings of the fifth acm workshop on research advances in large digital … , 2012
    2012
    Citations: 22
  • Extractive summarization of broadcast news: Comparing strategies for european portuguese
    R Ribeiro, DM De Matos
    International Conference on Text, Speech and Dialogue, 115-122 , 2007
    2007
    Citations: 22
  • Evaluating pictogram prediction in a location-aware augmentative and alternative communication system
    LF Garcia, LC de Oliveira, DM de Matos
    Assistive Technology 28 (2), 83-92 , 2016
    2016
    Citations: 20