This paper introduces a novel methodology for Feature Selection for Functional Classification, FSFC, that addresses the challenge of jointly performing feature selection and classification of functional data in scenarios with categorical responses and multivariate longitudinal features. FSFC tackles a newly defined optimization problem that integrates logistic loss and functional features to identify the most crucial variables for classification. To address the minimization procedure, we employ functional principal components and develop a new adaptive version of the Dual Augmented Lagrangian algorithm. The computational efficiency of FSFC enables handling high-dimensional scenarios where the number of features may considerably exceed the number of statistical units. Simulation experiments demonstrate that FSFC outperforms other machine learning and deep learning methods in computational time and classification accuracy. Furthermore, the FSFC feature selection capability can be leveraged to significantly reduce the problem’s dimensionality and enhance the performances of other classification algorithms. The efficacy of FSFC is also demonstrated through a real data application, analyzing relationships between four chronic diseases and other health and demographic factors. FSFC source code is publicly available at https://github.com/IBM/funGCN.

Studies have shown that data retrieval and visualization tools can help health professionals to improve their understanding and communication with patients, their relationship with stakeholders, and their decision-making process. However, not many efforts have been made in this direction. In this paper, we present a prototype system for the indexing, annotation, and visualization of the PubMed knowledge base to enable the search and retrieval of health-related evidence. The proposed tool builds and keeps updated an enriched graph based on PubMed articles associating them with concepts extracted from the Unified Medical Language System (UMLS) Metathesaurus. Moreover, it allows a full-text search and graph-based navigation and supports an overview of concepts and related publications. The proposed architecture enables scale-up thanks to its containerized nature and parallelization capabilities. The code is open-source under the Apache V2 license.

Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, Debasis Ganguly. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

Due to the fast pace at which research reports in behaviour change are published, researchers, consultants and policymakers would benefit from more automatic ways to process these reports. Automatic extraction of the reports’ intervention content, population, settings and their results etc. are essential in synthesising and summarising the literature. However, to the best of our knowledge, no unique resource exists at the moment to facilitate this synthesis. In this paper, we describe the construction of a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation. We also describe and release the annotation of 57 entities, that can be used as an off-the-shelf data resource for tasks such as entity recognition, etc. Both the corpus and the annotation dataset are being made available to the community.

Different to traditional IR, which retrieves a set of topically relevant documents given a user query, we investigate causal retrieval, which involves retrieving a set of documents that describe a set of potential causes leading to an effect specified in the query. We argue that the nature of causal relevance should be different to that of traditional topical relevance. This is because although the causally relevant documents would have partial term overlap with the ones that are topically relevant for a query, yet it is expected that a majority of these documents would use a different set of terms to describe a number of causes possibly leading to their effects. To address this, we propose a feedback model to estimate a distribution of terms which are relatively infrequent but associated with high weights in the topically relevant distribution, leading to potential causal relevance. Our experiments demonstrate that such a feedback model turns out to be substantially more effective than traditional IR models and a number of other causality heuristic baselines.

While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult.The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard.In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards.Experiments show that our model outperforms several baselines by a large margin.Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.

In this paper a brief overview of the third workshop on Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction. The paper is focussing on the main aspects intended to be discussed in the workshop reflecting the main scope of the papers presented during the meeting. The MA3HMI 2018 workshop is held in conjunction with the 18th ACM International Conference on Mulitmodal Interaction (ICMI 2018) taking place in Boulder, USA, in October 2018. This year, we have solicited papers concerning the different phases of the development of multimodal systems. Tools and systems that address real-time conversations with artificial agents and technical systems are also within the scope.

In this paper a brief overview of the third workshop on Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction. The paper is focussing on the main aspects intended to be discussed in the workshop reflecting the main scope of the papers presented during the meeting. The MA3HMI 2016 workshop is held in conjunction with the 18th ACM International Conference on Mulitmodal Interaction (ICMI 2016) taking place in Tokyo, Japan, in November 2016. This year, we have solicited papers concerning the different phases of the development of multimodal systems. Tools and systems that address real-time conversations with artificial agents and technical systems are also within the scope.

The dynamics of social interactions during human-human conversation have attracted the attention of many researchers aiming at improving the naturalness of automatic dialogue systems and creating socially aware machines.While many studies have investigate social dynamics in relation to the emotional sphere of a conversation, in this work we investigate whether these dynamics are correlated with the discourse structure, particularly topic changes.We conduct an observational study in two corpora of differing nature.In both corpora we find that at topic terminations, the interactional entropy, defined as the amount of social signals exchanged by the participants, decreases with the introduction of a new topic.We conclude that a relation exists between topic changes and amount of social interaction and, hence, that social signals have a discourse value in addition to a social function.This value, we suggest, is group acknowledgement that a new topic has gained the floor.Understanding these social dynamics within conversations can be exploited in the development of socially aware dialogue systems.

Conversational interaction is a dynamic activity in which participants engage in the construction of meaning and in establishing and maintaining social relationships. Lexical and prosodic accommodation have been observed in many studies as contributing importantly to these dimensions of social interaction. However, while previous works have considered accommodation mechanisms at global levels (for whole conversations, halves and thirds of conversations), this work investigates their evolution through repeated analysis at time intervals of increasing granularity to analyze the dynamics of alignment in a spoken language corpus. Results show that the levels of both prosodic and lexical accommodation fluctuate several times over the course of a conversation.