Extração de informação aplicada a comentários da área do turismo

Motivation: The primary motivation of this dissertation was to show that it is possible to construct an NLP solution for the Portuguese language capable of helping in the hotel industry. Objective(s): The main objective of this dissertation was to extract useful information from hotel commentaries using NLP. Method: An NLP pipeline was created to extract useful information, and then sentimental analyse was used to characterise that information. Results: After processing all the commentaries of a hotel was possible to extract what people like or dislike about it. Conclusions: The two main conclusions were that is possible to create a Portuguese NLP pipeline for the hotel industry, and that is possible to extract useful information from thousands of commentaries.Motivação: A principal motivação por trás desta tese foi mostrar que é possível escrever um programa para NLP usando a língua portuguesa. Objetivo(s): O principal objetivo desta tese foi extrair informação hotel dos comentários feitos a hotéis usando NLP. Método: Foi criado um pipeline de NLP para extrair informação útil. Depois foi usado análise de sentimentos para caracterizar essa informação. Resultados: Depois de todos os comentários serem processados foi possível descobrir o que as pessoas gostam ou desgostam sobre um hotel. Conclusões: As duas principais conclusões foram que era possível fazer NLP em português e que era possível extrair informação útil de milhar de comentários.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

The Detection of Contradictory Claims in Biomedical Abstracts

Research claims in the biomedical domain are not always consistent, and may even be contradictory. This thesis explores contradictions between research claims in order to determine whether or not it is possible to develop a solution to automate the detection of such phenomena. Such a solution will help decision-makers, including researchers, to alleviate the effects of contradictory claims on their decisions. This study develops two methodologies to construct corpora of contradictions. The first methodology utilises systematic reviews to construct a manually-annotated corpus of contradictions. The second methodology uses a different approach to construct a corpus of contradictions which does not rely on human annotation. This methodology is proposed to overcome the limitations of the manual annotation approach. Moreover, this thesis proposes a pipeline to detect contradictions in abstracts. The pipeline takes a question and a list of research abstracts which may contain answers to it. The output of the pipeline is a list of sentences extracted from abstracts which answer the question, where each sentence is annotated with an assertion value with respect to the question. Claims which feature opposing assertion values are considered as potentially contradictory claims. The research demonstrates that automating the detection of contradictory claims in research abstracts is a feasible problem

Detect and Classify -- Joint Span Detection and Classification for Health Outcomes

A health outcome is a measurement or an observation used to capture and assess the effect of a treatment. Automatic detection of health outcomes from text would undoubtedly speed up access to evidence necessary in healthcare decision making. Prior work on outcome detection has modelled this task as either (a) a sequence labelling task, where the goal is to detect which text spans describe health outcomes, or (b) a classification task, where the goal is to classify a text into a pre-defined set of categories depending on an outcome that is mentioned somewhere in that text. However, this decoupling of span detection and classification is problematic from a modelling perspective and ignores global structural correspondences between sentence-level and word-level information present in a given text. To address this, we propose a method that uses both word-level and sentence-level information to simultaneously perform outcome span detection and outcome type classification. In addition to injecting contextual information to hidden vectors, we use label attention to appropriately weight both word and sentence level information. Experimental results on several benchmark datasets for health outcome detection show that our proposed method consistently outperforms decoupled methods, reporting competitive results

Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications

Objective: Artificial intelligence (AI) provides people and professionals working in the field of participatory health informatics an opportunity to derive robust insights from a variety of online sources. The objective of this paper is to identify current state of the art and application areas of AI in the context of participatory health. Methods: A search was conducted across seven databases (PubMed, Embase, CINAHL, PsychInfo, ACM Digital Library, IEEExplore, and SCOPUS), covering articles published since 2013. Additionally, clinical trials involving AI in participatory health contexts registered at clinicaltrials.gov were collected and analyzed. Results: Twenty-two articles and 12 trials were selected for review. The most common application of AI in participatory health was the secondary analysis of social media data: self-reported data including patient experiences with healthcare facilities, reports of adverse drug reactions, safety and efficacy concerns about over-the-counter medications, and other perspectives on medications. Other application areas included determining which online forum threads required moderator assistance, identifying users who were likely to drop out from a forum, extracting terms used in an online forum to learn its vocabulary, highlighting contextual information that is missing from online questions and answers, and paraphrasing technical medical terms for consumers. Conclusions: While AI for supporting participatory health is still in its infancy, there are a number of important research priorities that should be considered for the advancement of the field. Further research evaluating the impact of AI in participatory health informatics on the psychosocial wellbeing of individuals would help in facilitating the wider acceptance of AI into the healthcare ecosystem

Sentence Classification with Hierarchical Neural Networks for Rhetorical Sections Extraction

Υπόβαθρο: Εκατομμύρια επιστημονικά άρθρα και επιστημονικές εργασίες δημοσιεύονται κάθε χρόνο, καθιστώντας την έρευνα για σχετική βιβλιογραφία όλο και πιο δύσκολη με κάθε μέρα που περνά. Ως εκ τούτου, οι σαφείς και ενημερωτικές περιλήψεις έχουν καταστεί απαραίτητο μέσο για να εντοπίζουν οι ερευνητές τις επιθυμητές πληροφορίες εγκαίρως και με αποτελεσματικό τρόπο. Πολλές περιλήψεις, ωστόσο, εξακολουθούν να στερούνται κοινών ρητορικών δομικών στοιχείων τα οποία θα βελτίωναν τους επικοινωνιακούς τους σκοπούς στο πλαίσιο του ακαδημαϊκού λόγου. Στόχος: Στην παρούσα διατριβή στοχεύουμε να εξετάσουμε την αποτελεσματικότητα των μοντέλων ταξινόμησης προτάσεων για την εξαγωγή ρητορικών ενοτήτων σε περιλήψεις διαφορετικών τομέων και δομών και να δημιουργήσουμε ένα εργαλείο που υτοματοποιεί αυτήν τη διαδικασία. Μέθοδος: Τα μοντέλα ταξινόμησης προτάσεων που χρησιμοποιήθηκαν εδώ βασίστηκαν σε ένα ιεραρχικό νευρωνικό δίκτυο (HNN) που έχει εκπαιδευτεί σε τρία διαφορετικά σύνολα δεδομένων. Αποτέλεσμα: Τα αποτελέσματά μας δείχνουν ότι τα μοντέλα μας επιβεβαιώνουν την ”state of the art” απόδοσή τους (SOTA) σε περιλήψεις του ίδιου επιστημονικού πεδίου με εκείνες που εκπαιδεύτηκαν, αλλά η διαπεδιακή ακρίβειά τους μειώνεται σημαντικά ειδικά όταν εφαρμόζονται σε μη κλασσικά δομημένες περιλήψεις. Συμπέρασμα: Ένα ακριβές εργαλείο για την απόκτηση των ρητορικών τμημάτων των περιλήψεων μπορεί να αποτελέσει τη βάση για ένα μεγαλύτερο σύστημα που θα μπορεί να συνοψίζει τις πληροφορίες, βοηθώντας έτσι σε μεγάλο βαθμό την επιτάχυνση της διαδικασίας της βιβλιογραφικής έρευνας.Background: Millions of scholarly articles and scientific papers are being published each year, making the search for relevant literature harder with each passing day. Clear and informative abstracts have therefore become an essential medium for researchers to locate their desired information in a timely and efficient manner. Many abstracts however, still lack common rhetorical structural elements that would improve their communicative purposes within the context of academic discourse. Objective: In the present thesis we aim to review the efficacy of sentence classification models for rhetorical sections extraction on abstracts of different domains and structures and create a tool that automates this process. Method: The sentence classification models used here were based on a hierarchical neural network (HNN) that has been trained on three different datasets. Result: Our results show that our models manage to confirm their state of the art (SOTA) performance on abstracts of the same scientific field with the ones they were trained in, but their inter­domain accuracy drops significantly especially when applied to unordinarily structured abstracts. Conclusion: An accurate tool for obtaining the rhetorical sections of abstracts can become the basis for a larger framework that could summarize information, helping tremendously to speed up the process of literature research