Adaptation of LIMSI's QALC for QA4MRE.

International audienceIn this paper, we present LIMSI participation to one of the pilot tasks of QA4MRE at CLEF 2012: Machine Reading of Biomedical Texts about Alzheimer. For this exercise, we adapted an existing question answering (QA) system, QALC, by searching answers in the reading document. This basic version was used for the evaluation and obtains 0.2, which was increased to 0.325 after basic corrections. We developed then different methods for choosing an answer, based on the expected answer type and the question plus answer rewritten to form hypothesis compared with candidates sentences. We also conducted studies on relation extraction by using an existing system. The last version of our system obtains 0.375

NEGATION TRIGGERS AND THEIR SCOPE

Recent interest in negation has resulted in a variety of different annotation schemes for different application tasks, several vetted in shared task competitions. Current negation detection systems are trained and tested for a specific application task within a particular domain. The availability of a robust, general negation detection module that can be added to any text processing pipeline is still missing. In this work we propose a linguistically motivated trigger and scope approach for negation detection in general. The system, NEGATOR, introduces two baseline modules: the scope module to identify the syntactic scope for different negation triggers and a variety of trigger lists evaluated for that purpose, ranging from minimal to extensive. The scope module consists of a set of specialized transformation rules that determine the scope of a negation trigger using dependency graphs from parser output. NEGATOR is evaluated on different corpora from different genres with different annotation schemes to establish general usefulness and robustness. The NEGATOR system also participated in two shared task competitions which address specific issues related to negation. Both these tasks presented an opportunity to demonstrate that the NEGATOR system can be easily adapted and extended to meet specific task requirements. The parallel, comparative evaluations suggest that NEGATOR is indeed a robust baseline system that is domain and task independent

ARNLI: ARABIC NATURAL LANGUAGE INFERENCE ENTAILMENT AND CONTRADICTION DETECTION

Natural Language Inference (NLI) is a hot topic research in natural language processing, contradiction detection between sentences is a special case of NLI. This is considered a difficult NLP task which has a big influence when added as a component in many NLP applications, such as Question Answering Systems, text Summarization. Arabic Language is one of the most challenging low-resources languages in detecting contradictions due to its rich lexical, semantics ambiguity. We have created a dataset of more than 12k sentences and named ArNLI, that will be publicly available. Moreover, we have applied a new model inspired by Stanford contradiction detection proposed solutions on English language. We proposed an approach to detect contradictions between pairs of sentences in Arabic language using contradiction vector combined with language model vector as an input to machine learning model. We analyzed results of different traditional machine learning classifiers and compared their results on our created dataset (ArNLI) and on an automatic translation of both PHEME, SICK English datasets. Best results achieved using Random Forest classifier with an accuracy of 99%, 60%, 75% on PHEME, SICK and ArNLI respectively

Construction of an ontology for intelligent Arabic QA systems leveraging the Conceptual Graphs representation

The last decade had known a great interest in Arabic Natural Language Processing (NLP) applications. This interest is due to the prominent importance of this 6th most wide-spread language in the world with more than 350 million native speakers. Currently, some basic Arabic language challenges related to the high inflection and derivation, Part-of-Speech (PoS) tagging, and diacritical ambiguity of Arabic text are practically tamed to a great extent. However, the development of high level and intelligent applications such as Question Answering (QA) systems is still obstructed by the lacks in terms of ontologies and other semantic resources. In this paper, we present the construction of a new Arabic ontology leveraging the contents of Arabic WordNet (AWN) and Arabic VerbNet (AVN). This new resource presents the advantage to combine the high lexical coverage and semantic relations between words existing in AWN together with the formal representation of syntactic and semantic frames corresponding to verbs in AVN. The Conceptual Graphs representation was adopted in the framework of a multi-layer platform dedicated to the development of intelligent and multi-agents systems. The built ontology is used to represent key concepts in questions and documents for further semantic comparison. Experiments conducted in the context of the QA task show a promising coverage with respect to the processed questions and passages. The obtained results also highlight an improvement in the performance of Arabic QA regarding the c@1 measure.The work of the last author was carried out in the framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie Curie, the DIANA APPLICATIONS - Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Abouenour, L.; Nasri, M.; Bouzoubaa, K.; Kabbaj, A.; Rosso, P. (2014). Construction of an ontology for intelligent Arabic QA systems leveraging the Conceptual Graphs representation. Journal of Intelligent and Fuzzy Systems. 27(6):2869-2881. https://doi.org/10.3233/IFS-141248S2869288127

Development of a Corpus for User­based Scientific Question Answering

Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2021In recent years Question & Answering (QA) tasks became particularly relevant in the research field of natural language understanding. However, the lack of good quality datasets has been an important limiting factor in the quest for better models. Particularly in the biomedical domain, the scarcity of gold standard labelled datasets has been a recognized obstacle given its idiosyncrasies and complexities often require the participation of skilled domain¬specific experts in producing such datasets. To address this issue, a method for automatically gather Question¬Answer pairs from online QA biomedical forums has been suggested yielding a corpus named BiQA. The authors describe several strategies to validate this new dataset but a human manual verification has not been conducted. With this in mind, this dissertation was set out with the objectives of performing a manual verification of a sample of 1200 questions of BiQA and also to expanding these questions, by adding features, into a new corpus of text ¬ BiQA2 ¬ with the goal of contributing with a new corpusfor biomedical QA research. Regarding the manual verification of BiQA, a methodology for its characterization was laid out and allowed the identification of an array of potential problems related to the nature of its questions and answers aptness for which possible improvement solutions were presented. Concomitantly, the proposed new BiQA2 corpus ¬ created upon the validated questions and answers from the perused samples from BiQA ¬ builds new features similar to those observed in other biomedical corpus such as the BioASQ dataset. Both BiQA and BiQA2 were applied to deep learning strategies previously submitted to the BioASQ competition to assess their performance as a source of training data. Although the results achieved with the models created using BiQA2 exhibit limited capability pertaining to the BioASQ challenge, they also show some potential to contribute positively to model training in tasks such as Document re-ranking and answering to ‘yes/no’ questions

ENHANCING SENTIMENT LEXICA WITH NEGATION AND MODALITY FOR SENTIMENT ANALYSIS OF TWEETS

Sentiment analysis became one of the core tasks in the field of Natural Language Processing especially with the rise of social media. Public opinion is important for many domains such as commerce, politics, sociology, psychology, or finance. As an important player in social media, Twitter is the most frequently used microblogging platform for public opinion on any topic. In recent years, sentiment analysis in Twitter turned into a recognized shared task challenge. In this thesis, we propose to enhance sentiment lexica with the linguistic notions negation and modality for this challenge. We test the interoperability between various sentiment lexica with each other and with negation and modality and add some Twitter-specific ad-hoc features. The performance of different combinations of these features is analyzed in comprehensive ablation experiments. We participated in two challenges of the International Workshop on Semantic Evaluations (SemEval 2015). Our system performed robustly and reliably in the sentiment classification of tweets task, where it ranked 9th among 40 participants. However, it proved to be the state-of-the-art for measuring degree of sentiment of tweets with figurative language, where it ranked 1st among 35 systems

Benchmarking Machine Reading Comprehension: A Psychological Perspective

Machine reading comprehension (MRC) has received considerable attention as a benchmark for natural language understanding. However, the conventional task design of MRC lacks explainability beyond the model interpretation, i.e., reading comprehension by a model cannot be explained in human terms. To this end, this position paper provides a theoretical basis for the design of MRC datasets based on psychology as well as psychometrics, and summarizes it in terms of the prerequisites for benchmarking MRC. We conclude that future datasets should (i) evaluate the capability of the model for constructing a coherent and grounded representation to understand context-dependent situations and (ii) ensure substantive validity by shortcut-proof questions and explanation as a part of the task design.Comment: 21 pages, EACL 202