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The article presents the experiments carried out as part of the participation in the main task of QA4MRE@CLEF 2012. In the developed system, we first combine the question and each answer option to form the Hypothesis (H). Stop words are removed from each H and query words are identified to retrieve the most relevant sentences from the associated do...
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This paper analyses the characteristic of Economic Model Resource Platform and Lucene search engine, a toolkit of full text retrieval, and employ it in the application. The SSH web development framework are designed to develop a search engine subsystems to achieve the classification of the platform model recommended the personalized retrieval funct...
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... In this paper, we focus on machine comprehension, which is QA in which the answer is con-tained within a provided passage. Several comprehension tasks have been developed, including Remedia (Hirschman et al., 1999), CBC4kids (Breck et al., 2001), and the QA4MRE textual question answering tasks in the CLEF evaluations (Peñas et al., 2011;Peñas et al., 2013;Clark et al., 2012;Bhaskar et al., 2012). ...
... It then employs semantic expansion, using some inference rules on the background collections, to expand the answer choices that could not be found in the retrieved passages. However, these two Arabic systems do not compare to the best performing system in the same campaign, which was created by Bhaskar et al. [8]. It uses the English test-set and performs at an accuracy of 0.53 and c@1 of 0.65. ...
Question answering systems generally, and Arabic systems are no exception, hit an upper bound of performance due to the propagation of error in their pipeline. This increases the significance of answer selection systems as they enhance the certainty and accuracy of question answering. Very few works tackled the Arabic answer selection problem, and they did not demonstrate en-couraging performance because they use the same question answering pipeline without any changes to satisfy the requirements of answer selection. In this pa-per, we present "ALQASIM 2.0", which uses a new approach to Arabic answer selection. It analyzes the reading test documents instead of the questions, utiliz-es sentence splitting, root expansion, and semantic expansion using an automat-ically generated ontology. Our experiments are conducted on the test-set pro-vided by CLEF 2012 through the task of QA4MRE. This approach leads to a promising performance of 0.36 accuracy and 0.42 c@1.
... This information can be relevant for machine reading systems, since negated and modalised events should be treated differently than factual events in the inference making process. We have participated in RespubliQA@CLEF 2010 [2], QA4MRE@CLEF 2011 [3] and QA4MRE@2012 [4]. This year we have participated in Main Task [1] and Pilot Task [1]. ...
The article presents the experiments carried out as part of the participation in the pilot task (Modality and Negation)1 of QA4MRE@CLEF 2012. Modality and Negation are two main grammatical devices that allow to express extra-propositional aspects of meaning. Modality is a grammatical category that allows to express aspects related to the attitude of the speaker towards statements. Negation is a grammatical category that allows to change the truth value of a proposition. The input for the systems is a text where all events expressed by verbs are identified and numbered the output should be a label per event. The possible values are: mod, neg, neg-mod, none. In the developed system, we first build a database for modal verbs of two categories: epistemic and deontic. Also, we used a negative verb list of 1877 verbs. This negative verb list has been used to identify negative modality. We extract the each tagged events from each sentences. Then our system check modal verbs by that database from each sentences. If any modal verbs is found before that an event then that event should be modal verb and tagged as mod. If modal verb is there and also negeted words is found before that evet then that event should negeted mod and tagged as neg-mod. If no modal verb is found before that an event but negeted word are found before that event then that event should be negeted and tagged as neg. Otherwise the event should tagged as none. We trained our system by traing data (sample data) that was provided by QA4MRE organizer. Then we are tested our system on test dataset. In test data set there are eight documents, two per each of the four topics such as Alzheimer, music and society, AIDs and climate change. Our system overall accuracy is 0.6262 (779 out of 1244).
... This information can be relevant for machine reading systems, since negated and modalised events should be treated differently than factual events in the inference making process. We have participated in RespubliQA@CLEF 2010 [2], QA4MRE@CLEF 2011 [3] and QA4MRE@2012 [4]. This year we have participated in Main Task [1] and Pilot Task [1]. ...
The article presents the experiments carried out as part of the participation in the pilot task (Modality and Negation) 1 of QA4MRE@CLEF 2012. Modality and Negation are two main grammatical devices that allow to express extra-propositional aspects of meaning. Modality is a grammatical category that allows to express aspects related to the attitude of the speaker towards statements. Negation is a grammatical category that allows to change the truth value of a proposition. The input for the systems is a text where all events expressed by verbs are identified and numbered the output should be a label per event. The possible values are: mod, neg, neg-mod, none. In the developed system, we first build a database for modal verbs of two categories: epistemic and deontic. Also, we used a negative verb list of 1877 verbs. This negative verb list has been used to identify negative modality. We extract the each tagged events from each sentences. Then our system check modal verbs by that database from each sentences. If any modal verbs is found before that an event then that event should be modal verb and tagged as mod. If modal verb is there and also negeted words is found before that evet then that event should negeted mod and tagged as neg-mod. If no modal verb is found before that an event but negeted word are found before that event then that event should be negeted and tagged as neg. Otherwise the event should tagged as none. We trained our system by traing data (sample data) that was provided by QA4MRE organizer. Then we are tested our system on test dataset. In test data set there are eight documents, two per each of the four topics such as Alzheimer, music and society, AIDs and climate change. Our system overall accuracy is 0.6262 (779 out of 1244).
Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access, and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into five distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base, and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and we discuss some potential future directions to explore.
Le travail de cette thèse sřinscrit dans un cadre général du traitement automatique de lalangue naturelle (TALN). Il se situe dans le contexte de la recherche dřinformations précises.Nous nous intéressons plus précisément aux systèmes de question-réponse. En effet, cettethèse a pour but de proposer une nouvelle approche pour la question-réponse arabe. Cetteapproche sera implémentée en un système de question-réponse pour lřarabe. Ce systèmeintègre des procédures de raisonnement automatique (RA) et des techniques dereconnaissance dřimplications textuelles (RTE). Pour le faire, nous nous appuyons sur desreprésentations sémantiques et logiques de la question et des passages. Ainsi, nousdéterminons lřimplication entre ces représentations logiques afin de trouver la réponseprécise.
What if you could turn all websites of an entire domain into a single database? Imagine all real estate offers, all airline flights, or all your local restaurants' menus automatically collected from hundreds or thousands of agencies, travel agencies, or restaurants, presented as a single homogeneous dataset.
Historically, this has required tremendous e ort by the data providers and whoever is collecting the data: Vertical search engines aggregate offers through specific interfaces which provide suitably structured data. The semantic web vision replaces the specific interfaces with a single one, but still requires providers to publish structured data.
Attempts to turn human-oriented HTML interfaces back into their underlying databases have largely failed due to the variability of web sources. We demonstrate that this is about to change: the availability of comprehensive entity recognition together with advances in ontology reasoning have made possible a new generation of knowledge-driven, domain-specific data extraction approaches. To that end, we introduce DIADEM, the first automated data extraction system that can turn nearly any website of a domain into structured data, working fully automatically, and present some preliminary evaluation results.
We also present a brief overview of BERyL, diadem's sub-module for web block classification.
This paper presents " ALQASIM " , a question answering system that focuses on answer selection and validation. Our experiments have been conducted in the framework of the main task of QA4MRE @ CLEF 2013. ALQASIM uses a novel technique by analyzing the reading test documents instead of the questions, which leads to a promising performance of 0.31 accuracy and 0.36 C@1, without using the test-set background collections.
Arabic is the 6th most wide-spread natural language in the world with more than 350 million native speakers. Arabic question answering systems are gaining great importance due to the increasing amounts of Arabic content on the Internet and the increasing demand for information that regular information retrieval techniques cannot satisfy. In spite of the importance of Arabic question answering, there is no review that covers Arabic question answering systems, tools, resources, and test-sets so far, which was the motivation for this work. In this survey, different Arabic question answering systems are demonstrated and analyzed and the main question answering tasks like question analysis, passage retrieval, and answer extraction are explored. The main difficulties of modern standard Arabic and how these difficulties are tamed and classified are also explained. Arabic question answering evaluation metrics, test-sets, and language resources are reviewed, and future trends are also highlighted to guide new research in this area. This survey provides guidance for new research in Arabic question answering to get up-to-date knowledge about the state-of-the-art approaches in this area. It also demonstrates the tools created and used by researchers to build an Arabic question answering system.
