Voice-QA: evaluating the impact of misrecognized words on passage retrieval

Question Answering is an Information Retrieval task where the query is posed using natural language and the expected result is a concise answer. Voice-activated Question Answering systems represent an interesting application, where the question is formulated by speech. In these systems, an Automatic Speech Recognition module can be used to transcribe the question. Thus, recognition errors may be introduced, producing a significant effect on the answer retrieval process. In this work we study the relationship between some features of misrecognized words and the retrieval results. The features considered are the redundancy of a word in the result set and its inverse document frequency calculated over the collection. The results show that the redundancy of a word may be an important clue on whether an error on it would deteriorate the retrieval results, at least if a closed model is used for speech recognition.This work was carried out in the framework of TextEnterprise (TIN2009-13391-C04-03), Timpano (TIN2011-28169-C05-01), WIQEI IRSES (grant no. 269180) within the FP 7 Marie Curie People, FPU Grant AP2010-4193 from the Spanish Ministerio de Educaci´on (first author), and the Microcluster VLC/Campus on Multimodal Intelligent Systems (third author)Calvo Lance, M.; Buscaldi, D.; Rosso, P. (2012). Voice-QA: evaluating the impact of misrecognized words on passage retrieval. En Advances in Artificial Intelligence - IBERAMIA 2012. Springer Verlag (Germany). 462-471. https://doi.org/10.1007/978-3-642-34654-5_47S462471Buscaldi, D., Gómez, J.M., Rosso, P., Sanchis, E.: N-Gram vs. Keyword-Based Passage Retrieval for Question Answering. In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol. 4730, pp. 377–384. Springer, Heidelberg (2007)Harabagiu, S., Moldovan, D., Picone, J.: Open-Domain Voice-Activated Question Answering. In: 19th International Conference on Computational Linguistics (COLING 2002), pp. 1–7 (2002)Jones, K.: Index Term Weighting. Information Storage and Retrieval 9(11), 619–633 (1973)Moldovan, D., Paşca, M., Harabagiu, S., Surdeanu, M.: Performance Issues and Error Analysis in an Open-Domain Question Answering System. ACM Transactions on Information Systems (TOIS) 21(2), 133–154 (2003)Rosso, P., Hurtado, L.F., Segarra, E., Sanchis, E.: On the Voice-Activated Question Answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(1), 75–85 (2012)Sanderson, M., Paramita, M.L., Clough, P., Kanoulas, E.: Do User Preferences and Evaluation Measures Line Up? In: 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010), pp. 555–562. ACM, New York (2010)Turmo, J., Comas, P., Rosset, S., Galibert, O., Moreau, N., Mostefa, D., Rosso, P., Buscaldi, D.: Overview of QAST 2009. In: Peters, C., Di Nunzio, G.M., Kurimo, M., Mandl, T., Mostefa, D., Peñas, A., Roda, G. (eds.) CLEF 2009. LNCS, vol. 6241, pp. 197–211. Springer, Heidelberg (2010

GeoTextMESS: result fusion with fuzzy Borda ranking in geographical information retrieval

In this paper we discuss the integration of different GIR systems by means of a fuzzy Borda method for result fusion. Two of the systems, the one by the Universidad Politécnica de Valencia and the one of the Universidad of Jaén participated to the GeoCLEF task under the name TextMess. The proposed result fusion method takes as input the document lists returned by the different systems and returns a document list where the documents are ranked according to the fuzzy Borda voting scheme. The obtained results show that the fusion method allows to improve the results of the component systems, although the fusion is not optimal, because it is effective only if the components return a similar set of relevant documents.Peer ReviewedPostprint (author’s final draft

On the evaluation and improvement of arabic wordnet coverage and usability

The final publication is available at Springer via http://dx.doi.org/10.1007/s10579-013-9237-0[EN] Built on the basis of the methods developed for Princeton WordNet and EuroWordNet, Arabic WordNet (AWN) has been an interesting project which combines WordNet structure compliance with Arabic particularities. In this paper, some AWN shortcomings related to coverage and usability are addressed. The use of AWN in question/answering (Q/A) helped us to deeply evaluate the resource from an experience-based perspective. Accordingly, an enrichment of AWN was built by semi-automatically extending its content. Indeed, existing approaches and/or resources developed for other languages were adapted and used for AWN. The experiments conducted in Arabic Q/A have shown an improvement of both AWN coverage as well as usability. Concerning coverage, a great amount of named entities extracted from YAGO were connected with corresponding AWN synsets. Also, a significant number of new verbs and nouns (including Broken Plural forms) were added. In terms of usability, thanks to the use of AWN, the performance for the AWN-based Q/A application registered an overall improvement with respect to the following three measures: accuracy (+9.27 % improvement), mean reciprocal rank (+3.6 improvement) and number of answered questions (+12.79 % improvement).The work presented in Sect. 2.2 was done in the framework of the bilateral Spain-Morocco AECID-PCI C/026728/09 research project. The research of the two first authors is done in the framework of the PROGRAMME D'URGENCE project (grant no. 03/2010). The research of the third author is done in the framework of WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People, DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) research project and VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. We would like to thank Manuel Montes-y-Gomez (INAOE-Puebla, Mexico) and Sandra Garcia-Blasco (Bitsnbrain, Spain) for their feedback on the work presented in Sect. 2.4. We would like finally to thank Violetta Cavalli-Sforza (Al Akhawayn University in Ifrane, Morocco) for having reviewed the linguistic level of the entire document.Abouenour, L.; Bouzoubaa, K.; Rosso, P. (2013). On the evaluation and improvement of arabic wordnet coverage and usability. Language Resources and Evaluation. 47(3):891-917. https://doi.org/10.1007/s10579-013-9237-0S891917473Abbès, R., Dichy, J., & Hassoun, M. (2004). The architecture of a standard Arabic lexical database: Some figures, ratios and categories from the DIINAR.1 source program. In Workshop on computational approaches to Arabic script-based languages, Coling 2004. Geneva, Switzerland.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2009a). Structure-based evaluation of an Arabic semantic query expansion using the JIRS passage retrieval system. In Proceedings of the workshop on computational approaches to Semitic languages, E-ACL-2009, Athens, Greece, March.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2009b). Three-level approach for passage retrieval in Arabic question/answering systems. In Proceedings of the 3rd international conference on Arabic language processing CITALA’09, Rabat, Morocco, May, 2009.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2010a). An evaluated semantic query expansion and structure-based approach for enhancing Arabic question/answering. Special Issue in the International Journal on Information and Communication Technologies/IEEE. June.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2010b). Using the YAGO ontology as a resource for the enrichment of named entities in Arabic WordNet. In Workshop LR & HLT for semitic languages, LREC’10. Malta. May, 2010.Ahonen-Myka, H. (2002). Discovery of frequent word sequences in text. 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A text mining approach for definition question answering. In Proceedings of the 5th international conference on natural language processing, FinTal’2006, Turku, Finland.Diab, M. T. (2004). Feasibility of bootstrapping an Arabic Wordnet leveraging parallel corpora and an English Wordnet. In Proceedings of the Arabic language technologies and resources, NEMLAR, Cairo, Egypt.El Amine, M. A. (2009). Vers une interface pour l’enrichissement des requêtes en arabe dans un système de recherche d’information. In Proceedings of the 2nd conférence internationale sur l’informatique et ses applications (CIIA’09), May 3–4, Saida, Algeria.Elghamry, K. (2008). Using the web in building a corpus-based hypernymy-hyponymy Lexicon with hierarchical structure for Arabic. In Proceedings of the 6th international conference on informatics and systems, INFOS 2008. Cairo, Egypt.Elkateb, S., Black, W., Vossen, P., Farwell, D., Rodríguez, H., Pease, A., et al. (2006). Arabic WordNet and the challenges of Arabic. In Proceedings of Arabic NLP/MT conference, London, UK.Fellbaum, C. (Ed.). (1998). WordNet: An electronic lexical database. MA: MIT Press.García-Blasco, S., Danger, R., & Rosso, P. (2010). Drug–drug interaction detection: A new approach based on maximal frequent sequences. Sociedad Española para el Procesamiento del Lenguaje Natural, SEPLN, 45, 263–266.García-Hernández, R. A. (2007). Algoritmos para el descubrimiento de patrones secuenciales maximales. Ph.D. Thesis, INAOE. September, Mexico.García-Hernández, R. A., Martínez Trinidad, J. F., & Carrasco-ochoa, J. A. (2010). Finding maximal sequential patterns in text document collections and single documents. Informatica, 34(1), 93–101.Goweder, A., & De Roeck, A. (2001). Assessment of a significant Arabic corpus. In Proceedings of the Arabic NLP workshop at ACL/EACL, (pp. 73–79), Toulouse, France.Graff, D. (2007). Arabic Gigaword (3rd ed.). Philadelphia, USA: Linguistic Data Consortium.Graff, D., Kong, J., Chen, K., & Maeda, K. (2007). English Gigaword (3rd ed.). Philadelphia, USA: Linguistic Data Consortium.Hammou, B., Abu-salem, H., Lytinen, S., & Evens, M. (2002). QARAB: A question answering system to support the Arabic language. In Proceedings of the workshop on computational approaches to Semitic languages, ACL, (pp. 55–65), Philadelphia.Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics, COLING ‘92 (vol. 2, pp. 539–545).Kanaan, G., Hammouri, A., Al-Shalabi, R., & Swalha, M. (2009). A new question answering system for the Arabic language. American Journal of Applied Sciences, 6(4), 797–805.Kim, H., Chen, S., & Veale, T. (2006). Analogical reasoning with a synergy of HowNet and WordNet. In Proceedings of GWC’2006, the 3rd global WordNet conference, January, Cheju, Korea.Kipper-Schuler, K. (2006). VerbNet: A broad-coverage, comprehensive verb lexicon. Ph.D. Thesis.Mohammed, F. A., Nasser, K., & Harb, H. M. (1993). A knowledge-based Arabic question answering system (AQAS). In ACM SIGART bulletin (pp. 21–33).Niles, I., & Pease, A. (2001). Towards a standard upper ontology. In Proceedings of FOIS-2 (pp. 2–9), Ogunquit, Maine.Niles, I., & Pease, A. (2003). Linking lexicons and ontologies: Mapping WordNet to the suggested upper merged ontology. In Proceedings of the 2003 international conference on information and knowledge engineering, Las Vegas, Nevada.Ortega-Mendoza, R. M., Villaseñor-pineda, L., & Montes-y-Gõmez, M. (2007). Using lexical patterns to extract hyponyms from the Web. In Proceedings of the Mexican international conference on artificial intelligence MICAI 2007. November, Aguascalientes, Mexico. Lecture Notes in Artificial Intelligence 4827. Berlin: Springer.Palmer, M., P. Kingsbury, & D. Gildea. (2005). The proposition bank: An annotated corpus of semantic roles. Computational Linguistics, 21. USA: MIT Press.Pantel, P., & Pennacchiotti, M. (2006). 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Saul et al. (Eds.), Advances in neural information processing systems, 17. Cambridge, MA: MIT Press.Suchanek, F. M., Kasneci, G., & Weikum, G. (2007). YAGO: A core of semantic knowledge unifying WordNet and Wikipedia. In Proceedings of 16th international World Wide Web conference WWW’2007, (pp. 697–706), May, Banff, Alberta, Canada: ACM Press.Tjong Kim Sang, E., & Hofmann, K. (2007). Automatic extraction of Dutch hypernym–hyponym pairs. In Proceedings of CLIN-2006, Leuven, Belgium.Toral, A., Munoz, R., & Monachini, M. (2008). Named entity WordNet. In Proceedings of the Sixth international conference on language resources and evaluation (LREC’08), Marrakech, Morocco.Vossen, P. (Ed.). (1998). EuroWordNet, a multilingual database with lexical semantic networks. The Netherlands: Kluwer.Wagner, A. (2005). Learning thematic role relations for lexical semantic nets. Ph.D. Thesis, University of Tübingen, 2005

AI-KG: an Automatically Generated Knowledge Graph of Artificial Intelligence

Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings, and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize, and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications. In this paper, we present the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities. AI-KG includes about 14M RDF triples and 1.2M reified statements extracted from 333K research publications in the field of AI, and describes 5 types of entities (tasks, methods, metrics, materials, others) linked by 27 relations. AI-KG has been designed to support a variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and helping to inform decision-making in funding bodies and research policymakers. AI-KG has been generated by applying an automatic pipeline that extracts entities and relationships using three tools:DyGIE++, Stanford CoreNLP, and the CSO Classifier. It then integrates and filters the resulting triples using a combination of deep learning and semantic technologies in order to produce a high-quality knowledge graph. This pipeline was evaluated on a manually crafted gold standard, yielding competitive results. AI-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint

From Contracts in Structured English to CL Specifications

In this paper we present a framework to analyze conflicts of contracts written in structured English. A contract that has manually been rewritten in a structured English is automatically translated into a formal language using the Grammatical Framework (GF). In particular we use the contract language CL as a target formal language for this translation. In our framework CL specifications could then be input into the tool CLAN to detect the presence of conflicts (whether there are contradictory obligations, permissions, and prohibitions. We also use GF to get a version in (restricted) English of CL formulae. We discuss the implementation of such a framework.Comment: In Proceedings FLACOS 2011, arXiv:1109.239

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects