Research in Linguistic Engineering: Resources and Tools

In this paper we are revisiting some of the resources and tools developed by the members of the Intelligent Systems Research Group (GSI) at UPM as well as from the Information Retrieval and Natural Language Processing Research Group (IR&NLP) at UNED. Details about developed resources (corpus, software) and current interests and projects are given for the two groups. It is also included a brief summary and links into open source resources and tools developed by other groups of the MAVIR consortium

Analysis of biomedical and health queries: Lessons learned from TREC and CLEF evaluation benchmarks

International audienceBACKGROUND:Inherited ichthyoses represent a group of rare skin disorders characterized by scaling, hyperkeratosis and inconstant erythema, involving most of the tegument. Epidemiology remains poorly described. This study aims to evaluate the prevalence of inherited ichthyosis (excluding very mild forms) and its different clinical forms in France.METHODS:Capture - recapture method was used for this study. According to statistical requirements, 3 different lists (reference/competence centres, French association of patients with ichthyosis and internet network) were used to record such patients. The study was conducted in 5 areas during a closed period.RESULTS:The prevalence was estimated at 13.3 per million people (/M) (CI95\%, [10.9 - 17.6]). With regard to autosomal recessive congenital ichthyosis, the prevalence was estimated at 7/M (CI 95\% [5.7 - 9.2]), with a prevalence of lamellar ichthyosis and congenital ichthyosiform erythroderma of 4.5/M (CI 95\% [3.7 - 5.9]) and 1.9/M (CI 95\% [1.6 - 2.6]), respectively. Prevalence of keratinopathic forms was estimated at 1.1/M (CI 95\% [0.9 - 1.5]). Prevalence of syndromic forms (all clinical forms together) was estimated at 1.9/M (CI 95\% [1.6 - 2.6]).CONCLUSIONS:Our results constitute a crucial basis to properly size the necessary health measures that are required to improve patient care and design further clinical studies

Foundation, Implementation and Evaluation of the MorphoSaurus System: Subword Indexing, Lexical Learning and Word Sense Disambiguation for Medical Cross-Language Information Retrieval

Im medizinischen Alltag, zu welchem viel Dokumentations- und Recherchearbeit gehört, ist mittlerweile der überwiegende Teil textuell kodierter Information elektronisch verfügbar. Hiermit kommt der Entwicklung leistungsfähiger Methoden zur effizienten Recherche eine vorrangige Bedeutung zu. Bewertet man die Nützlichkeit gängiger Textretrievalsysteme aus dem Blickwinkel der medizinischen Fachsprache, dann mangelt es ihnen an morphologischer Funktionalität (Flexion, Derivation und Komposition), lexikalisch-semantischer Funktionalität und der Fähigkeit zu einer sprachübergreifenden Analyse großer Dokumentenbestände. In der vorliegenden Promotionsschrift werden die theoretischen Grundlagen des MorphoSaurus-Systems (ein Akronym für Morphem-Thesaurus) behandelt. Dessen methodischer Kern stellt ein um Morpheme der medizinischen Fach- und Laiensprache gruppierter Thesaurus dar, dessen Einträge mittels semantischer Relationen sprachübergreifend verknüpft sind. Darauf aufbauend wird ein Verfahren vorgestellt, welches (komplexe) Wörter in Morpheme segmentiert, die durch sprachunabhängige, konzeptklassenartige Symbole ersetzt werden. Die resultierende Repräsentation ist die Basis für das sprachübergreifende, morphemorientierte Textretrieval. Neben der Kerntechnologie wird eine Methode zur automatischen Akquise von Lexikoneinträgen vorgestellt, wodurch bestehende Morphemlexika um weitere Sprachen ergänzt werden. Die Berücksichtigung sprachübergreifender Phänomene führt im Anschluss zu einem neuartigen Verfahren zur Auflösung von semantischen Ambiguitäten. Die Leistungsfähigkeit des morphemorientierten Textretrievals wird im Rahmen umfangreicher, standardisierter Evaluationen empirisch getestet und gängigen Herangehensweisen gegenübergestellt

Finding answers to questions, in text collections or web, in open domain or specialty domains

International audienceThis chapter is dedicated to factual question answering, i.e. extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e. a query made of a list of words), and provides clues for finding precise answers. We will first focus on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. We will first present how to answer factual question in open domain. We will also present answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, we present main approaches and the remaining problems

Overview of the PAN/CLEF 2015 Evaluation Lab

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24027-5_49This paper presents an overview of the PAN/CLEF evaluation lab. During the last decade, PAN has been established as the main forum of text mining research focusing on the identification of personal traits of authors left behind in texts unintentionally. PAN 2015 comprises three tasks: plagiarism detection, author identification and author profiling studying important variations of these problems. In plagiarism detection, community-driven corpus construction is introduced as a new way of developing evaluation resources with diversity. In author identification, cross-topic and cross-genre author verification (where the texts of known and unknown authorship do not match in topic and/or genre) is introduced. A new corpus was built for this challenging, yet realistic, task covering four languages. In author profiling, in addition to usual author demographics, such as gender and age, five personality traits are introduced (openness, conscientiousness, extraversion, agreeableness, and neuroticism) and a new corpus of Twitter messages covering four languages was developed. In total, 53 teams participated in all three tasks of PAN 2015 and, following the practice of previous editions, software submissions were required and evaluated within the TIRA experimentation framework.Stamatatos, E.; Potthast, M.; Rangel, F.; Rosso, P.; Stein, B. (2015). Overview of the PAN/CLEF 2015 Evaluation Lab. En Experimental IR Meets Multilinguality, Multimodality, and Interaction: 6th International Conference of the CLEF Association, CLEF'15, Toulouse, France, September 8-11, 2015, Proceedings. Springer International Publishing. 518-538. doi:10.1007/978-3-319-24027-5_49S518538Álvarez-Carmona, M.A., López-Monroy, A.P., Montes-Y-Gómez, M., Villaseñor-Pineda, L., Jair-Escalante, H.: INAOE’s participation at PAN 2015: author profiling task–notebook for PAN at CLEF 2015. In: CLEF 2013 Working Notes. CEUR (2015)Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, Genre, and Writing Style in Formal Written Texts. TEXT 23, 321–346 (2003)Bagnall, D.: Author identification using multi-headed recurrent neural networks. In: CLEF 2015 Working Notes. CEUR (2015)Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on twitter. In: Proceedings of EMNLP 2011. ACL (2011)Burrows, S., Potthast, M., Stein, B.: Paraphrase Acquisition via Crowdsourcing and Machine Learning. ACM TIST 4(3), 43:1–43:21 (2013)Castillo, E., Cervantes, O., Vilariño, D., Pinto, D., León, S.: Unsupervised method for the authorship identification task. 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In: Proceedings of COLING 2008 (2008)Maharjan, S., Shrestha, P., Solorio, T., Hasan, R.: A straightforward author profiling approach in mapreduce. In: Bazzan, A.L.C., Pichara, K. (eds.) IBERAMIA 2014. LNCS, vol. 8864, pp. 95–107. Springer, Heidelberg (2014)Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text. Journal of Artificial Intelligence Research 30(1), 457–500 (2007)Eissen, S.M., Stein, B.: Intrinsic plagiarism detection. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 565–569. Springer, Heidelberg (2006)Mohammadi, G., Vinciarelli, A.: Automatic personality perception: Prediction of Trait Attribution Based on Prosodic Features. IEEE Transactions on Affective Computing 3(3), 273–284 (2012)Moreau, E., Jayapal, A., Lynch, G., Vogel, C.: Author verification: basic stacked generalization applied to predictions from a set of heterogeneous learners. In: CLEF 2015 Working Notes. CEUR (2015)Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “How old do you think I am?”; a study of language and age in twitter. In: Proceedings of ICWSM 2013. AAAI (2013)Oberlander, J., Nowson, S.: Whose thumb is it anyway?: classifying author personality from weblog text. In: Proceedings of COLING 2006. ACL (2006)Peñas, A., Rodrigo, A.: A simple measure to assess non-response. In: Proceedings of HLT 2011. ACL (2011)Pennebaker, J.W., Mehl, M.R., Niederhoffer, K.G.: Psychological Aspects of Natural Language Use: Our Words. Our Selves. Annual Review of Psychology 54(1), 547–577 (2003)Potthast, M., Barrón-Cedeño, A., Eiselt, A., Stein, B., Rosso, P.: Overview of the 2nd international competition on plagiarism detection. In: CLEF 2010 Working Notes. CEUR (2010)Potthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-Language Plagiarism Detection. Language Resources and Evaluation (LRE) 45, 45–62 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: CLEF 2011 Working Notes (2011)Potthast, M., Gollub, T., Hagen, M., Graßegger, J., Kiesel, J., Michel, M., Oberländer, A., Tippmann, M., Barrón-Cedeño, A., Gupta, P., Rosso, P., Stein, B.: Overview of the 4th international competition on plagiarism detection. In: CLEF 2012 Working Notes. CEUR (2012)Potthast, M., Gollub, T., Hagen, M., Tippmann, M., Kiesel, J., Rosso, P., Stamatatos, E., Stein, B.: Overview of the 5th international competition on plagiarism detection. In: CLEF 2013 Working Notes. CEUR (2013)Potthast, M., Gollub, T., Rangel, F., Rosso, P., Stamatatos, E., Stein, B.: Improving the reproducibility of PAN’s shared tasks: plagiarism detection, author identification, and author profiling. In: Kanoulas, E., Lupu, M., Clough, P., Sanderson, M., Hall, M., Hanbury, A., Toms, E. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 268–299. Springer, Heidelberg (2014)Potthast, M., Hagen, M., Beyer, A., Busse, M., Tippmann, M., Rosso, P., Stein, B.: Overview of the 6th international competition on plagiarism detection. In: CLEF 2014 Working Notes. CEUR (2014)Potthast, M., Göring, S., Rosso, P., Stein, B.: Towards data submissions for shared tasks: first experiences for the task of text alignment. In: CLEF 2015 Working Notes. CEUR (2015)Potthast, M., Hagen, M., Stein, B., Graßegger, J., Michel, M., Tippmann, M., Welsch, C.: ChatNoir: a search engine for the clueweb09 corpus. In: Proceedings of SIGIR 2012. 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Adaptation of machine translation for multilingual information retrieval in the medical domain

Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve eectiveness of cross-lingual IR. Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech–English, German–English, and French–English. MT quality is evaluated on data sets created within the Khresmoi project and IR eectiveness is tested on the CLEF eHealth 2013 data sets. Results. The search query translation results achieved in our experiments are outstanding – our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech–English, from 23.03 to 40.82 for German–English, and from 32.67 to 40.82 for French–English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French–English. For Czech–English and German–English, the increased MT quality does not lead to better IR results. Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance – better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions