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  • 1.
    Ahrenberg, Lars
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Andersson, Mikael
    Linköping University.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    A Simple Hybrid Aligner for Generating Lexical Correspondences in Parallel Texts1998In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics (COLING-ACL'98) Montreal, August 10-14, 1998, / [ed] Pierre Isabelle, The Association for Computational Linguistics , 1998, p. 29-35Conference paper (Refereed)
  • 2.
    Ahrenberg, Lars
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Andersson, Mikael
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    A System for Incremental and Interactive Word Linking2002In: In Third International Conference on Language Resources and Evaluation (LREC 2002), Las Palmas, 29-31 May 2002., European Language Resources Association (ELRA) , 2002, p. 485-490Conference paper (Refereed)
    Abstract [en]

    Aligned parallel corpora constitute a critical information resource for a great number of linguistic and technological endeavors. Automatic sentence alignment has reached a level whereby large parallel documents can be fully aligned with the aid of interactive post-editing tools. Word alignment systems have not yet reached the same level of performance, but are good enough to support full word alignment if embedded in an interactive system. In this paper we describe a system for fast and accurate word alignment currently under development at our department, where the user can review and improve the output from an automatic system in an incremental fashion.

  • 3.
    Ahrenberg, Lars
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    A knowledge-lite approach to word alignment2000In: Parallel Text Processing: Alignment and Use of Translation Corpora / [ed] Jean Veronis, Dordrecht, The Netherlands: Kluwer Academic Publishers, 2000, p. 97-116Chapter in book (Other academic)
    Abstract [en]

    The most promising approach to word alignment is to combine statistical methods with non-statistical information sources. Some of the proposed non-statistical sources, including bilingual dictionaries, POS-taggers and lemmatizers, rely on considerable linguistic knowledge, while other knowledge-lite sources such as cognate heuristics and word order heuristics can be implemented relatively easy. While knowledge-heavy sources might be expected to give better performance, knowledge-lite systems are easier to port to new language pairs and text types, and they can give sufficiently good results for many purposes, e.g. if the output is to be used by a human user for the creation of a complete word-aligned bitext. In this paper we describe the current status of the Linköping Word Aligner (LWA), which combines the use of statistical measures of co-occurrence with four knowledge-lite modules for (i)) word categorization, (ii) morphological variation, (iii) word order, and (iv) phrase recognition. We demonstrate the portability of the system (from English-Swedish texts to French-English texts) and present results for these two language-pairs. Finally, we will report observations from an error analysis of system output, and identify the major strengths and weaknesses of the system.

  • 4.
    Ahrenberg, Lars
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Correspondence measures for MT evaluation.2000In: Proceedings of the Second International Conference on Linguistic Resources and Evaluation (LREC-2000, Paris, France: European Language Resources Association (ELRA) , 2000, p. 41-46Conference paper (Refereed)
  • 5.
    Ahrenberg, Lars
    et al.
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    On translation corpora and translation support tools: A project report.1996In: Languages in Contrast. : Papers from a Symposium on Text-based Cross-linguistic Studies, Lund 4-5 March 1994 / [ed] K. Aijmer, B Altenberg & M. Johansson, Lund: Lund University Press , 1996, p. 185-200Conference paper (Refereed)
  • 6.
    Ahrenberg, Lars
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Språkliga effekter av översättningssystem.1998In: Svenskan i IT-samhället. / [ed] Olle Josephsson, Uppsala: Hallgren & Fallgren , 1998, p. 96-115Chapter in book (Other academic)
    Abstract [sv]

    Hur förändras det svenska språket av datorer och IT-teknik? Hur påverkas svenska ord, meningar och texter? Ett tiotal språkvetare diskuterar vad ordbehandlare, e-post, internet och s.k. "översättningsmaskiner" kan få för konsekvenser för svenskan. Analyser och resonemang kring en mängd exempel från både myndighetsbrev och chattare på nätet

  • 7.
    Ahrenberg, Lars
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Petterstedt, Michael
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Interactive Word Alignment for Language Engineering2003In: The 10th Conference of the European Chapter of the Association for Computational Linguistics, Conference Companion, Association for Computational Linguistics , 2003, p. 49-52Conference paper (Refereed)
  • 8.
    Ahrenberg, Lars
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Sågvall Hein, Anna
    Institutionen för lingvistik, Uppsala universitet..
    Tiedemann, Jörg
    Institutionen för lingvistik, Uppsala universitet.
    Evaluation of word alignment systems2000In: Proceedings of the Second International Conference on Linguistic Resources and Evaluation (LREC-2000), Paris, France: European Language Resources Association (ELRA) , 2000, p. 1255-1261Conference paper (Refereed)
  • 9.
    Deleger, Louise
    et al.
    INSERM.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Zweigenbaum, Pierre
    CNRS.
    Translating medical terminologies through word alignment in parallel text corpora2009In: JOURNAL OF BIOMEDICAL INFORMATICS, ISSN 1532-0464, Vol. 42, no 4, p. 692-701Article in journal (Refereed)
    Abstract [en]

    Developing international multilingual terminologies is a time-consuming process. We present a methodology which aims to ease this process by automatically acquiring new translations of medical terms based on word alignment in parallel text corpora, and test it on English and French. After collecting a parallel, English-French corpus, we detected French translations of English terms from three terminologies-MeSH, SNOMED CT and the MedlinePlus Health Topics. We obtained respectively for each terminology 74.8%, 77.8% and 76.3% of linguistically correct new translations. A sample of the MeSH translations was submitted to expert review and 61.5% were deemed desirable additions to the French MeSH. In conclusion, we successfully obtained good quality new translations, which underlines the suitability of using alignment in text corpora to help translating terminologies. Our method may be applied to different European languages and provides a methodological framework that may be used with different processing tools.

  • 10.
    Deléger, Louise
    et al.
    INSERM, U729, Paris, France.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Zweigenbaum, Pierre
    INSERM, U729, Paris, France.
    Contribution to Terminology Internationalization by Word Alignment in Parallel Corpora2006In: AMIA 2006 Symposium Proceedings, Washington D.C., USA: AMIA , 2006, p. 185-189Conference paper (Refereed)
    Abstract [en]

    Background and objectives

    Creating a complete translation of a large vocabulary is a time-consuming task, which requires skilled and knowledgeable medical translators. Our goal is to examine to which extent such a task can be alleviated by a specific natural language processing technique, word alignment in parallel corpora. We experiment with translation from English to French.

    Methods

    Build a large corpus of parallel, English-French documents, and automatically align it at the document, sentence and word levels using state-of-the-art alignment methods and tools. Then project English terms from existing controlled vocabularies to the aligned word pairs, and examine the number and quality of the putative French translations obtained thereby. We considered three American vocabularies present in the UMLS with three different translation statuses: the MeSH, SNOMED CT, and the MedlinePlus Health Topics.

    Results

    We obtained several thousand new translations of our input terms, this number being closely linked to the number of terms in the input vocabularies.

    Conclusion

    Our study shows that alignment methods can extract a number of new term translations from large bodies of text with a moderate human reviewing effort, and thus contribute to help a human translator obtain better translation coverage of an input vocabulary. Short-term perspectives include their application to a corpus 20 times larger than that used here, together with more focused methods for term extraction.

  • 11.
    Deléger, Louise
    et al.
    INSERM U729, Paris, France.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Zweigenbaum, Pierre
    INSERM U729, Paris, France.
    Enriching Medical Terminologies: an Approach Based on Aligned Corpora2006In: Ubiquity: Technologies for Better Health in Aging Societies, MIE2006 / [ed] Arie Hasman, Reinhold Haux, Johan van der Lei, Etienne De Clercq, Francis Roger-France, IOS Press, 2006, p. 747-752Conference paper (Refereed)
    Abstract [en]

    Medical terminologies such as those in the UMLS are never exhaustive and there is a constant need to enrich them, especially in terms of multilinguality. We present a methodology to acquire new French translations of English medical terms based on word alignment in a parallel corpus - i.e. pairing of corresponding words. We automatically collected a 27.7-million-word parallel, English-French corpus. Based on a first 1.3-million-word extract of this corpus, we detected 10,171 candidate French translations of English medical terms from MeSH and SNOMED, among which 3,807 are new translations of English MeSH terms

  • 12. Deléger, Louise
    et al.
    Merkel, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Zweigenbaum, Pierre
    Using Word Alignment to Extend Multilingual Medical Terminologies2006In: Workshop on Acquiring and Representing Multilingual, Specialized Lexicons: the Case of Biomedicine,2006, Genova: ARMSL , 2006Conference paper (Refereed)
  • 13.
    Eldén, Lars
    et al.
    Linköping University, Department of Mathematics, Computational Mathematics. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Ahrenberg, Lars
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Fagerlund, Martin
    Linköping University, Department of Mathematics, Computational Mathematics. Linköping University, The Institute of Technology.
    Computing Semantic Clusters by Semantic Mirroring and Spectral Graph Partitioning2013In: Mathematics in Computer Science, ISSN 1661-8270, Vol. 7, p. 293-313Article in journal (Refereed)
    Abstract [en]

    Using the technique of semantic mirroring a graph is obtained that represents words and their translationsfrom a parallel corpus or a bilingual lexicon. The connectedness of the graph holds information about the semanticrelations of words that occur in the translations. Spectral graph theory is used to partition the graph, which leadsto a grouping of the words in different clusters. We illustrate the method using a small sample of seed words froma lexicon of Swedish and English adjectives and discuss its application to computational lexical semantics andlexicography.

  • 14. Fagerlund, Martin
    et al.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Eldén, Lars
    Linköping University, Department of Mathematics, Scientific Computing. Linköping University, The Institute of Technology.
    Ahrenberg, Lars
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Computing Word Senses by Semantic Mirroring and Spectral Graph Partitioning2010In: Proceedings of TextGraphs-5 - 2010 Workshop on Graph-based Methods for Natural Language Processing / [ed] Carmen Banea, Alessandro Moschitti, Swapna Somasundaran and Fabio Massimo Zanzotto, Stroudsburg, PA, USA: The Association for Computational Linguistics , 2010, p. 103-107Conference paper (Refereed)
    Abstract [en]

    Using the technique of ”semantic mirroring”a graph is obtained that representswords and their translations from a parallelcorpus or a bilingual lexicon. The connectednessof the graph holds informationabout the different meanings of words thatoccur in the translations. Spectral graphtheory is used to partition the graph, whichleads to a grouping of the words accordingto different senses. We also report resultsfrom an evaluation using a small sample ofseed words from a lexicon of Swedish andEnglish adjectives.

  • 15.
    Flycht-Eriksson (Silvervarg), Annika
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Jönsson, Arne
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Sundblad, Håkan
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Ontology-driven Information-providing Dialogue Systems2003In: Proceedings of the Americas Conference on Information Systems / [ed] Dennis Galletta and Jeanne Ross, Association for Information Systems , 2003Conference paper (Refereed)
  • 16.
    Foo, Jody
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Merkel, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Building Standardized Term Bases Through Automated Term Extraction and Advanced Editing Tools2006In: International Conference on Terminology,2006, Antwerp: ICT , 2006Conference paper (Refereed)
  • 17.
    Foo, Jody
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Computer aided term bank creation and standardization: Building standardized term banks through automated term extraction and advanced editing tools2010In: Terminology in Everyday Life / [ed] Marcel Thelen and Frieda Steurs, John Benjamins Publishing Company , 2010, p. 163-180Chapter in book (Other academic)
    Abstract [en]

    Using a standardized term bank in both authoring and translation processes can facilitate the use of consistent terminology, which in turn minimizes confusion and frustration from the readers. One of the problems of creating a standardized term bank, is the time and effort required. Recent developments in term extraction techniques based on word alignment can improve extraction of term candidates when parallel texts are available. The aligned units are processed automatically, but a large quantity of term candidates will still have to be processed by a terminologist to select which candidates should be promoted to standardized terms. To minimize the work needed to process the extracted term candidates, we propose a method based on using efficient editing tools, as well as ranking the extracted set of term candidates by quality. This sorted set of term candidates can then be edited, categorized and filtered in a more effective way. In this paper, the process and methods used to arrive at a standardized term bank are presented and discussed.

     

  • 18.
    Foo, Jody
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Using machine learning to perform automatic term recognition2010In: Proceedings of the LREC 2010 Workshop on Methods for automatic acquisition of Language Resources and their evaluation methods / [ed] Núria Bel, Béatrice Daille, Andrejs Vasiljevs, European Language Resources Association, 2010, p. 49-54Conference paper (Refereed)
    Abstract [en]

    In this paper a machine learning approach is applied to Automatic Term Recognition (ATR). Similar approaches have been successfully used in Automatic Keyword Extraction (AKE). Using a dataset consisting of Swedish patent texts and validated terms belonging to these texts, unigrams and bigrams are extracted and annotated with linguistic and statistical feature values. Experiments using a varying ratio between positive and negative examples in the training data are conducted using the annotated n-grams. The results indicate that a machine learning approach is viable for ATR. Furthermore, a machine learning approach for bilingual ATR is discussed. Preliminary analysis however indicate that some modifications have to be made to apply the monolingual machine learning approach to a bilingual context.

  • 19.
    Holmqvist, Maria
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Stymne, Sara
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Ahrenberg, Lars
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Alignment-based reordering for SMT2012In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12), 2012, p. 3436-3440Conference paper (Other academic)
    Abstract [en]

    We present a method for improving word alignment quality for phrase-based statistical machine translation by reordering the source text according to the target word order suggested by an initial word alignment. The reordered text is used to create a second word alignment which can be an improvement of the first alignment, since the word order is more similar. The method requires no other pre-processing such as part-of-speech tagging or parsing. We report improved Bleu scores for English-to-German and English-to-Swedish translation. We also examined the effect on word alignment quality and found that the reordering method increased recall while lowering precision, which partly can explain the improved Bleu scores. A manual evaluation of the translation output was also performed to understand what effect our reordering method has on the translation system. We found that where the system employing reordering differed from the baseline in terms of having more words, or a different word order, this generally led to an improvement in translation quality.

  • 20.
    Jönsson, Arne
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Alexandersson, JanBecker, TillmanJokinen, KristiinaMerkel, MagnusLinköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    IJCAI 2003 workshop on Knowledge and Reasoning in Practical Dialogue Systems2003Conference proceedings (editor) (Other academic)
  • 21.
    Jönsson, Arne
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Andén, Frida
    Degerstedt, Lars
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Flycht-Eriksson (Silvervarg), Annika
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Norberg, Sara
    Experiences from combining dialogue system development with information extraction techniques2004In: New Directions in Question Answering / [ed] Mark T. Maybury, Boston: AAAIMIT Press , 2004, p. 153-163Chapter in book (Other academic)
    Abstract [en]

    Next generation question answering systems are challenged on many fronts including but not limited to massive, heterogeneous and sometimes streaming collections, diverse and challenging users, and the need to be sensitive to context, ambiguity, and even deception. This chapter describes new directions in question answering (QA) including enhanced question processing, source selection, document retrieval, answer determination, and answer presentation generation. We consider important directions such as answering questions in context (e.g., previous queries, day or time, the data, the task, location of the interactive device), scenario based QA, event and temporal QA, spatial QA, opinionoid QA, multimodal QA, multilingual QA, user centered and collaborative QA, explanation, interactive QA, QA reuse, and novel architectures for QA. The chapter concludes by outlining a roadmap of the future of question answering, articulating necessary resources for, impediments to, and planned or possible future capabilities.

  • 22.
    Jönsson, Arne
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Extending Q&A systems to dialogue system2003In: Working Notes from NoDaLiDa 03, Reykjavik, Iceland, 2003Conference paper (Refereed)
  • 23.
    Jönsson, Arne
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Extending QandA systems to dialogue systems2003In: Working Notes from NoDaLiDa 03, Reykjavik, Iceland, 2003, 2003Conference paper (Other academic)
  • 24.
    Jönsson, Arne
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Some issues in Dialogue-Based Question-Answering2003In: Working Notes from AAAI Spring Symposium, Stanford, 2003., 2003Conference paper (Refereed)
  • 25.
    Marko, Kornél
    et al.
    Freiburg University Hospital, Department of Medical Informatics, Freiburg, Germany.
    Baud, Robert
    University Hospitals of Geneva, Service of Medical Informatics, Geneva, Switzerland.
    Zweigenbaum, Pierre
    Inserm, U729; Assistance Publique – Paris Hospitals, STIM; Inalco, CRIM, Paris, France.
    Borin, Lars
    Göteborg University, NLP Section, Department of Swedish, Göteborg, Sweden.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Schulz, Stefan
    Freiburg University Hospital, Department of Medical Informatics, Freiburg, Germany.
    Towards a Multilingual Medical Lexicon2006In: AMIA 2006 Symposium Proceedings, Washington D.C., USA: AMIA , 2006, p. 534-538Conference paper (Refereed)
    Abstract [en]

    We present results of the collaboration of a multinational team of researchers from (computational) linguistics, medicine, and medical informatics with the goal of building a multilingual medical lexicon with high coverage and complete morpho-syntactic information. Monolingual lexical resources were collected and subsequently mapped between languages using a morpho-semantic term normalization engine, which captures intra- as well as interlingual synonymy relationships on the level of subwords.

  • 26. Markó, Kornél
    et al.
    Baud, Robert
    Zweigenbaum, Pierre
    Merkel, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Toporowska-Gronostaj, Maria
    Kokkinakis, Dimitrios
    Schulz, Stefan
    Cross-Lingual Alignment of Medical Lexicons2006In: Workshop on Acquiring and Representing Multilingual, Specialized Lexicons: the Case of Biomedicine,2006, Genova: ARMSL , 2006Conference paper (Refereed)
  • 27.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Temporal information in natural language1989Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The subject of this thesis is temporal information; how it is expressed and conveyed in natural language. When faced with the task of processing temporal information in natural language computationally, a number of challenges has to be met. The linguistic units that carry temporal information must be recognized and their semantic functions decided upon. Certain temporal information is not explicitly stated by grammatical means and must be deduced from contextual knowledge and from discourse principles depending on the type of discourse.

    In this thesis, a grammatical and semantic description of Swedish temporal expressions is given. The context dependency of temporal expressions is examined and the necessity of a conceptual distinction between phases and periods is argued for. Furthermore, it is argued that the Reichenbachian notion of reference time is unnecessary in the processing of temporal processing of texts. Instead the general contextual parameters speech time/utterance situation (ST/US) and discourse time/temporal focus (DT/TF) are defended. An algorithm for deciding the temporal structure of discourse is presented where events are treated as primary individuals.

  • 28.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Understanding and enhancing translation by parallel text processing1999Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In recent years the fields of translation studies, natural language processing and corpus linguistics have come to share one object of study, namely parallel text corpora, and more specifically translation corpora. In this thesis it is shown how all three fields can benefit from each other, and, in particular, that a prerequisite for making better translations (whether by humans or with the aid of computer-assisted tools) is to understand features and relationships that exist in a translation corpus. The Linköping Translation Corpus (LTC) is the empirical foundation for this work. LTC is comprised of translations from three different domains and translated with different degrees of computer support. Results in the form of tools, measures and analyses of translations in LTC are presented.

    In the translation industry, the use of translation memories, which are based on the concept of reusability, has been increasing steadily in recent years. In an empirical study, the notion of reusability in technical translation is investigated as well as translators' attitudes towards translation tools.

    A toolbox for creating and analysing parallel corpora is also presented. The tools are then used for uncovering relationships between the originals and their corresponding translations. The Linköping Word Aligner (LWA) is a portable tool for linking words and expressions between a source and target text. LWA is evaluated with the aid of reference data compiled before the system evaluation. The reference data are created and evaluated automatically with the help of an annotation tool, called the PLUG Link Annotator.

    Finally, a model for describing correspondences between a source text and a target text is introduced. The model uncovers voluntary shifts concerning structure and content. The correspondence model is then applied to the LTC.

  • 29.
    Merkel, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Foo, Jody
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Terminology extraction and term ranking for standardizing term banks2007In: Proceedings of 16th Nordic Conference of Computational Linguistics Nodalida,2007 / [ed] Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit, Tartu, Estonia: University of Tartu , 2007, p. 349-354Conference paper (Refereed)
    Abstract [en]

    This paper presents how word alignment techniques could be used for building standardized term banks. It is shown that time and effort could be saved by a relatively simple evaluation metric based on frequency data from term pairs, and source and target distributions inside the alignment results. The proposed Q-value metric is shown to outperform other tested metrics such as Dice's coefficient, and simple pair frequency.

     

  • 30.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems.
    Foo, Jody
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Ahrenberg, Lars
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    IPhraxtor - A linguistically informed system for extraction of term candidates2013In: Proceedings of the 19th Nordic Conference on Computational Linguistics (Nodalida 2013), Oslo, May 22-24, 2013, NEALT Proceedings Series 16 / [ed] Stephan Oepen, Kristin Hagen, Janne Bondi Johannesse, Linköping: Linköping University Electronic Press, 2013, Vol. 85, p. 121-132Conference paper (Refereed)
    Abstract [en]

    In this paper a method and a flexible tool for performing monolingual term extraction is presented; based on the use of syntactic analysis where information on parts-of-speech; syntactic functions and surface syntax tags can be utilised. The standard approaches to evaluating term extraction; namely by manual evaluation of the top n term candidates or by comparing to a gold standard consisting of a list of terms from a specific domain can have its advantages; but in this paper we try to realise a proposal by Bernier-Colborne (2012) where extracted terms are compared to a gold standard consisting of a test corpus where terms have been annotated in context. Apart from applying this evaluation to different configuratio

  • 31.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Foo, Jody
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Andersson, Mikael
    Fodina Language Technology AB.
    Edholm, Lars
    Fodina Language Technology AB.
    Gidlund, Mikaela
    Fodina Language Technology AB.
    Åsberg, Sanna
    Fodina Language Technology AB.
    Automatic Extraction and Manual Validation of Hierarchical Patent Terminology2009In: NORDTERM 16. Ontologier og taksonomier.: Rapport fra NORDTERM 2009 / [ed] B. Nistrup Madsen & H. Erdman Thomsen, Copenhagen, Denmark: Copenhagen Business School Press, 2009, p. 249-262Conference paper (Refereed)
    Abstract [en]

    Several methods can be applied to create a set of validated terms from existing documents. In this paper we describe an automatic bilingual term candidate extraction method, and the validation process used to create a hierarchical patent terminology. The process described was used to extract terms from patent texts, commissioned by the Swedish Patent Office with the purpose of using the terms for machine translation. Information on the correct linguistic inflection patterns and hierarchical partitioning of terms based on their use are of utmost importance.The process contains six phases, 1) Analysis of the source material and system configuration; 2) Term candidate extraction; 3) Term candidate filtering and initial linguistic validation; 4) Manual validation by domain experts; 5) Final linguistic validation; and 6) Publishing the validated terms.Input to the extraction process consisted of more than 91 000 patent document pairs in English and Swedish, 565 million words in English and 450 million words in Swedish. The English documents were supplied in EBD SGML format and the Swedish documents were supplied in OCR processed scans of patent documents. After grammatical and statistical analysis, the documents were word-aligned. Using the word-aligned material, candidate terms were extracted based on linguistic patterns. 750 000 term candidates were extracted and stored in a relational database. The term candidates were processed in 8 months resulting in 181 000 unique validated term pairs that were exported into several hierarchically organized OLIF files.

  • 32.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Gidlund, Mikaela
    Fodina Language Technology AB.
    Integrerat termstöd för teknisk dokumentation2012In: NORDTERM 17: Samarbetet ger resultat: från begreppskaos till överenskomna termer / [ed] Anu Ylisalmi, Jyväskylä: Nordterm , 2012, p. 149-153Conference paper (Other academic)
  • 33.
    Merkel, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Lange, Andreas
    A pattern extraction workbench combining multiple linguistic levels2004In: Conference on Language Resouces and Evaluation LREC,2004, Paris: ELDA , 2004Conference paper (Refereed)
  • 34.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nilsson, Henrik
    Terminologicentrum (TNC), Stockholm.
    Getting to terms with terminology at Swedish public agencies2011In: CHAT 2011: Creation, Harmonization and Application of Terminology Resources / [ed] Tatiana Gornostay and Andrejs Vasiļjevs, Tartu: Northern European Association for Language Technology (NEALT) , 2011, p. 36-39Conference paper (Refereed)
    Abstract [en]

    This paper describes on-going work aimed atassisting public agencies in Sweden to conformto the new Swedish Language Act(passed in 2009). The Language Act highlightsterminology as a key factor for a publicagency, as well as a responsibility for a publicagency to ensure that its terminology is madeavailable, used and developed. Term-O-Stat isan action program to help public agencies toimprove their terminological efforts. Term-OStatis divided into four distinct steps: 1) terminventory, 2) term classification, 3) conceptualanalysis and term choice, and 4) term implementation.We describe the four steps and alsoexperiences from the realization of step 1 and2 at the Swedish Social Insurance Agency.

  • 35.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nilsson, Henrik
    Terminologicentrum.
    Term-O-Stat - ett fyrstegsprogram för terminologiarbete inom myndigheter2012In: NORDTERM 17: Samarbetet ger resultat: från begreppskaos till överenskomna termer / [ed] Anu Ylisalmi, Jyväskylä: Nordterm , 2012, p. 222-228Conference paper (Other academic)
  • 36.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nilsson, Henrik
    Terminologicentrum.
    Term-O-Stat - fyra steg för terminologiarbete på svenska myndigheter2011In: Språkbruk, ISSN 0358-9293, Vol. 4, p. 9-13Article in journal (Refereed)
  • 37.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nilsson, Henrik
    Terminologicentrum TNC, Sweden.
    Tillgänglighet till terminologi: svenska myndigheters ansvar2010In: Språkteknologi för ökad tillgänglighet, Linköping: Linköping University Electronic Press, 2010, p. 35-47Conference paper (Other academic)
    Abstract [sv]

    Med språklagen (SFS 2009:600), som antogs i juli 2009, har terminologi blivit allt viktigare för myndigheter eftersom de enligt paragraf 12 i lagen har ett särskilt ansvar för att terminologi finns tillgänglig, men också för att den används och utvecklas. Terminologicentrum TNC och Fodina Language Technology presenterar här sitt arbete med att få igång ett fungerande erminologiarbete på myndigheter enligt språklagen med hjälp av Term-O-Stat, ett fyrstegsprogram med inslag av både maskinell terminologihantering och automatisk termextraktion samt mer traditionellt terminologiarbete. Terminologiarbete i förhållande till annat språkarbete inom myndigheter, särskilt klarspråksarbete, beskrivs också.

  • 38.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Petterstedt, Michael
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Ahrenberg, Lars
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Interactive Word Alignment for Corpus Linguistics2003In: Proceedings of Corpus Linguistics 2003, 28-31st March, 2003, Lancaster UK. UCREL Technical Papers., UCREL (University Centre for Computer Corpus Research on Language) , 2003, p. 533-542Conference paper (Refereed)
  • 39.
    Merkel, Magnus
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Petterstedt, Michael
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Jönsson, Arne
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Enhancing access to public information2002In: Proceedings of the 7th ERCIM Workshop "User Interfaces for All" Paris, France, 2002, 2002Conference paper (Refereed)
  • 40.
    Nyström, Mikael
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Merkel, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Ahrenberg, Lars
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Zweigenbaum, Pierre
    Petersson, Håkan
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Creating a medical English-Swedish dictionary using interactive word alignment2006In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 6, no 35Article in journal (Refereed)
    Abstract [en]

    Background: This paper reports on a parallel collection of rubrics from the medical terminology systems ICD-10, ICF, MeSH, NCSP and KSH97-P and its use for semi-automatic creation of an English-Swedish dictionary of medical terminology. The methods presented are relevant for many other West European language pairs than English-Swedish. Methods: The medical terminology systems were collected in electronic format in both English and Swedish and the rubrics were extracted in parallel language pairs. Initially, interactive word alignment was used to create training data from a sample. Then the training data were utilised in automatic word alignment in order to generate candidate term pairs. The last step was manual verification of the term pair candidates. Results: A dictionary of 31,000 verified entries has been created in less than three man weeks, thus with considerably less time and effort needed compared to a manual approach, and without compromising quality. As a side effect of our work we found 40 different translation problems in the terminology systems and these results indicate the power of the method for finding inconsistencies in terminology translations. We also report on some factors that may contribute to making the process of dictionary creation with similar tools even more expedient. Finally, the contribution is discussed in relation to other ongoing efforts in constructing medical lexicons for non-English languages. Conclusion: In three man weeks we were able to produce a medical English-Swedish dictionary consisting of 31,000 entries and also found hidden translation errors in the utilized medical terminology systems. © 2006 Nyström et al, licensee BioMed Central Ltd.

  • 41.
    Nyström, Mikael
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Ahrenberg, Lars
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Zweigenbaum, Pierre
    Assistance Publique-Hôpitaux de Paris, Inserm U729, Inalco CRIM.
    Petersson, Håkan
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Creating a medical English-Swedish dictionary using interactive word alignment2009In: Lexicography: The Changing Landscape / [ed] Salonee Priya, Hyderabad, India: The Icfai University Press , 2009, 1, p. 131-157Chapter in book (Other academic)
    Abstract [sv]

    Lexicography is a realm of growing academic specialization. Dictionaries map meaning onto use. We have innumerable dictionaries on different subjects and for different purposes which we keep referring to, time and again. Despite the frequency with which dictionaries are unquestioningly consulted, many have little idea of what actually goes into making them or how meanings are definitively ascertained. We have become so accustomed to using dictionaries that we fail to take notice of the effort and time spent in their making. Understanding the finer nuances of the art of dictionary-making will be of interest to everyone. With changing times and the penetration of technology, the bulkier forms of dictionaries have given way to softer forms. This book updates the reader to the changing notions of the lexicon and dictionary-making in the new realm of modern technology and newer electronic tools. The book introduces us to lexicography and leads us to dictionaries for general and specific purposes. It examines dictionary compilation and research and enables compilers, users, educators and publishers to look anew at the art of lexicography. It duly takes into account the fact that dictionaries are meant to fulfill the needs of specific user groups and reflects the same in the chapters devoted to various professional dictionaries, which have recently achieved widespread recognition in the lexicographical literature. A good read for students of linguistics, teachers and translators apart from general readers interested in knowing the intricate art of making a dictionary.

  • 42.
    Nyström, Mikael
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Merkel, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Petersson, Håkan
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Creating a medical dictionary using word alignment: The influence of sources and resources2007In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 7, no 37Article in journal (Refereed)
    Abstract [en]

    Background. Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. Methods. We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. Results. The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. Conclusion. More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10. © 2007 Nyström et al, licensee BioMed Central Ltd.

  • 43.
    Nyström, Mikael
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Merkel, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Petersson, Håkan
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Evaluating Bilingual Medical Terminologies with Word Alignment Methods2007In: Medinfo 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Health Systems / [ed] Kuhn, Klaus A; Warren, James R; Leong, Tze-Yun, Amsterdam: IOS Press, 2007, p. 244-Conference paper (Refereed)
  • 44.
    Nyström, Mikael
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Åhlfeldt, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Klein, Gunnar
    Karolinska Institutet, Solna.
    Nilsson, Gunnar
    Karolinska Institutet.
    Chen, Rong
    Karolinska Institutet, Solna.
    Ahrenberg, Lars
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Merkel, Magnus
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Halvautomatisk översättning av SNOMED CT till svenska2003In: IT i vården - terminologi, 2003Conference paper (Other academic)
1 - 44 of 44
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