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  • 1.
    De Bona, Fabio
    et al.
    Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany.
    Riezler, Stefan
    Hall, Keith
    Ciaramita, Massimiliano
    Herdagdelen, Amac
    University of Trento, Rovereto, Italy.
    Holmqvist, Maria
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Learning dense models of query similarity from user click logs2010In: HLT '10: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010, p. 474-482Conference paper (Refereed)
  • 2.
    Herdagdelen, Amac
    et al.
    University of Trento, Rovereto, Italy.
    Ciaramita, Massimiliano
    Google, Zürich, Switzerland.
    Mahler, Daniel
    Google, Zürich, Switzerland.
    Holmqvist, Maria
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Hall, Keith
    Google, Zurich, Sweden .
    Riezler, Stefan
    Google, Zürich, Switzerland.
    Alfonseca, Enrique
    Google, Zürich, Switzerland.
    Generalized syntactic and semantic models of query reformulation2010In: SIGIR '10 Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, ACM Press, 2010, p. 283-290Conference paper (Other academic)
    Abstract [en]

    We present a novel approach to query reformulation which combines syntactic and semantic information by means of generalized Levenshtein distance algorithms where the substitution operation costs are based on probabilistic term rewrite functions. We investigate unsupervised, compact and efficient models, and provide empirical evidence of their effectiveness. We further explore a generative model of query reformulation and supervised combination methods providing improved performance at variable computational costs. Among other desirable properties, our similarity measures incorporate information-theoretic interpretations of taxonomic relations such as specification and generalization.

  • 3.
    Holmqvist, Maria
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Heuristic Word Alignment with Parallel Phrases2010In: Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10) / [ed] Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias, European Language Resources Association (ELRA) , 2010Conference paper (Refereed)
  • 4.
    Holmqvist, Maria
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Memory-based learning of word translation2007In: Proceedings of 16th Nordic Conference of Computational Linguistics Nodalida,2007 / [ed] oakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek, Mare Koit, Tartu: University of Tartu , 2007, p. 231-234Conference paper (Refereed)
    Abstract [en]

    A basic task in machine translation is to choose the right translation for source words with several possible translations in the target language. In this paper we treat word translation as a word sense disambiguation problem and train memory-based classifiers on words with alternative translations. The training data was automatically labeled with the corresponding translations by word-aligning a parallel corpus. Results show that many words were translated with accuracy above the baseline.

  • 5.
    Holmqvist, Maria
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Word Alignment by Re-using Parallel Phrases2008Licentiate thesis, monograph (Other academic)
    Abstract [en]

    In this thesis we present the idea of using parallel phrases for word alignment. Each parallel phrase is extracted from a set of manual word alignments and contains a number of source and target words and their corresponding alignments. If a parallel phrase matches a new sentence pair, its word alignments can be applied to the new sentence. There are several advantages of using phrases for word alignment. First, longer text segments include more  context and will be more likely to produce correct word alignments than shorter segments or single words. More importantly, the use of longer phrases makesit possible to generalize words in the phrase by replacing words by parts-of-speech or other grammatical information. In this way, the number of words covered by the extracted phrases can go beyond the words and phrases that were present in the original set of manually aligned sentences. We present  experiments with phrase-based word alignment on three types of English–Swedish parallel corpora: a software manual, a novel and proceedings of the European Parliament. In order to find a balance between improved coverage and high alignment accuracy we investigated different properties of generalised phrases to identify which types of phrases are likely to produce accurate alignments on new data. Finally, we have compared phrase-based word alignments to state-of-the-art statistical alignment with encouraging results. We show that phrase-based word alignments can be used to enhance statistical word alignment. To evaluate word alignments an English–Swedish reference set for the Europarl corpus was constructed. The guidelines for producing this reference alignment are presented in the thesis.

  • 6.
    Holmqvist, Maria
    et al.
    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.
    A Gold Standard for English-Swedish Word Alignment2011In: Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011 / [ed] Bolette Sandford Pedersen, Gunta Nepore and Inguna Skadina, Tartu, Estland, 2011, p. 106-113Conference paper (Other academic)
    Abstract [en]

    Word alignment gold standards are an importantresource for developing and evaluatingword alignment methods. In thispaper we present a free English–Swedishword alignment gold standard consistingof texts from Europarl with manually verifiedword alignments. The gold standardcontains two sets of word aligned sentences,a test set for the purpose of evaluationand a training set that can be usedfor supervised training. The guidelinesused for English–Swedish alignment werecreated based on guidelines for other languagepairs and with statistical machinetranslation as the targeted application. Wealso present results of intrinsic evaluationusing our gold standard and discuss the relationshipto extrinsic evaluation in a statisticalmachine translation system.

  • 7.
    Holmqvist, Maria
    et al.
    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.
    Stymne, Sara
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    High-precision Word Alignment with Parallel Phrases2008In: The second Swedish Language Technology Conference SLTC-08,2008, 2008, p. 45-46Conference paper (Refereed)
  • 8.
    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.
    Experiments with word alignment, normalization and clause reordering for SMT between English and German2011In: Proceedings of the Sixth Workshop on Statistical Machine Translation (WMT 2011) / [ed] Chris Callison-Burch, Philipp Koehn, Christof Monz, Omar F. Zaidan, 2011, p. 393-398Conference paper (Refereed)
    Abstract [en]

    This paper presents the LIU system for the WMT 2011 shared task for translation between German and English. For English– German we attempted to improve the translation tables with a combination of standard statistical word alignments and phrase-based word alignments. For German–English translation we tried to make the German text more similar to the English text by normalizing German morphology and performing rule-based clause reordering of the German text. This resulted in small improvements for both translation directions.

  • 9.
    Holmqvist, Maria
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory.
    Stymne, Sara
    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.
    Getting to Know Moses: Initial Experiments on German-English Factored Translation2007In: Proceedings of the Second Workshop on Statistical Machine Translation,2007, Stroudsberg, PA: Association for Computational Linguistics , 2007, p. 181-Conference paper (Refereed)
  • 10.
    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.

  • 11.
    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.
    Jody, Foo
    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.
    Improving alignment for SMT by reordering and augmenting the training corpus2009In: Proceedings of the Fourth Workshop on Statistical Machine Translation (WMT09), Athens, Greece, 2009, p. 120-124Conference paper (Refereed)
    Abstract [en]

    We describe the LIU systems for English-German and German-English translation in the WMT09 shared task. We focus on two methods to improve the word alignment: (i) by applying Giza++ in a second phase to a reordered training corpus, where reordering is based on the alignments from the first phase, and (ii) by adding lexical data obtained as high-precision alignments from a different word aligner. These methods were studied in the context of a system that uses compound processing, a morphological sequence model for German, and a part-of-speech sequence model for English. Both methods gave some improvements to translation quality as measured by Bleu and Meteor scores, though not consistently. All systems used both out-of-domain and in-domain data as the mixed corpus had better scores in the baseline configuration.

  • 12.
    Stymne, Sara
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Holmqvist, Maria
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Processing of Swedish Compounds for Phrase-Based Statistical Machine Translation2008In: Proceedings of the 12th European Association for Machine Translation Conference, Hamburg, Germany: HITEC e.V , 2008, p. 182-191Conference paper (Refereed)
    Abstract [en]

    We investigated the effects of processing Swedish compounds for phrase-based SMT between Swedish and English. Compounds were split in a pre-processing step using an unsupervised empirical method. After translation into Swedish, compounds were merged, using a novel merging algorithm. We investigated two ways of handling compound parts, by marking them as compound parts or by normalizing them to a canonical form. We found that compound splitting did improve translation into Swedish, according to automatic metrics. For translation into English the results were not consistent across automatic metrics. However, error analysis of compound translation showed a small improvement in the systems that used splitting. The number of untranslated words in the English output was reduced by 50%.

  • 13.
    Stymne, Sara
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Holmqvist, Maria
    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.
    Effects of Morphological Analysis in Translation between German and English2008In: Proceedings of the Third Workshop on Statistical Machine Translation, Stroudsburg, PA, USA: Association for Computational Linguistics, 2008, p. 135-138Conference paper (Refereed)
    Abstract [en]

    We describe the LIU systems for German-English and English-German translation submitted to the Shared Task of the Third Workshop of Statistical Machine Translation. The main features of the systems, as compared with the baseline, is the use of morphological pre- and post-processing, and a sequence model for German using morphologically richparts-of-speech. It is shown that these additions lead to improved translations.

  • 14.
    Stymne, Sara
    et al.
    Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
    Holmqvist, Maria
    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.
    Vs and OOVs: Two Problems for Translation between German and English2010In: Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR (WMT'10), 2010, p. 183-188Conference paper (Refereed)
    Abstract [en]

    In this paper we report on experiments with three preprocessing strategies for improving translation output in a statistical MT system. In training, two reordering strategies were studied: (i) reorder on thebasis of the alignments from Giza++, and (ii) reorder by moving all verbs to the end of segments. In translation, out-of-vocabulary words were preprocessed in a knowledge-lite fashion to identify a likely equivalent. All three strategies were implemented for our English-German systems submitted to the WMT10 shared task. Combining them lead to improvements in both language directions.

1 - 14 of 14
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