Martin Hassel


2011

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Exploiting Structured Data, Negation Detection and SNOMED CT Terms in a Random Indexing Approach to Clinical Coding
Aron Henriksson | Martin Hassel
Proceedings of the Second Workshop on Biomedical Natural Language Processing

2010

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Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
Hercules Dalianis | Martin Hassel | Gunnar Nilsson
Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents

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Characteristics and Analysis of Finnish and Swedish Clinical Intensive Care Nursing Narratives
Helen Allvin | Elin Carlsson | Hercules Dalianis | Riitta Danielsson-Ojala | Vidas Daudaravicius | Martin Hassel | Dimitrios Kokkinakis | Heljä Lundgren-Laine | Gunnar Nilsson | Øystein Nytrø | Sanna Salanterä | Maria Skeppstedt | Hanna Suominen | Sumithra Velupillai
Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents

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Uncertainty Detection as Approximate Max-Margin Sequence Labelling
Oscar Täckström | Sumithra Velupillai | Martin Hassel | Gunnar Eriksson | Hercules Dalianis | Jussi Karlgren
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

2009

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Identification of Parallel Text Pairs Using Fingerprints
Martin Hassel | Hercules Dalianis
Proceedings of the International Conference RANLP-2009

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Global Evaluation of Random Indexing through Swedish Word Clustering Compared to the People’s Dictionary of Synonyms
Magnus Rosell | Martin Hassel | Viggo Kann
Proceedings of the International Conference RANLP-2009

2006

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Towards Holistic Summarization – Selecting Summaries, Not Sentences
Martin Hassel | Jonas Sjöbergh
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper we present a novel method for automatic text summarization through text extraction, using computational semantics. The new idea is to view all the extracted text as a whole and compute a score for the total impact of the summary, instead of ranking for instance individual sentences. A greedy search strategy is used to search through the space of possible summaries and select the summary with the highest score of those found. The aim has been to construct a summarizer that can be quickly assembled, with the use of only a very few basic language tools, for languages that lack large amounts of structured or annotated data or advanced tools for linguistic processing. The proposed method is largely language independent, though we only evaluate it on English in this paper, using ROUGE-scores on texts from among others the DUC 2004 task 2. On this task our method performs better than several of the systems evaluated there, but worse than the best systems.

2004

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FarsiSum - A Persian Text Summarizer
Martin Hassel | Nima Mazdak
Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages