Matches in ScholarlyData for { ?s ?p ?o. }
- 149 hasTopic Linguistics.
- 149 isPartOf proceedings.
- 149 keyword "Corpus (creation, annotation, etc.)".
- 149 keyword "Dialogue & Natural Interactivity".
- 149 keyword "Emotions".
- 149 title "On the Role of the NIMITEK Corpus in Developing an Emotion Adaptive Spoken Dialogue System".
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- item-1 hasContent milan-gnjatovic.
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- item-2 hasContent dietmar-roesner.
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- 15 creator dimitrios-kokkinakis.
- 15 type InProceedings.
- 15 label "A Semantically Annotated Swedish Medical Corpus".
- 15 sameAs 15.
- 15 abstract "With the information overload in the life sciences there is an increasing need for annotated corpora, particularly with biological and biomedical entities, which is the driving force for data-driven language processing applications and the empirical approach to language study. Inspired by the work in the GENIA Corpus, which is one of the very few of such corpora, extensively used in the biomedical field, and in order to fulfil the needs of our research, we have collected a Swedish medical corpus, the MEDLEX Corpus. MEDLEX is a large structurally and linguistically annotated document collection, consisting of a variety of text documents related to various medical text subfields, and does not focus at a particular medical genre, due to the lack of large Swedish resources within a particular medical subdomain. Out of this collection we selected 300 documents which were manually examined by two human experts who inspected, corrected and/or accordingly modified the automatically provided annotations according to a set of provided labelling guidelines. The annotations consist of medical terminology provided by the Swedish and English MeSH© (Medical Subject Headings) thesauri as well as named entity labels provided by an enhanced named entity recognition software.".
- 15 hasAuthorList authorList.
- 15 hasTopic Linguistics.
- 15 isPartOf proceedings.
- 15 keyword "Corpus (creation, annotation, etc.)".
- 15 keyword "Named Entity recognition".
- 15 keyword "Semantics".
- 15 title "A Semantically Annotated Swedish Medical Corpus".
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- item-1 hasContent dimitrios-kokkinakis.
- 152 creator oana-frunza.
- 152 type InProceedings.
- 152 label "A Trainable Tokenizer, solution for multilingual texts and compound expression tokenization".
- 152 sameAs 152.
- 152 abstract "Tokenization is one of the initial steps done for almost any text processing task. It is not particularly recognized as a challenging task for English monolingual systems but it rapidly increases in complexity for systems that apply it for different languages. This article proposes a supervised learning approach to perform the tokenization task. The method presented in this article is based on character transitions representation, a representation that allows compound expressions to be recognized as a single token. Compound tokens are identified independent of the character that creates the expression. The method automatically learns tokenization rules from a pre-tokenized corpus. The results obtained using the trainable system show that for Romanian and English a statistical significant improvement is obtained over a baseline system that tokenizes texts on every non-alphanumeric character.".
- 152 hasAuthorList authorList.
- 152 hasTopic Linguistics.
- 152 isPartOf proceedings.
- 152 keyword "Acquisition, Machine Learning".
- 152 keyword "Multilinguality".
- 152 keyword "Text mining".
- 152 title "A Trainable Tokenizer, solution for multilingual texts and compound expression tokenization".
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- item-1 hasContent oana-frunza.
- 154 creator els-lefever.
- 154 creator isabelle-delaere.
- 154 creator klaar-vanopstal.
- 154 creator veronique-hoste.
- 154 type InProceedings.
- 154 label "Learning-based Detection of Scientific Terms in Patient Information".
- 154 sameAs 154.
- 154 abstract "In this paper, we investigate the use of a machine-learning based approach to the specific problem of scientific term detection in patient information. Lacking lexical databases which differentiate between the scientific and popular nature of medical terms, we used local context, morphosyntactic, morphological and statistical information to design a learner which accurately detects scientific medical terms. This study is the first step towards the automatic replacement of a scientific term by its popular counterpart, which should have a beneficial effect on readability. We show a F-score of 84% for the prediction of scientific terms in an English and Dutch EPAR corpus. Since recasting the term extraction problem as a classification problem leads to a large skewedness of the resulting data set, we rebalanced the data set through the application of some simple TF-IDF-based and Log-likelihood-based filters. We show that filtering indeed has a beneficial effect on the learner s performance. However, the results of the filtering approach combined with the learning-based approach remain below those of the learning-based approach.".
- 154 hasAuthorList authorList.
- 154 hasTopic Linguistics.
- 154 isPartOf proceedings.
- 154 keyword "Acquisition, Machine Learning".
- 154 keyword "Lexicon, lexical database".
- 154 keyword "Tools, systems, applications".
- 154 title "Learning-based Detection of Scientific Terms in Patient Information".
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- item-1 hasContent veronique-hoste.
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- item-3 hasContent klaar-vanopstal.
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- item-4 hasContent isabelle-delaere.
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- 155 creator guillaume-gravier.
- 155 creator gwenole-lecorve.
- 155 creator pascale-sebillot.
- 155 type InProceedings.
- 155 label "On the Use of Web Resources and Natural Language Processing Techniques to Improve Automatic Speech Recognition Systems".
- 155 sameAs 155.
- 155 abstract "Language models used in current automatic speech recognition systems are trained on general-purpose corpora and are therefore not relevant to transcribe spoken documents dealing with successive precise topics, such as long multimedia streams, frequently tacking reportages and debates. To overcome this problem, this paper shows that Web resources and natural language processing techniques can be effective to automatically adapt the baseline language model of an automatic speech recognition system to any encountered topic. More precisely, we detail how to characterize the topic of transcription segment and how to collect Web pages from which a topic-specific language model can be trained. Then, an adapted language model is obtained by combining the topic-specific language model with the general-purpose language model. Finally, new transcriptions are generated using the adapted language model and are compared with transcriptions previously obtained with the baseline language model. Experiments show that our topic adaptation technique leads to significant transcription quality gains.".
- 155 hasAuthorList authorList.
- 155 hasTopic Linguistics.
- 155 isPartOf proceedings.
- 155 keyword "Information Extraction, Information Retrieval".