| 1. |
- Granfeldt, Jonas, et al.
(author)
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CEFLE and Direkt Profil: a new computer learner corpus in French L2 and a system for grammatical profiling
- 2006
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In: Proceedings of the 5th International Conference on Language Resources and Evaluation. ; , s. 565-570
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Conference paper (peer-reviewed)abstract
- Abstract in UndeterminedThe importance of computer learner corpora for research in both second language acquisition and foreign language teaching is rapidly increasing. Computer learner corpora can provide us with data to describe the learner's interlanguage system at different points of its development and they can be used to create pedagogical tools.In this paper, we first present a new computer learner corpora in French. We then describe an analyzer called Direkt Profil, that we have developed using this corpus. The system carries out a sentence analysis based on developmental sequences, i.e. local morphosyntactic phenomena linked to a development in the acquisition of French as a foreign language. We present a brief introduction to developmental sequences and some examples in French. In the final section, we introduce and evaluate a method to optimize the definition and detection of learner profiles using machine-learning techniques.
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| 2. |
- Johansson, Richard, et al.
(author)
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Using WordNet to Extend FrameNet Coverage
- 2007
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In: LU-CS-TR: 2007-240. - 9789197693905 ; , s. 27-30
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Conference paper (peer-reviewed)abstract
- We present two methods to address the problem of sparsity in the FrameNet lexical database. The first method is based on the idea that a word that belongs to a frame is ``similar'' to the other words in that frame. We measure the similarity using a WordNet-based variant of the Lesk metric. The second method uses the sequence of synsets in WordNet hypernym trees as feature vectors that can be used to train a classifier to determine whether a word belongs to a frame or not. The extended dictionary produced by the second method was used in a system for FrameNet-based semantic analysis and gave an improvement in recall. We believe that the methods are useful for bootstrapping FrameNets for new languages.
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| 3. |
- Medved, Dennis, et al.
(author)
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Selection of an optimal feature set to predict heart transplantation outcomes
- 2016
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In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. - 1557-170X. - 9781457702204 ; 2016-October, s. 3290-3293
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Conference paper (peer-reviewed)abstract
- Heart transplantation (HT) is a life saving procedure, but a limited donor supply forces the surgeons to prioritize the recipients. The understanding of factors that predict mortality could help the doctors with this task. The objective of this study is to find locally optimal feature sets to predict survival of HT patients for different time periods. To this end, we applied logistic regression together with a greedy forward and backward search. As data source, we used the United Network for Organ Sharing (UNOS) registry, where we extracted adult patients (>17 years) from January 1997 to December 2008. As methods to predict survival, we used the Index for Mortality Prediction After Cardiac Transplantation (IMPACT) and the International Heart Transplant Survival Algorithm (IHTSA). We used the LIBLINEAR library together with the Apache Spark cluster computing framework to carry out the computation and we found feature sets for 1, 5, and 10 year survival for which we obtained area under the ROC curves (AUROC) of 68%, 68%, and 76%, respectively.
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| 4. |
- Ek, Tobias, et al.
(author)
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Named Entity Recognition for Short Text Messages
- 2011
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In: Computational Linguistics and Related Fields. - : Elsevier BV. - 1877-0428. ; 27, s. 178-187
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Conference paper (peer-reviewed)abstract
- This paper describes a named entity recognition (NER) system for short text messages (SMS) running on a mobile platform. Most NER systems deal with text that is structured, formal, well written, with a good grammatical structure, and few spelling errors. SMS text messages lack these qualities and have instead a short-handed and mixed language studded with emoticons, which makes NER a challenge on this kind of material. We implemented a system that recognizes named entities from SMSes written in Swedish and that runs on an Android cellular telephone. The entities extracted are locations, names, dates, times, and telephone numbers with the idea that extraction of these entities could be utilized by other applications running on the telephone. We started from a regular expression implementation that we complemented with classifiers using logistic regression. We optimized the recognition so that the incoming text messages could be processed on the telephone with a fast response time. We reached an F-score of 86 for strict matches and 89 for partial matches. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of PACLING Organizing Committee.
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| 5. |
- Klang, Marcus, et al.
(author)
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Docforia: A Multilayer Document Model
- 2017
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In: Proceedings of the 21st Nordic Conference of Computational Linguistics. - 1650-3740 .- 1650-3686. ; 131, s. 226-230
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Conference paper (peer-reviewed)abstract
- In this paper, we describe Docforia, a multilayer document model and application programming interface (API) to store formatting, lexical, syntactic, and semantic annotations on Wikipedia and other kinds of text and visualize them. While Wikipedia has become a major NLP resource, its scale and heterogeneity makes it relatively difficult to do experimentations on the whole corpus. These experimentations are rendered even more complexas,to the best of our knowledge,there is no available tool to visualize easily the results of a processing pipeline. We designed Docforia so that it can store millions of documents and billions of tokens, annotated using different processing tools,that themselves use multiple formats, and compatible with cluster computing frameworks such as Hadoop or Spark. The annotation output, either partial or complete, can then be shared more easily. To validate Docforia, we processed six language versions of Wikipedia: English, French, German, Spanish, Russian, and Swedish, up to semantic role labeling, depending on the NLP tools available for a given language. We stored the results in our document model and we created a visualization tool to inspect the annotation results.
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| 6. |
- Borin, Lars, 1957, et al.
(author)
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Mining semantics for culturomics: towards a knowledge-based approach
- 2013
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In: 2013 ACM International Workshop on Mining Unstructured Big Data Using Natural Language Processing, UnstructureNLP 2013, Held at 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013; San Francisco, CA; United States; 28 October 2013 through 28 October 2013. - New York, NY, USA : ACM. - 9781450324151 ; , s. 3-10
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Conference paper (peer-reviewed)abstract
- The massive amounts of text data made available through the Google Books digitization project have inspired a new field of big-data textual research. Named culturomics, this field has attracted the attention of a growing number of scholars over recent years. However, initial studies based on these data have been criticized for not referring to relevant work in linguistics and language technology. This paper provides some ideas, thoughts and first steps towards a new culturomics initiative, based this time on Swedish data, which pursues a more knowledge-based approach than previous work in this emerging field. The amount of new Swedish text produced daily and older texts being digitized in cultural heritage projects grows at an accelerating rate. These volumes of text being available in digital form have grown far beyond the capacity of human readers, leaving automated semantic processing of the texts as the only realistic option for accessing and using the information contained in them. The aim of our recently initiated research program is to advance the state of the art in language technology resources and methods for semantic processing of Big Swedish text and focus on the theoretical and methodological advancement of the state of the art in extracting and correlating information from large volumes of Swedish text using a combination of knowledge-based and statistical methods.
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| 7. |
- Johansson, Richard, et al.
(author)
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Syntactic Representations Considered for Frame-Semantic Analysis
- 2007
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In: Proceedings of the Sixth International Workshop on Treebanks and Linguistic Theories. ; 1, s. 61-72
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Conference paper (peer-reviewed)abstract
- We address the question of which syntactic representation is best suited for FrameNet-based semantic analysis of English text. We compare analyzers based on dependencies and constituents, and a dependency syntax with a rich set of grammatical functions with one with a smaller set. Our study demonstrates that dependency-based and constituent-based analyzers give roughly equivalent performance, and that a richer set of functions has a positive influence on argument classification for verbs.
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| 8. |
- Klang, Marcus, et al.
(author)
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Hedwig : A named entity linker
- 2020
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In: LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. - 9791095546344 ; , s. 4501-4508
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Conference paper (peer-reviewed)abstract
- Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.
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| 9. |
- Weegar, Rebecka, et al.
(author)
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Linking Entities Across Images and Text
- 2015
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In: Proceedings of the 19th Conference on Computational Language Learning. - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781941643778 ; , s. 185-193
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Conference paper (peer-reviewed)abstract
- This paper describes a set of methods to link entities across images and text. Asa corpus, we used a data set of images, where each image is commented by a short caption and where the regions in the images are manually segmented and labeled with a category. We extracted the entity mentions from the captions and we computed a semantic similarity between the mentions and the region labels. We also measured the statistical associations between these mentions and the labels and we combined them with the semantic similarity to produce mappings in the form of pairs consisting of a region label and a caption entity. In a second step, we used the syntactic relationships between the mentions and the spatial relationships between the regions to rerank the lists of candidate mappings. To evaluate our methods, we annotated a test set of 200 images, where we manually linked the image regions to their corresponding mentions in the captions. Eventually, we could match objects in pictures to their correct mentions for nearly 89 percent of the segments, when such a matching exists.
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| 10. |
- Medved, Dennis, et al.
(author)
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Predicting the Outcome for Patients in a Heart Transplantation Queue using Deep Learning
- 2017
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In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : Smarter Technology for a Healthier World, EMBC 2017 - Proceedings - Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. - 1557-170X. - 9781509028092 ; , s. 74-77
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Conference paper (peer-reviewed)abstract
- Heart transplantations have made it possible to extend the median survival time to 12 years for patients with end-stage heart diseases. This operation is unfortunately limited by the availability of donor organs and patients have to wait on average about 200 days in a waiting list before being operated. This waiting time varies considerably across the patients. In this paper, we studied the outcome for patients entering a transplantation waiting list using deep learning techniques. We implemented a model in the form of two-layer neural networks and we predicted the outcome as still waiting, transplanted or dead in the waiting list, at three different time points: 180 days, 365 days, and 730 days. As data source, we used the United Network for Organ Sharing (UNOS) registry, where we extracted adult patients (>17 years) from January 2000 to December 2011. We trained our model using the Keras framework, and we report F1 macro scores of respectively 0.674, 0.680, and 0.680 compared to a baseline of 0.271. We also applied a backward elimination procedure, using our neural network, to extract the 10 most significant parameters predicting the patient status for the three different time points.
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