| 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. |
- Ahmed, Rafsan, et al.
(author)
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EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text
- 2023
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Other publication (other academic/artistic)abstract
- Medical research generates a large number of publications with the PubMed database already containing >35 million research articles. Integration of the knowledge scattered across this large body of literature could provide key insights into physiological mechanisms and disease processes leading to novel medical interventions. However, it is a great challenge for researchers to utilize this information in full since the scale and complexity of the data greatly surpasses human processing abilities. This becomes especially problematic in cases of extreme urgency like the COVID-19 pandemic. Automated text mining can help extract and connect information from the large body of medical research articles. The first step in text mining is typically the identification of specific classes of keywords (e.g., all protein or disease names), so called Named Entity Recognition (NER). Here we present an end-to-end pipeline for NER of typical entities found in medical research articles, including diseases, cells, chemicals, genes/proteins, and species. The pipeline can access and process large medical research article collections (PubMed, CORD-19) or raw text and incorporates a series of deep learning models fine-tuned on the HUNER corpora collection. In addition, the pipeline can perform dictionary-based NER related to COVID-19 and other medical topics. Users can also load their own NER models and dictionaries to include additional entities. The output consists of publication-ready ranked lists and graphs of detected entities and files containing the annotated texts. An associated script allows rapid inspection of the results for specific entities of interest. As model use cases, the pipeline was deployed on two collections of autophagy-related abstracts from PubMed and on the CORD19 dataset, a collection of 764 398 research article abstracts related to COVID-19.
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| 4. |
- Arrskog, Tobias, et al.
(author)
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Hyperlocal event extraction of future events
- 2012
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In: DeRiVE 2012: Detection, Representation, and Exploitation of Events in the Semantic Web (CEUR Workshop Proceedings). - 1613-0073. ; 902, s. 11-21
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Conference paper (peer-reviewed)abstract
- From metropolitan areas to tiny villages, there is a wide variety of organizers of cultural, business, entertainment, and social events. These organizers publish such information to an equally wide variety of sources. Every source of published events uses its own document structure and provides dierent sets of information. This raises signicant customization issues. This paper explores the possibilities of extracting future events from a wide range of web sources, to determine if the document structure and content can be exploited for time-ecient hyperlocal event scraping. We report on two experimental knowledge-driven, pattern-based programs that scrape events from web pages using both their content and structure.
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| 5. |
- Berglund, Anders, et al.
(author)
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A Machine Learning Approach to Extract Temporal Information from Texts in Swedish and Generate Animated 3D Scenes
- 2006
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In: Proceedings of EACL-2006, 11th Conference of the European Chapter of the Association for Computational Linguistics. ; , s. 385-392
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Conference paper (peer-reviewed)abstract
- Carsim is a program that automatically converts narratives into 3D scenes. Carsim considers authentic texts describing road accidents, generally collected from web sites of Swedish newspapers or transcribed from hand-written accounts by victims of accidents. One of the program’s key features is that it animates the generated scene to visualize events. To create a consistent animation, Carsim extracts the participants mentioned in a text and identifies what they do. In this paper, we focus on the extraction of temporal relations between actions. We first describe how we detect time expressions and events. We then present a machine learning technique to order the sequence of events identified in the narratives. We finally report the results we obtained.
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| 6. |
- Berglund, Anders, et al.
(author)
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Extraction of temporal information from texts in Swedish
- 2006
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In: Proceedings of LREC-2006, The fifth international conference on Language Resources and Evaluation. ; , s. 259-264
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Conference paper (peer-reviewed)abstract
- This paper describes the implementation and evaluation of a generic component to extract temporal information from texts in Swedish. It proceeds in two steps. The first step extracts time expressions and events, and generates a feature vector for each element it identifies. Using the vectors, the second step determines the temporal relations, possibly none, between the extracted events and orders them in time. We used a machine learning approach to find the relations between events. To run the learning algorithm, we collected a corpus of road accident reports from newspapers websites that we manually annotated. It enabled us to train decision trees and to evaluate the performance of the algorithm.
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| 7. |
- Björkelund, Anders, et al.
(author)
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A high-performance syntactic and semantic dependency parser
- 2010
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In: [Host publication title missing]. ; , s. 33-36
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Conference paper (peer-reviewed)abstract
- This demonstration presents a highperformance syntactic and semantic dependency parser. The system consists of a pipeline of modules that carry out the tokenization, lemmatization, part-of-speech tagging, dependency parsing, and semantic role labeling of a sentence. The system’s two main components draw on improved versions of a state-of-the-art dependency parser (Bohnet, 2009) and semantic role labeler (Björkelund et al., 2009) developed independently by the authors. The system takes a sentence as input and produces a syntactic and semantic annotation using the CoNLL 2009 format. The processing time needed for a sentence typically ranges from 10 to 1000 milliseconds. The predicate–argument structures in the final output are visualized in the form of segments, which are more intuitive for a user.
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| 8. |
- Björkelund, Anders, et al.
(author)
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Exploring Lexicalized Features for Coreference Resolution
- 2011
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In: CoNLL 2011 - 15th Conference on Computational Natural Language Learning: Shared Task, Proceedings. - 9781937284084 ; , s. 45-50
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Conference paper (peer-reviewed)abstract
- In this paper, we describe a coreference solver based on the extensive use of lexical features and features extracted from dependency graphs of the sentences. The solver uses Soon et al. (2001)'s classical resolution algorithm based on a pairwise classification of the mentions. We applied this solver to the closed track of the CoNLL 2011 shared task (Pradhan et al., 2011). We carried out a systematic optimization of the feature set using cross-validation that led us to retain 24 features. Using this set, we reached a MUC score of 58.61 on the test set of the shared task. We analyzed the impact of the features on the development set and we show the importance of lexicalization as well as of properties related to dependency links in coreference resolution.
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| 9. |
- Björkelund, Anders, et al.
(author)
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Knowledge and Skill Representations for Robotized Production
- 2011
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In: Proceedings of the 18th IFAC World Congress, 2011. - 1474-6670. - 9783902661937 ; , s. 8999-9004
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Conference paper (peer-reviewed)abstract
- Model-based systems in control are a means to utilize efficiently human knowledge and achieve high performance. While models consisting of formalized knowledge are used during the engineering step, running systems usually do not contain a high-level, symbolic representation of the control and most of its properties, typically named numerical parameters. On a system level and beyond the plant data, there is also a need to represent the meaning of the data such that deployment and fault analysis could be augmented with partly automated inference based on the semantics of the data. To that end, we extended the formalized knowledge traditionally used in control to include the control purpose, engineering assumption, quality, involved state machines, and so on. We then represented the control semantics in a format that allows an easier extraction of information using querying and reasoning. It aims at making knowledge in control engineering reusable so that it can be shipped together with the control systems. We implemented prototypes that include automatic conversion of plant data from AutomationML into RDF triples, as well as the automated extraction of control properties, the conversion of parameters, and their storage in the same triple store. Although these techniques are standard within the semantic web community, we believe that our robotic prototypes for semantic control represent a novel approach.
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| 10. |
- Björkelund, Anders, et al.
(author)
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Knowledge for Intelligent Industrial Robots
- 2012
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In: AAAI Technical Report SS-12-02, Designing Intelligent Robots: Reintegrating AI. - 9781577355519 ; SS-12-02
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Conference paper (peer-reviewed)abstract
- This paper describes an attempt to provide more intelligence to industrial robotics and automation systems. We develop an architecture to integrate disparate knowledge representations used in different places in robotics and automation. This knowledge integration framework, a possibly distributed entity, abstracts the components used in design or production as data sources, and provides a uniform access to them via standard interfaces. Representation is based on the ontology formalizing the process, product and resource triangle, where skills are considered the common element of the three. Production knowledge is being collected now and a preliminary version of KIF undergoes verification.
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