Jan Tore Lønning


2019

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Reinforcement-based denoising of distantly supervised NER with partial annotation
Farhad Nooralahzadeh | Jan Tore Lønning | Lilja Øvrelid
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

Existing named entity recognition (NER) systems rely on large amounts of human-labeled data for supervision. However, obtaining large-scale annotated data is challenging particularly in specific domains like health-care, e-commerce and so on. Given the availability of domain specific knowledge resources, (e.g., ontologies, dictionaries), distant supervision is a solution to generate automatically labeled training data to reduce human effort. The outcome of distant supervision for NER, however, is often noisy. False positive and false negative instances are the main issues that reduce performance on this kind of auto-generated data. In this paper, we explore distant supervision in a supervised setup. We adopt a technique of partial annotation to address false negative cases and implement a reinforcement learning strategy with a neural network policy to identify false positive instances. Our results establish a new state-of-the-art on four benchmark datasets taken from different domains and different languages. We then go on to show that our model reduces the amount of manually annotated data required to perform NER in a new domain.

2018

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SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers
Farhad Nooralahzadeh | Lilja Øvrelid | Jan Tore Lønning
Proceedings of The 12th International Workshop on Semantic Evaluation

This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest dependency path (sdp) between two entities, then we introduce a convolutional neural network (CNN) which takes the shortest dependency path embeddings as input and performs relation classification with differing objectives for each subtask of the shared task. This approach achieved overall F1 scores of 76.7 and 83.2 for relation classification on clean and noisy data, respectively. Furthermore, for combined relation extraction and classification on clean data, it obtained F1 scores of 37.4 and 33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared task.

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Evaluation of Domain-specific Word Embeddings using Knowledge Resources
Farhad Nooralahzadeh | Lilja Øvrelid | Jan Tore Lønning
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2009

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A Minimal Recursion Semantic Analysis of Locatives
Fredrik Jørgensen | Jan Tore Lønning
Computational Linguistics, Volume 35, Number 2, June 2009 - Special Issue on Prepositions

2008

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Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Johan Bos | Edward Briscoe | Aoife Cahill | John Carroll | Stephen Clark | Ann Copestake | Dan Flickinger | Josef van Genabith | Julia Hockenmaier | Aravind Joshi | Ronald Kaplan | Tracy Holloway King | Sandra Kuebler | Dekang Lin | Jan Tore Lønning | Christopher Manning | Yusuke Miyao | Joakim Nivre | Stephan Oepen | Kenji Sagae | Nianwen Xue | Yi Zhang
Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation

2006

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Re-Usable Tools for Precision Machine Translation
Jan Tore Lønning | Stephan Oepen
Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions

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Discriminant-Based MRS Banking
Stephan Oepen | Jan Tore Lønning
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We present an approach to discriminant-based MRS banking, i.e. the construction of an annotated corpus where each input item is paired with a logical-form semantics. Semantic annotations are produced by parsing with a broad-coverage precision grammar, followed by manual disambiguation. The selection of the preferred analysis for each item (and hence its semantic form) builds on a notion of semantic discriminants, essentially localized dependencies extracted from a full-fledged, underspecified semantic representation.

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Using a Bi-Lingual Dictionary in Lexical Transfer
Lars Nygaard | Jan Tore Lønning | Torbjørn Nordgård | Stephan Oepen
Proceedings of the 11th Annual conference of the European Association for Machine Translation

2005

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Holistic regression testing for high-quality MT: some methodological and technological reflections
Stephan Oepen | Helge Dyvik | Dan Flickinger | Jan Tore Lønning | Paul Meurer | Victoria Rosén
Proceedings of the 10th EAMT Conference: Practical applications of machine translation

1989

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Computational Semantics of Mass Terms
Jan Tore Lønning
Fourth Conference of the European Chapter of the Association for Computational Linguistics