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2009
2016
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- Roland Roller, Mark Stevenson
- CLEF
- 2014
We describe our system to extract genia events that was developed for the BioNLP 2013 Shared Task. Our system uses a supervised information extraction platform based on Support Vector Machines (SVM) and separates the process of event classification into multiple stages. For each event type the SVM parameters are adjusted and feature selection carried out.… (More)
This paper describes first results using the Unified Medical Language System (UMLS) for distantly supervised relation extraction. UMLS is a large knowledge base which contains information about millions of medical concepts and relations between them. Our approach is evaluated using existing relation extraction data sets that contain relations that are… (More)
- Sebastian Möller, Robert Schleicher, +5 authors Norbert Reithinger
- 2009
We describe ongoing work to integrate statistical user models in the usability engineering process of spoken dialogue systems. The idea is to generate user dialogue actions up to the spoken utterances in response to system utterances and directly feed them to the system under test. The underlying user models are derived semi-automatically from dialogue… (More)
In this paper, we describe our contribution to the Spoken Dialog Challenge. We set up a user simulation using the large Let's Go corpus as resource to build our models. Automatic calls were made to all four dialog systems in the SDC, bus information systems that cover the schedule of Pittsburgh, PA. We discuss in detail the architecture and required setup… (More)
The increasing number of spoken dialog systems calls for efficient approaches for their development and testing. Our goal is the minimization of hand-crafted resources to maximize the portability of this evaluation environment across spoken dialog systems and domains. In this paper we discuss the user simulation technique which allows us to learn general… (More)
Automatic recognition of relationships between key entities in text is an important problem which has many applications. Supervised machine learning techniques have proved to be the most effective approach to this problem. However, they require labelled training data which may not be available in sufficient quantity (or at all) and is expensive to produce.… (More)
Distant supervision is a useful technique for creating relation classifiers in the absence of labelled data. The approaches are often evaluated using a held-out portion of the distantly labelled data, thereby avoiding the need for lablelled data entirely. However, held-out evaluation means that systems are tested against noisy data, making it difficult to… (More)
- Roland Roller, Eneko Agirre, Aitor Soroa, Mark Stevenson
- ACL
- 2015
Distant supervision is a widely applied approach to automatic training of relation extraction systems and has the advantage that it can generate large amounts of labelled data with minimal effort. However , this data may contain errors and consequently systems trained using distant supervision tend not to perform as well as those based on manually labelled… (More)
- Roland Roller, Hans Uszkoreit, +6 authors Danilo Schmidt
- 2016
In this work we present a fine-grained annotation schema to detect named entities in German clinical data of chronically ill patients with kidney diseases. The annotation schema is driven by the needs of our clinical partners and the linguistic aspects of German language. In order to generate annotations within a short period, the work also presents a… (More)