Bootstrapping a Verb Lexicon for Biomedical Information Extraction

The accurate extraction of information from texts requires both syntactic and semantic resources. We are developing a verb dictionary for use in the processing of biomedical texts that includes both syntactic subcategorisation frames and semantic event frames, and links them together. In this paper, we describe the acquisition of syntactic subcategorisation frames from a large corpus of abstracts of the subject of E. Coli, together with the extraction of linguistic event frames from a subset of this corpus, in which the biological process of E. coli gene regulation has been linguistically annotated by a group of biologists. Finally, we report on work carried out to link the syntactic and semantic information together, by mapping syntactic arguments of subcategorisation frames to semantic arguments of the event frames

FrameNet CNL: a Knowledge Representation and Information Extraction Language

The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL. The framework is used on natural language documents and represents the extracted knowledge in a tailor-made Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be generated automatically in multiple languages. This approach brings together the fields of information extraction and CNL, because a source text can be considered belonging to FrameNet-CNL, if information extraction parser produces the correct knowledge representation as a result. We describe a state-of-the-art information extraction parser used by a national news agency and speculate that FrameNet-CNL eventually could shape the natural language subset used for writing the newswire articles.Comment: CNL-2014 camera-ready version. The final publication is available at link.springer.co

A semantic feature for human motion retrieval

With the explosive growth of motion capture data, it becomes very imperative in animation production to have an efficient search engine to retrieve motions from large motion repository. However, because of the high dimension of data space and complexity of matching methods, most of the existing approaches cannot return the result in real time. This paper proposes a high level semantic feature in a low dimensional space to represent the essential characteristic of different motion classes. On the basis of the statistic training of Gauss Mixture Model, this feature can effectively achieve motion matching on both global clip level and local frame level. Experiment results show that our approach can retrieve similar motions with rankings from large motion database in real-time and also can make motion annotation automatically on the fly. Copyright © 2013 John Wiley & Sons, Ltd

Towards Semantic Fast-Forward and Stabilized Egocentric Videos

The emergence of low-cost personal mobiles devices and wearable cameras and the increasing storage capacity of video-sharing websites have pushed forward a growing interest towards first-person videos. Since most of the recorded videos compose long-running streams with unedited content, they are tedious and unpleasant to watch. The fast-forward state-of-the-art methods are facing challenges of balancing the smoothness of the video and the emphasis in the relevant frames given a speed-up rate. In this work, we present a methodology capable of summarizing and stabilizing egocentric videos by extracting the semantic information from the frames. This paper also describes a dataset collection with several semantically labeled videos and introduces a new smoothness evaluation metric for egocentric videos that is used to test our method.Comment: Accepted for publication and presented in the First International Workshop on Egocentric Perception, Interaction and Computing at European Conference on Computer Vision (EPIC@ECCV) 201