Tim Furche


Structured Aspect Extraction
Omer Gunes | Tim Furche | Giorgio Orsi
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Aspect extraction identifies relevant features from a textual description of an entity, e.g., a phone, and is typically targeted to product descriptions, reviews, and other short texts as an enabling task for, e.g., opinion mining and information retrieval. Current aspect extraction methods mostly focus on aspect terms and often neglect interesting modifiers of the term or embed them in the aspect term without proper distinction. Moreover, flat syntactic structures are often assumed, resulting in inaccurate extractions of complex aspects. This paper studies the problem of structured aspect extraction, a variant of traditional aspect extraction aiming at a fine-grained extraction of complex (i.e., hierarchical) aspects. We propose an unsupervised and scalable method for structured aspect extraction consisting of statistical noun phrase clustering, cPMI-based noun phrase segmentation, and hierarchical pattern induction. Our evaluation shows a substantial improvement over existing methods in terms of both quality and computational efficiency.


EAGER: Extending Automatically Gazetteers for Entity Recognition
Omer Farukhan Gunes | Tim Furche | Christian Schallhart | Jens Lehmann | Axel-Cyrille Ngonga Ngomo
Proceedings of the 3rd Workshop on the People’s Web Meets NLP: Collaboratively Constructed Semantic Resources and their Applications to NLP