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.

We present the TÜBİTAK-UEKAE statistical machine translation system that participated in the IWSLT 2008 evaluation campaign. Our system is based on the open-source phrase-based statistical machine translation software Moses. Additionally, phrase-table augmentation is applied to maximize source language coverage; lexical approximation is applied to replace out-of-vocabulary words with known words prior to decoding; and automatic punctuation insertion is improved. We describe the preprocessing and postprocessing steps and our training and decoding procedures. Results are presented on our participation in the classical Arabic-English and Chinese-English tasks as well as the new Chinese-Spanish direct and Chinese-English-Spanish pivot translation tasks.