This paper describes our system used in the Aspect Based Sentiment Analysis Task 4 at the SemEval-2014. Our system consists of two components to address two of the subtasks respectively: a Conditional Random Field (CRF) based classifier for Aspect Term Extraction (ATE) and a linear classifier for Aspect Term Polarity Classification (ATP). For the ATE subtask, we implement a variety of lexicon, ...

We explore the connection between term rewriting systems (TRS) and aspect-oriented programming (AOP). Term rewriting is a paradigm that is used in fields such as program transformation and theorem proving. AOP is a method for decomposing software, complementary to the usual separation into programs, classes, functions, etc. An aspect represents code that is scattered across the components of an...

Abstract Aspect term extraction (ATE) aims at identifying the aspect terms that are expressed in a sentence. Recently, Seq2Seq learning has been employed ATE and significantly improved performance. However, it suffers from some weaknesses, such as lacking ability to encode more informative information integrate of surrounding words encoder. The static word embeddings fall short modeling dynamic...

Aspect-based sentiment analysis (ABSA) task is a fine-grained of natural language processing and consists two subtasks: aspect term extraction (ATE) polarity classification (APC). Most the related works merely focus on subtask Chinese inferring fail to emphasize research Chinese-oriented ABSA multi-task learning. Based local context (LCF) mechanism, this paper firstly proposes learning model fo...

Aspect-based sentiment analysis (ABSA) is a method used to identify the aspects discussed in given text and determine expressed towards each aspect. This can help provide more fine-grained understanding of opinions text. The majority Arabic ABSA techniques use today significantly rely on repeated pre-processing feature-engineering operations, as well outside resources (e.g., lexicons). In essen...

high resolution range profile (hrrp) is being known as one of the most powerful tools for radar target recognition. the main problem with range profile for radar target recognition is its sensitivity to aspect angle. to overcome this problem, consecutive samples of hrrp (or feature vectors extracted from them)were assumed to be identically independently distributed (iid) in small frames of aspe...