Multiword Expressions (MWEs) appear frequently and ungrammatically in the natural languages. Identifying MWEs in free texts is a very challenging problem. This paper proposes a knowledge-free, training-free, and langu...
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Multiword Expressions (MWEs) appear frequently and ungrammatically in the natural languages. Identifying MWEs in free texts is a very challenging problem. This paper proposes a knowledge-free, training-free, and language-independent Multiword Expression Distance (MED). The new metric is derived from an accepted physical principle, measures the distance from an n-gram to its semantics, and outperforms other state-of-the-art methods on MWEs in two applications: question answering and named entity extraction.
The stable model semantics was recently generalized by Ferraris, Lee and Lifschitz to the full first-order language with a syntax translation approach that is very similar to McCarthy's circumscription. In this pa...
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In this paper, a novel method for robotic belt grinding based on support vector machine and particle swarm optimization algorithm is presented. Firstly, the dynamic model of the robotic belt grinding process is built ...
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CLUHSIC is a recent clustering framework that unifies the geometric, spectral and statistical views of clustering. In this paper, we show that the recently proposed discriminative view of clustering, which includes th...
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The objective of this work is to embed watermark information into digital audio data as the deterioration of sound quality is not perceivable to human ears. And hence, we consider that watermark information is embedde...
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The objective of this work is to embed watermark information into digital audio data as the deterioration of sound quality is not perceivable to human ears. Hence, we consider that watermark information is embedded in...
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Energy efficiency is one of key issues of wireless sensor network (WSN). In this paper, we propose a self-learning scheduling approach (SSA) to reduce energy consumption for wireless sensor network (WSN). This approac...
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Energy efficiency is very important for wireless sensor network (WSN). This paper presents an evolutionary self-learning scheduling approach (ESSA) to reduce energy consumption for WSN. The ESSA is based on a new prop...
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Standard binary crossover operators such as uniform and one-point crossover are referred to as being "geometric" since they always generate an offspring between its two parents under the Hamming distance. Th...
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In this paper, we focus on object feature based review summarization. Different from most of previous work with linguistic rules or statistical methods, we formulate the review mining task as a joint structure tagging...
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In this paper, we focus on object feature based review summarization. Different from most of previous work with linguistic rules or statistical methods, we formulate the review mining task as a joint structure tagging problem. We propose a new machine learning framework based on Conditional Random Fields (CRFs). It can employ rich features to jointly extract positive opinions, negative opinions and object features for review sentences. The linguistic structure can be naturally integrated into model representation. Besides linear- chain structure, we also investigate conjunction structure and syntactic tree structure in this framework. Through extensive experiments on movie review and product review data sets, we show that structure-aware models outperform many state-of-the-art approaches to review mining.
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