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An improved Bi-LSTM performance using Dt-WE for implicit aspect extraction
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In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we trained the proposed domain-trained word embeddings (Dt-WE) model using explicit and implicit aspects. Second, interpolate Dt-WE model as a front layer in Bi-LSTM. Finally, extract implicit aspects by testing the trained architecture using the opinionated reviews that comprise multiple implicit aspects. Our model outperforms several of the current methods for implicit aspect extraction.

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Publication Date
Tue Apr 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Financial markets liquidity and their impact in the return of common stocks
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    The research aimed to study the financial markets liquidity and returns of common stocks , Take the research  the theoretical concepts  associated with each of the liquidity of financial markets and returns of common stocks , As well as the use of mathematical methods in the practical side to measure market liquidity and Stocks  Return, the community of research in listed companies in Iraqi stock exchange that have been trading on its stock and number 85 joint-stock company, The research was based to one premise, there is a statistically significant effect for the liquidity of the Iraqi stock exchange on returns  of common stocks  to traded companies in which , Using th

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