Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network
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SemEval
Sentiment Analysis
ENCODE
Tree (set theory)
SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.
SemEval
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This article illustrates the use of the Encyclopedia of DNA Elements (ENCODE) resource to generate or refine hypotheses from genomic data on disease and other phenotypic traits. First, the goals and history of ENCODE and related epigenomics projects are reviewed. Second, the rationale for ENCODE and the major data types used by ENCODE are briefly described, as are some standard heuristics for their interpretation. Third, the use of the ENCODE resource is examined. Standard use cases for ENCODE, accessing the ENCODE resource, and accessing data from related projects are discussed. Although the focus of this article is the use of ENCODE data, some of the same approaches can be used with data from other projects.
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Encyclopedia
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This paper describes the participation of ELiRF-UPV team at tasks 1 and 3 of Semeval-2018. We present a deep learning based system that assembles Convolutional Neural Networks and Long Short-Term Memory neural networks. This system has been used with slight modifications for the two tasks addressed both for English and Spanish. Finally, the results obtained in the competition are reported and discussed.
SemEval
Affect
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This paper presents the coarse-grained English all-words task at SemEval-2007. We describe our experience in producing a coarse version of the WordNet sense inventory and preparing the sense-tagged corpus for the task. We present the results of participating systems and discuss future directions.
SemEval
Word Sense Disambiguation
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The ENCODE (Encyclopedia of DNA Elements) project, started in 2003, is a consortium of 442 scientists from around the world working together to assign a function to the DNA that does not encode genes. ENCODE used 147 different cell types and many different research techniques to achieve their goal. On September 5, 2012, ENCODE released the initial results of their study. The purpose of this review is to summarize a fraction of ENCODE’s results.
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Encyclopedia
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SemEval
Word Sense Disambiguation
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This paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.
SemEval
Representation
Sentiment Analysis
Microblogging
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SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.
SemEval
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A new encode/decode scheme of OCDMA—films encode/decoder is proposed. The principle of films encode/decoder is analyzed. The system structure of films encode/decoder is given. It can also be seen that all optical CDMA encode/decode can be realized by the proposed system of films encode/decoder.At the same time, the OCDMA encode/decoder can be integrated easily.It can also made the access be controlled easily.
ENCODE
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ENCODE
Gene density
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