Distributed Representation
A multiple distributed representation method based on neural network for biomedical event extraction
... a distributed representation method which contains not only word embedding information but also task-based distributed features like trigger type on deep learning models to realize event trigger ...
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Multi-Channel Distributed Representation for Classifying Tweets by using Convolutional Neural Networks
... In experiments, we implemented methods with Keras, which is one of the well-known deep-learning frameworks. The length of input was set to 80 words, and in the embed- ding layer, the dimension of distributed ...
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Implanting Rational Knowledge into Distributed Representation at Morpheme Level
... In this paper, after constructing the Chinese lexical and semantic ontology based on word-formation, we propose a novel approach to implanting the structured rational knowl- edge into distributed ...
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Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.
... interpretable representation of data for machines that can increase performance of learning ...data-driven distributed representation for biological ...this representation qualitatively and ...
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Estimating Distributed Representation Performance in Disaster-Related Social Media Classification
... feature representation techniques that capture the structure of the text in the form of a numerical vector ...dominant representation types, recent times have seen a strong swing towards distributed ...
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Distributed Representation of Words in Cause and Effect Spaces
... This paper focuses on building up distributed representation of words in cause and effect spaces, a task-specific word embedding technique for causality. The causal embedding model is trained on a large set ...
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Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding
... compact representation because the dimensionality does not increase, and information can be queried out through the binding/unbinding operation with cue ...
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Mut2Vec: distributed representation of cancerous mutations
... generate distributed representations of mutations for the characterization of cancer ...meaningful distributed continu- ous space that places words with similar meanings close to each ...continuous ...
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Improving Distributed Representation of Word Sense via WordNet Gloss Composition and Context Clustering
... In distributed representations of word senses, each word sense is usually represented by a dense and real-valued vector in a low-dimensional space which captures the contextual semantic informa- ...sense ...
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DUTIR in BioNLP ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations
... The tasks of SeeDev-binary and BB-event both can be treated as binary relation extraction which specifics whether there is interaction between two entities. In relation extraction, the semantic and syntactic information ...
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Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media
... Inspired by the work of Limsopatham and Col- lier (2016, winner of w-nut 2016 shared task on Named Entity Recognition in Twitter), Chiu and Nichols (2016), and Huang et al (2015), we ap- proached this shared task with ...
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Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification
... Abstract: Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are ...
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Idest: Learning a Distributed Representation for Event Patterns
... Figure 5: Cluster size log-scale and ratio of unique verb lemmas in the clusters generated from NEWSSPIKE and IDEST with the REVERB extractions as input... lemmas in a cluster that are u[r] ...
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Distributed representation and estimation of WFST based n gram models
... Sharding with individual n-grams as the unit rather than working with the more complex WFST topologies does have its benefits, particularly when it comes to relatively easy balancing of shards. The primary benefit of ...
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A Distributed Representation Based Query Expansion Approach for Image Captioning
... One limitation in this work is the Out-of- Vocabulary (OOV) words, which is around 1% on average for the benchmark datasets. We omit them in our calculations, since there is no practical way to map word vectors for the ...
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Distributed Word Representation Learning for Cross Lingual Dependency Parsing
... real-valued distributed representation of words under a deep neural network architecture, which is expected to capture semantic similari- ties of words not only within the same lan- guage but also across ...
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Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation
... the distributed representation learned by the methods of Collobert et ...the distributed word representation itself as polari- ...the distributed word represen- tation, yet achieves ...
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Rethinking representation
... this representation is present in EVERY ...of distributed representation, and will crop up many times in the following ...with distributed representation models, that there is some ...
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Preferential votes and minority representation in open list proportional representation systems
... In the baseline model, parties are passive players. Given the quota system specified by the law and the assumptions about candidates’ characteristics, they essentially can play no role in deciding list composition. This ...
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Representation in the genome
... Perhaps the best way to proceed is to investigate in some detail an example that seems to offer relatively good prospects of instantiating the genetic representation of phenotypic traits. In the 1980s, researchers ...
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