Distributed Representation (connectionism)
A multiple distributed representation method based on neural network for biomedical event extraction
... proposed distributed representation, including depen- dent context which was formed by word embedding and task-based features on the deep learning methods, works well on biomedical event extraction tasks ...
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Multi-Channel Distributed Representation for Classifying Tweets by using Convolutional Neural Networks
... of distributed representation in an embedding layer is set up an integer value ...of distributed representation to extract full information of a ...
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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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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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A Distributed Representation Based Query Expansion Approach for Image Captioning
... As mentioned earlier, a number of studies pose im- age captioning as a caption transfer problem by relying on the assumption that visually similar im- ages generally contain very similar captions. The pioneering work in ...
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Distributed representation and estimation of WFST based n gram models
... In this paper, we present methods for the dis- tributed representation and processing of large WFST-based n-gram language models by parti- tioning them into multiple blocks or shards. Our sharding approach meets ...
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Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding
... to representation evaluation: RSA provides a verification technique for HTM and also provides a way to observe high-dimensional geometric changes in the ...Sparse Distributed Representations in the HTM ...
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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
... Instead of complicate hand-designed feature en- gineering, we employed the distributed semantic representation and CNN model to extract binary relations between entities. SeeDev-binary task and BB-event ...
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Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media
... ic representation for each tweet or social media ...topic representation for each tweet derived from LDA modelling as a feature for each word in a tweet or ...
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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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This is how we do it: Answer Reranking for Open domain How Questions with Paragraph Vectors and Minimal Feature Engineering
... We present a simple yet powerful approach to non-factoid answer reranking whereby question-answer pairs are represented by con- catenated distributed representation vectors and a multilayer perceptron is ...
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Learning Salient Samples and Distributed Representations for Topic Based Chinese Message Polarity Classification
... the distributed representation of Chinese words through unsupervised fea- ture learning and the annotation of salient samples through active learning, with a raw corpus of over 90 million messages extracted ...
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Event Embeddings for Semantic Script Modeling
... Manshadi et al. (2008) proposed language model based approach to learning event se- quences, in their approach as well, events are treated as atomic units (a predicate-argument tu- ple). Recently, Rudinger et al. (2015b) ...
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Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation
... a distributed representation vector with continuous real values for each observation word as generalizable features, which has the ca- pacity of capturing multi-aspect latent characteris- tics of the word ...
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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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Unsupervised Text Normalization Using Distributed Representations of Words and Phrases
... We also performed sentence (tweet) level nor- malization on Twitter data. We manually an- notated (expanded abbreviations, shorthands and spelling errors) 1000 tweets and performed nor- malization using our approach. The ...
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Adaptive pattern recognition in a real world environment
... Pattern Classification = Local Representations Pattern Recall = Distributed Representation Exhibit 1.1 There is a one to one correspondence between the way information is represented in [r] ...
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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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