Embedding Learning for Classification
Learning Sentiment Specific Word Embedding for Twitter Sentiment Classification
... Table 4: Statistics of the sentiment lexicons. Join- t stands for the words that occur in both HL and MPQA with the same sentiment polarity. Results. Table 5 shows our results com- pared to other word embedding ...
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Classification of Transversal Dominic Sense Using Sensitive Sensitive Embedding
... task-specific embedding learning for cross-domain sentiment ...sentiment classification accuracies that are statistically comparable to the current state-of-the-art embedding learning ...
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Embedding Learning Through Multilingual Concept Induction
... A positive vs. negative classification is not rea- sonable for the New Testament because a large number of verses is mixed, e.g., “Now is come salvation . . . the power of his Christ: for the ac- cuser . . . cast ...
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Adaptive Region Embedding for Text Classification
... by learning to predict the neighbours of the word unsupervis- edly, and use that extra network to produce context-related features as additional ...joint embedding of words and la- ...region ...
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Task oriented Word Embedding for Text Classification
... the classification task as an ...the embedding space. To obtain a good classification performance, the expected word distribu- tion should have a clear classification boundary: words within ...
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Joint Embedding of Words and Labels for Text Classification
... Testing accuracy Simple compositional meth- ods indeed achieve comparable performance as the sophisticated deep CNN/RNN models. On the other hand, deep hierarchical attention model can improve the pure CNN/RNN models. ...
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Multi Task Label Embedding for Text Classification
... Multi-task learning in text classification lever- ages implicit correlations among related tasks to extract common features and yield perfor- mance ...Label Embedding to convert labels in text ...
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Generative Embedding for Model-Based Classification of fmri Data
... of classification algorithms in neuroimaging begin by embedding the measured recordings of each subject in a d-dimensional Euclidean space R d ...any learning algorithm that expects vectorial input, ...
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Embedding Methods for Fine Grained Entity Type Classification
... Feature learning We investigate whether hav- ing a large fine grained label space is helpful in learning a good representation for feature vec- tors (recall that W SABIE learns representations for both ...
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Recurrent Neural Network with Word Embedding for Complaint Classification
... Complaint classification aims at using information to deliver greater insights to enhance user experience after purchasing the products or ...word embedding technique is used to provide word features while ...
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GILE: A Generalized Input Label Embedding for Text Classification
... Compared with previous joint input-label models, the proposed model has a more general and flexible parametrization, which allows the output layer capacity to be controlled. Moreover, it is not restricted to linear ...
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Language classification from bilingual word embedding graphs
... — may be better predicted by contact than by genealogical relatedness. The validation of this hypothesis by means of bigger data sets will be the object of future work. References Rami Al-Rfou, Bryan Perozzi, and Steven ...
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Cost-Sensitive Label Embedding for Multi-Label Classification
... Label embedding (LE) is an important family of multi-label classifica- tion algorithms that digest the label information jointly for better ...label embedding with multidimensional scaling (CLEMS), ...
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Task2Vec: Task Embedding for Meta-Learning
... visual classification tasks which can be used to reason about the nature of those tasks and their re- ...an embedding based on estimates of the Fisher information matrix asso- ciated with the probe network ...
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DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning
... Furthermore the experiments support the idea that features extracted from several pre-trained word embedding models can be effectively com- bined to improve the overall classification per- formance . The ...
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Label Embedding using Hierarchical Structure of Labels for Twitter Classification
... DLR Augmented, which performed the best for this metric, uses augmented data. This is effec- tive in the learning process especially for classes that have small training data. Our method does not use augmented ...
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Feature Expansion using Word Embedding for Tweet Topic Classification
... C. Feature Expansion As introduced in the early section, we employ embedding words to address the problem of vocabulary mismatch. The idea is to identify the missing words in tweet representation if it can be ...
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Ontology alignment based on word embedding and random forest classification.
... {i.o.nkisi-orji , n.wiratunga, s.massie, k.hui}@rgu.ac.uk 2 British Geological Survey, Nottingham, UK [email protected] Abstract. Ontology alignment is crucial for integrating heterogeneous data sources and forms an ...
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Self Discriminative Learning for Unsupervised Document Embedding
... the embedding based on self-prediction of the surface of text, we explicitly exploit the inter-document infor- mation and directly model the relations of doc- uments in embedding space with a discrimi- ...
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Similarity Learning via Kernel Preserving Embedding
... neighbor classification (Wein- berger, Blitzer, and Saul 2005), image segmentation (Li et ...supervised learning (Kang et ...machine learning, pattern recognition, and data mining communities (Gao et ...
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