[PDF] Top 20 Learning Word Importance with the Neural Bag of Words Model
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Learning Word Importance with the Neural Bag of Words Model
... of neural network architectures have been proposed for different language processing ...volutional Neural Networks (CNN) (Kim, 2014; Johnson and Zhang, 2015; Wang et ...Recurrent/Recursive Neural ... See full document
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Sentiment embedding with feature selection and Emotion Detection in sentiment Analysis.
... learn word embeddings over “term-term” cooccurrence ...profound learning and neural system [30], [31], [32], a surge of studies learn word embeddings with neural ...a neural ... See full document
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Bag of Words as Target for Neural Machine Translation
... Due to the limitation of the training set, most of the existing neural machine translation models only have one reference sentences as the targets. However, a sentence can be translated into more than one correct ... See full document
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Bag of Words Transfer: Non Contextual Techniques for Multi Task Learning
... auxiliary word prediction objective terms may help regularize the representations used for prediction (Dai and Le, 2015; Kiros et ...language model instead of an unconditional language model, ... See full document
7
CARER: Contextualized Affect Representations for Emotion Recognition
... this model differs in that the set of patterns used is signifi- cantly smaller as compared to the original size of the enriched ...of words (BoW), character-level (char), n-grams, and TF-IDF) which are ... See full document
11
Stance Detection in Fake News A Combined Feature Representation
... as word or character n-grams overlapping score, bag-of- words (BOW), word embeddings, and latent se- mantic analysis features (Riedel et ...deep learning architectures to validate fake ... See full document
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Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology
... retrieval model to manage, search, and retrieve unstructured and complex image ...Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the ... See full document
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Detecting Online Spams through Supervised Learning Techniques
... use words as they are in writings. Nonetheless, words may have various implications, and various words may have a similar ...correct importance of a word can be controlled by its ... See full document
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Towards Understanding Neural Machine Translation with Word Importance
... timated word importance to detect the under- translated words by NMT ...input words should contribute lit- tle to the NMT outputs, yielding much smaller word ...Transformer model ... See full document
10
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
... scientists model text to understand politi- cal processes (Grimmer 2010; Roberts et ...a learning procedure similar to Structured Gov2Vec to streaming documents to learn represen- tations of documents that ... See full document
6
Learning to Understand Phrases by Embedding the Dictionary
... tic word representations are a success story of recent NLP ...mantics. Neural language embedding mod- els can be effectively trained to map dictio- nary definitions (phrases) to (lexical) repre- sentations ... See full document
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SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
... called Neural tensor skip-gram model (NTSG-1, NTSG-2, NTSG-3, NTSG-4), that learns multi- prototype word embeddings and uses a tensor layer to model the interaction of words and top- ... See full document
11
Neural Attentive Bag of Entities Model for Text Classification
... (or bag of entities) relevant to the document (Gabrilovich and Markovitch, 2006, 2007; Xiong et ...of words is that unlike words, entities pro- vide unambiguous semantic signals because they are ... See full document
11
Connecting Social Media to E-Commerce Site Using Cold Start Product Recommendation
... individual words are exchangeable, which is essentially the same as the bag-of-words model ...assumption. Word representations or embeddings learned using neural language models ... See full document
8
Identification of Duplication in Questions Posed on Knowledge Sharing Platform Quora using Machine Learning Techniques
... After the completion of pre processing of data, the feature extraction step is done in 2 ways-one using Count-Vectorizer and the other using TFIDF-Vectorizer, both of them being Bag of Words models. After ... See full document
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Not All Contexts Are Created Equal: Better Word Representations with Variable Attention
... the bag-of- words model for learning words represen- tations that take into account both syn- tactic and semantic properties within lan- ...tention model that finds within the ... See full document
6
Learning Neural Bag-of-Matrix-Summarization with Riemannian Network
... traditional neural lay- ...shallow learning schemes have led to suboptimal solutions on the specific nonlin- ear manifolds, which also often require significantly more time to conduct online predictions ... See full document
8
Let Sense Bags Do Talking: Cross Lingual Word Semantic Similarity for English and Hindi
... of words between two languages, where one language is treated as resourceful and other is resource ...two words are se- mantically similar if their senses are sim- ilar to each ... See full document
5
Target Foresight Based Attention for Neural Machine Translation
... variants. Model Complexity The proposed models introduce a few parameters to the NMT base- line system Nematus, which has 105M pa- ...complex model reduces speed by around 30%, which is the cost of the ... See full document
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Word Arrangement Model With Opinion Words And Reviews
... A graph-based co-ranking criterion is utilized to calculate the arrogance of each applicant. The applicants with greater assurance are produced as viewpoint objectives or viewpoint terms. In comparison to previous ... See full document
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