[PDF] Top 20 Deep Neural Models of Semantic Shift
Has 10000 "Deep Neural Models of Semantic Shift" found on our website. Below are the top 20 most common "Deep Neural Models of Semantic Shift".
Deep Neural Models of Semantic Shift
... In this paper, we have built the first diachronic distributional model that represents time as a con- tinuous variable instead of employing data bin- ning. There are several advantages to treating time as continuous. The ... See full document
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Deep Neural Models for Medical Concept Normalization in User Generated Texts
... The most attractive feature of the biomedical domain is that domain knowledge is prevailing in this domain for dozens of languages. In particular, UMLS is undoubtedly the largest lexico-semantic resource for ... See full document
7
Modeling Interestingness with Deep Neural Networks
... exploiting deep architectures, deep learning techniques are able to automatically discover from training data the hidden structures and the associ- ated features at different levels of abstraction use- ful ... See full document
12
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
... the models trained with external knowledge bases, such as Cause-Effect (Liu et ...Unsupervised Semantic Similarity Method (USSM) (Liu et ...closer. Neural Knowl- edge Activated Method (NKAM) (Liu et ... See full document
10
Shift Reduce CCG Parsing using Neural Network Models
... a neural network based shift-reduce CCG parser, the first neural network based parser for ...(2014)’s shift-reduce de- pendency parser for CCG ...structured neural network model based ... See full document
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
... Semantic word spaces have been very use- ful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection ... See full document
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Two Discourse Driven Language Models for Semantics
... quires deep semantic ...of semantic knowledge can be modeled as a language model if done at an appropriate level of ab- ...capture semantic frame chains and discourse information while ... See full document
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Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement
... The neural network models in the table, paragraph vec- tor (PV) (Le and Mikolov, 2014), CNN (Yu et ...have neural network approaches (Yu et ... See full document
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Assessing the Corpus Size vs Similarity Trade off for Word Embeddings in Clinical NLP
... of deep learning methods in NLP has resulted in a significant num- ber of uses of embeddings to represent ...and deep learning models: these models excel with low-dimensional, continuous ... See full document
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Semantic Language models with deep neural Networks
... Feed-forward NNLMs are based on fixed histories, therefore they also suffer from the problems related to fixed histories. Recurrent NNLMs (RNNLMs) [96, 93] overcome this problem by using recurrent connections, which ... See full document
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Neural Shift Reduce CCG Semantic Parsing
... of deep neural architectures for decision making in linear- time dependency parsing (Chen and Manning, 2014; Dyer et ...contrast, semantic parsing often relies on algorithms with polynomial number of ... See full document
12
Deep Neural Network Language Models
... language models make generalization a chal- ...the neural network language model (NNLM) (Bengio et ...layer neural networks (feed-forward or ...language models (Sarikaya et ... See full document
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Proceedings of the Human Informed Translation and Interpreting Technology Workshop (HiT IT 2019)
... Natural Language Processing and Machine Translation (MT) make use of the knowledge and expertise of professional translators and interpreters in order to build and improve models for automatic translation or for ... See full document
10
Deep Learning: A Vision for Computer
... Hinton et al. [17] introduced the notion of DL which is based on the deep belief network (DBN). Hinton proposed an unsupervised greedy layer-by-layer training algorithm to cope with the optimization concern of ... See full document
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Neural Mechanisms for Combinatorial Semantics in Language and Vision: Evidence From FMRI, Patients, and Brain Stimulation
... the semantic integration of words ...Integrating semantic information is an integral aspect of both word learning and word reading, and thus it may be that improvements in the processes of semantic ... See full document
147
Deep neural network models for image classification and regression
... Concerning the 1D-CNN, it may be interesting to explore other kinds of deep neural networks such as SAEs and train them with evolutionary methods, given the limitation of the number of training samples. ... See full document
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Deep Learning: Approaches and Challenges
... various deep learning architectures, and then bierfly describe their chal- lenges such as training data ...of deep learning optimization using traditional machine learning models are described such ... See full document
8
Deep Unsupervised Feature Learning for Natural Language Processing
... Input language representation: Neural models rely on vector representations of their input (as opposed to discrete representations as in, for instance, HMMs). In NLP, sentences are therefore encoded as ... See full document
6
Unified Framework For Deep Learning Based Text Classification
... studies, deep learning is penetrating to text classification as ...for deep learning vary on different parameters such as learning model, data sets used, tuning of hyper-parameters, types of features ...and ... See full document
5
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis
... ductive models (using a fine-tuned LM on each test ...transductive models consistently outperformed the baselines, which suggests that transductive LM fine-tuning improves performance of neural mod- ... See full document
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