[PDF] Top 20 Singleton Detection using Word Embeddings and Neural Networks
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Singleton Detection using Word Embeddings and Neural Networks
... 50-dimensional embeddings, 150 hid- den nodes, 5 context words (both sides), 2 con- text mentions (both sides), and a ...values using pair-wise approximate ran- domization tests (Yeh, ... See full document
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Multilingual segmentation based on neural networks and pre trained word embeddings
... The DISPRT 2019 workshop has organized a shared task aiming to identify cross-formalism and multilingual discourse segments. Elemen- tary Discourse Units (EDUs) are quite sim- ilar across different theories. Segmentation ... See full document
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Fine grained Opinion Mining with Recurrent Neural Networks and Word Embeddings
... However, bi-directionality and LSTM do not pro- vide clear gains over the simple Elman RNN. In fact, bi-directionality hurts the performance in most cases. This finding contrasts the find- ing of Irsoy and Cardie (2014) ... See full document
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Convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings
... tokenization. Using subword tokens to capture rare or unknown words has been demonstrated to be useful in machine translation (Sennrich et ...sults using only original word ...of using ... See full document
8
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
... the word vector space. For example, based on the pre-calculated word vec- tors, we have found other words, such as rebound, decline, tumble, slowdown, climb, which are very close to at least one of the ... See full document
6
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings
... deep neural network classifier approach for the task of automatic noun compound interpretation for ...pre-trained word embeddings as word representations, rather than using large, ... See full document
11
Character Aware Neural Networks for Arabic Named Entity Recognition for Social Media
... our word vectors with pretrained word embeddings (Zahran et ...of word embeddings for a given sentence S. Hence, every word in a sentence gets its left and right representation ... See full document
10
Clinical Abbreviation Disambiguation Using Neural Word Embeddings
... training word embeddings from large unlabeled corpora using deep neural ...networks. Word embedding is typically repre- sented as a dense real-valued low dimensional matrix M of ... See full document
6
Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation
... Many neural network models have been explored for ...volutional neural networks (CNNs) have shown its great effectiveness in computer vision tasks (Krizhevsky et ... See full document
10
Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection
... deep neural architecture fed with good word embeddings can return promising results in metaphor ...each word in the data can improve its performance all the while reducing its depth, ... See full document
11
NSEmo at EmoInt 2017: An Ensemble to Predict Emotion Intensity in Tweets
... The results of ensemble combination of SVR using linear kernel, neural networks, baseline method with 400 dimensional word embeddings are presented in Table 9. We have achieved the ... See full document
6
TüKaSt at SemEval 2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification
... on neural networks using dense vector representations (word embeddings) as ...Since embeddings are able to capture syntactic and semantic features and represent them in a ... See full document
7
Proceedings of the BioNLP 2018 workshop
... Manirupa Das, Eric Fosler-Lussier, Simon Lin, Soheil Moosavinasab, David Chen, Steve Rust, Yungui Huang and Rajiv Ramnath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... See full document
12
Attention Modeling for Targeted Sentiment
... input using a bidi- rectional gated recurrent neural network, and then use three-way gated neural network structure to model the interaction between the target and its left and right ...ing ... See full document
6
Token Level Metaphor Detection using Neural Networks
... metaphor detection, combining pre-trained word embeddings with a neural network ...basic word embeddings, which can be created in an unsupervised ...of using ... See full document
6
CogALex V Shared Task: GHHH Detecting Semantic Relations via Word Embeddings
... in neural networks and word embeddings, there are attempts to find semantic relations automatically from texts in an arithmetic fashion by measuring the distance between words in the vector ... See full document
6
Spam detection in im images using convolutional neural networks
... we check our WhatsApp, we are usually bombarded with many messages. Some of them are from our loved ones, while others are of professional importance. But unfortunately, among these messages are many unwanted messages, ... See full document
6
Neural Networks for Multi Word Expression Detection
... automatic detection of multiword expression researchers use language-independent approaches that combine association measures like mutual information or dice coefficient with machine learning approaches (Tsvetkov ... See full document
6
Captioning for Motion Detection for video surveillance Applications using Deep Learning
... model neural network which automatically learns to features from the images and generate the ...trained using COCO dataset where the dataset is split into test, train and validation ...convolutional ... See full document
6
Team Bertha von Suttner at SemEval 2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network
... level embeddings as the average of ELMo (Peters et al., 2018) word embeddings for each sentence, and represent the document as a sequence of such sentence embed- ...convolutional Neural ... See full document
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