[PDF] Top 20 Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
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Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
... learning word representations (Mikolov et ...include syntactic contexts (Levy and Goldberg, 2014) derived from depen- dency parse of ...Syntax-based embeddings encode functional similarity (in-place ... See full document
11
Incorporating Semantic Word Representations into Query Expansion for Microblog Information Retrieval
... directly using these methods in microblog retriev- ...by using global dictionary based que- ry expansion methods such as statistical dictionar- ies and semantic ...framework using microblog ... See full document
11
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
... used Graph Convolu- tional Networks (GCNs) to encode syntactic struc- ...noisy) syntactic dependency graphs by Marcheggiani and Titov ...linguistically-aware word represen- tations are ... See full document
7
CogALex V Shared Task: GHHH Detecting Semantic Relations via Word Embeddings
... Finding semantic relatedness between words is of crucial importance for natural language processing as it is essential for tasks like query expansion in information ...constructed semantic ... See full document
6
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
... tactic information in neural SRL models include: FitzGerald et ...of syntactic paths between arguments and predicates; Lei et ...(non- graph) convolutional networks and provided syn- ... See full document
10
Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases
... as word embeddings have been shown effective in capturing fine-grained semantic relations and syntactic regularities in large text corpora ...ple, convolutional neural networks ... See full document
16
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures
... both syntactic and se- mantic information in the modeling ...the syntactic and seman- tic information are only employed separately in the different deep learning architectures to gener- ate ... See full document
7
Efficient Sentence Embedding using Discrete Cosine Transform
... tional networks potentially yield superior clas- sification performance, the improvements in classification accuracy are typically mediocre compared to the simple vector ...compress word sequences in an ... See full document
7
GCN Sem at SemEval 2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks
... Parsing using Graph Convolutional and Recurrent Neural ...performs semantic parsing using information derived from syntactic dependencies between words in each ...model ... See full document
5
Incorporating Subword Information into Matrix Factorization Word Embeddings
... are semantic, the neighbors are se- mantically related, else if syntactic they have sim- ilar syntactic ...is semantic or syntactic in nature and weigh them ...supervision using ... See full document
6
Singleton Detection using Word Embeddings and Neural Networks
... all embeddings is used to represent un- known words, but more advanced approaches are possible, ...by using part-of-speech ...detection information could yield larger gains with these systems, as ... See full document
7
Parsing with Compositional Vector Grammars
... The standard RNN requires a single composi- tion function to capture all types of compositions: adjectives and nouns, verbs and nouns, adverbs and adjectives, etc. Even though this function is a powerful one, we find a ... See full document
11
Lung Semantic Segmentation using Convolutional Neural Networks
... segmentation convolutional neural networks are applied on NIH dataset, an open dataset where research can be carried ...accuracies using neural networks with weakly supervised learning ... See full document
6
Twitter Homophily: Network Based Prediction of User’s Occupation
... network information. People’s personal networks are homogeneous, ...network information has been utilized in friend recommen- dation (Guy et ... See full document
6
Adjusting Word Embeddings with Semantic Intensity Orders
... better semantic representations, different approaches using semantic lexicons as well as lex- ical knowledge to adjust word vectors have re- cently been ...each word vector to be in the ... See full document
8
Team Bertha von Suttner at SemEval 2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network
... use word level representations as the in- put for our ...sentence embeddings which are calculated as the average of the word embeddings of a ...done using any pre-trained word ... See full document
5
Performing Integrated Syntactic and Semantic Parsing Using Classification
... Performing Integrated Syntactic and Semantic Parsing Using Classification Performing Integrated Syntactic and Semantic Parsing Using Classification Robert T Kasper and Eduard H H o v y Information Sci[.] ... See full document
6
Exploring Semantic Representation in Brain Activity Using Word Embeddings
... the word embeddings esti- mated using the SWE model and the whole-brain fMRI data are shown in Figure ...skip-gram word embeddings and the behavioural data of the 60 nouns was ...derive ... See full document
11
The Role of Semantic Roles in Disambiguating Verb Senses
... as semantic class informa- tion, and attempts to model richer linguistic infor- mation about predicate ...the syntactic features are able to identify subjects and objects of only simple ... See full document
8
Derivational Morphological Relations in Word Embeddings
... The data naturally contains classes with signif- icant differences in size. To prevent the small classes from being underrepresented, we also eval- uated the clustering on a dataset, where the same number of derivation ... See full document
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