[PDF] Top 20 Sentence State LSTM for Text Representation
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Sentence State LSTM for Text Representation
... Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer var- ious limitations due to their sequential na- ture. We investigate an alternative ... See full document
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Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks
... from text, revealing key ideas such as ”who did what to whom, when, how, and where?”, and is considered to be one of the most complex tasks in natural language ...a sentence be captured in a general, ... See full document
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Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text
... the sentence “ She was maintained on [an epidural]- treatment and [pca]treatment for [pain control]pro- blem ” , the relative distances of “ She ” to “ [an epidural]treatment ” and “ [pain control]problem ” are − ... See full document
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A Model of Coherence Based on Distributed Sentence Representation
... multi-sentence text meaningful, both logically and syn- ...cross- sentence syntax (such as coreference and tense rules) are very hard to ...semantic representation for sentences au- ... See full document
10
Representation Learning for Answer Selection with LSTM Based Importance Weighting
... for representation learning in non-factoid answer selection are usually based on BiLSTMs (Tan et ...a representation for an input text we apply an LSTM on the concatenated d-dimensional word ... See full document
10
Sentence Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks
... several state-of-the-art baseline methods described ...and LSTM: The CNN-based detection model (Wang, 2017) and LSTM-based RNN model for representation learn- ing from word sequences (Rashkin ... See full document
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Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM based Architecture
... architecture LSTM- based models to recover different kinds of temporal relations from ...cross- sentence, and document creation time rela- ...outperforms state-of- the-art methods by a large ...post ... See full document
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Meta-Embedding Sentence Representation for Textual Similarity
... of sentence embedding represen- tation, also called bottom-up approach, represents sentences by a weighted sum of the embedding vectors of their individual ...of sentence em- beddings leads to better ... See full document
9
Sentence Classification for Investment Rules Detection
... Kim (Kim, 2014), showing how a simple model together with pre-trained word representations can be highly performing. But the use of word- embeddings has been challenged for CNNs, (John- son and Zhang, 2014, 2015) propose ... See full document
5
Hierarchical Structured Model for Fine to Coarse Manifesto Text Analysis
... the sentence- and document- level tasks ...using text only, we leverage context information, such as coalition and temporal dependencies to cali- brate the position further using ...the ... See full document
11
Arabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features
... Dialect identification is the task of identifying the dialect of a particular segment of speech or text of any size (i.e., word, sentence, or document) auto- matically. The task of Arabic Dialect ... See full document
5
Leveraging Sentence level Information with Encoder LSTM for Semantic Slot Filling
... labeler LSTM(W) and the proposed encoder-labeler LSTM(W) according to the exper- imental procedure explained in Section ...labeler LSTM(W) achieved the F 1 -score of ... See full document
7
Multimodal Differential Network for Visual Question Generation
... and state-of-the-art methods is provided in table 2 for VQA ...current state- of-the-art methods on that dataset and the second contains the ...previous state-of-the-art (Yang et ... See full document
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GTR LSTM: A Triple Encoder for Sentence Generation from RDF Data
... Our GTR-LSTM triple encoder overcomes the difficulty as follows. It receives a directed graph G = hV, Ei as the input, where V is a set of vertices that represent entities or literals, and E is a set of directed ... See full document
11
Learning Domain Representation for Multi Domain Sentiment Classification
... general sentence representation is then mapped into a domain-specific representation by attention over the input sentence using explic- itly learned domain descriptors, so that the most ... See full document
10
Hierarchical CVAE for Fine Grained Hate Speech Classification
... each hate ideology, we select the top three han- dles in terms of the number of followers. Due to ties, there are four different groups in several categories of our dataset. The dataset consists of all the content ... See full document
10
Sentence Fusion for Multidocument News Summarization
... Finally, subtrees which are not part of the intersection are pruned off the basis tree. However, removing all such subtrees may result in an ungrammatical or seman- tically flawed sentence; for example, we might ... See full document
32
A Formal Model for Information Selection in Multi Sentence Text Extraction
... of text similarity between the three text units; a clustering algorithm may form three singleton clusters, and MMR may determine that each textual unit is sufficiently dif- ferent from each other, ... See full document
7
Automatic Text Summarization For Bengali Language Including Grammatical Analysis
... Text summarizer generates summary of a text following some mathematical theory and graph. Our proposed method also follows those including grammatical rules. The method consists of three different steps. ... See full document
5
Hierarchical Attention Networks for Document Classification
... Figure 5 shows that our model can select the words carrying strong sentiment like delicious, amazing, terrible and their corresponding sentences. Sentences containing many words like cocktails, pasta, entree are disre- ... See full document
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