[PDF] Top 20 Dependency Based Embeddings for Sentence Classification Tasks
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Dependency Based Embeddings for Sentence Classification Tasks
... word embeddings in two word similarity datasets: WordSim-353 (Finkelstein et ...word embeddings for a pair of words to human judgements and report Spearman’s correlation in Ta- ble ...similarity ... See full document
11
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
... to sentence encoders in order to gain a bet- ter understanding of which properties of the in- put sentences their embeddings retain (see Sec- tion ...probing tasks. A probing task is a ... See full document
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Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases
... word embeddings and the interpretability of the applications using them, in this study, we propose a novel approach for evaluating the semantic relations in word embeddings using external knowledge bases: ... See full document
16
DisSent: Learning Sentence Representations from Explicit Discourse Relations
... curated sentence rela- tion datasets. We show that with dependency parsing and rule-based rubrics, we can curate a high quality sentence relation task by lever- aging explicit discourse ...of ... See full document
14
Dependency based Convolutional Neural Networks for Sentence Embedding
... Convolutional neural networks (CNNs), originally invented in computer vision (LeCun et al., 1995), has recently attracted much attention in natural language processing (NLP) on problems such as sequence labeling ... See full document
6
Modelling the Combination of Generic and Target Domain Embeddings in a Convolutional Neural Network for Sentence Classification
... domain embeddings in CNN for sentence ...word embeddings from a large number of target domain documents for individual ...two embeddings at the input layer and the fully connected layer of a ... See full document
5
Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification
... tion tasks, what they learn and encode into a single vector remains ...given sentence occurs in the input paragraph or not. We formulate a sentence content task to probe for this basic linguistic ... See full document
8
Fine Grained Sentence Functions for Short Text Conversation
... two sentence function classification tasks on the STC-SeFun dataset to: (1) determine the sentence functions of unlabeled queries and responses in a large cor- pus of short-text conversations, ... See full document
10
Towards Generalizable Sentence Embeddings
... word embeddings (Mikolov et ...its sentence analogue, Skipthought vec- tors, which are trained by predicting the surround- ing sentences when conditioned on the current ...various tasks: se- mantic ... See full document
10
MGNC CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
... multi-modal embeddings (Bruni et ...word embeddings to measure text similarity, however their focus was not on ...for sentence classification. This CNN-based architecture accepts ... See full document
6
Dependency Based Chinese Sentence Realization
... this classification, the only two possible linearizations of the sub-tree are <node 4, node 6, node 3, node 5> and <node 6, node 4, node 3, node ...selection. Dependency Relation Model: For a ... See full document
8
Searching for the X Factor: Exploring Corpus Subjectivity for Word Embeddings
... word embeddings, and input corpora may be dif- ferentially informative towards various NLP ...NLP tasks, such as senti- ment classification, revolve around subjective ex- pressions of likes or ... See full document
10
A Deeper Look into Dependency Based Word Embeddings
... rank based on functional similarity ef- fectively without the enhanced sentence feature representations explored by Komninos and Man- andhar ...the embeddings. Gen- erally, the worst-performing ... See full document
6
Towards Lossless Encoding of Sentences
... via morphemes. Socher et al. (2011a) use recur- sive autoencoders for paraphrase detection, learn- ing sentence embeddings (Socher et al., 2010) and syntactic parsing. Socher et al. (2011b) also use a ... See full document
7
Dependency Based Word Embeddings
... are represented as a very high dimensional but sparse vectors in which each entry is a measure of the association between the word and a particu- lar context (see (Turney and Pantel, 2010; Baroni and Lenci, 2010) for a ... See full document
7
Learning Sentence Embeddings with Auxiliary Tasks for Cross Domain Sentiment Classification
... auxiliary tasks are about whether an in- put sentence contains a positive or negative domain- independent sentiment ...input sentence that contains one of these words, regardless of the do- main the ... See full document
11
Empirical Linguistic Study of Sentence Embeddings
... NLP tasks and often provide state-of-the-art re- sults, it is extremely interesting and desirable to understand which properties of words, phrases or sentences are retained in their ...of classification ... See full document
11
N-best Rescoring for Parsing Based on Dependency-Based Word Embeddings
... Word embeddings have become increasingly popular lately, proving to be valuable as a source of features in a broad range of NLP tasks (Turian, Ratinov & Bengio, 2010; Socher et ...word embeddings ... See full document
16
CSE: Conceptual Sentence Embeddings based on Attention Model
... Most sentence embedding models typical- ly represent each sentence only using word surface, which makes these models indis- criminative for ubiquitous homonymy and ...of sentence, we employ ... See full document
11
Evaluating Embeddings using Syntax based Classification Tasks as a Proxy for Parser Performance
... of embeddings has a noticeable impact on parser ...uating embeddings directly in a parser is costly, we analyze the correlation between the full parsing task and a simple linear classification task ... See full document
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