[PDF] Top 20 Joint Embedding of Words and Labels for Text Classification
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Joint Embedding of Words and Labels for Text Classification
... Testing accuracy Simple compositional meth- ods indeed achieve comparable performance as the sophisticated deep CNN/RNN models. On the other hand, deep hierarchical attention model can improve the pure CNN/RNN models. ... See full document
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Adaptive Region Embedding for Text Classification
... a joint embedding of words and la- ...region embedding by the projecting the word embedding of each region onto the context ...alized Text Filtering perspective provides a ... See full document
8
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings
... derive labels for the remainder of the cor- ...class labels for each unlabeled document and then training a clas- sifier on the complete corpus based on those anno- ...derive labels without any ... See full document
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Embedding Words and Senses Together via Joint Knowledge Enhanced Training
... Previous works have addressed this limita- tion by automatically inducing word senses from monolingual corpora (Sch¨utze, 1998; Reisinger and Mooney, 2010; Huang et al., 2012; Di Marco and Navigli, 2013; Neelakantan et ... See full document
12
Multi Task Label Embedding for Text Classification
... in text classification lever- ages implicit correlations among related tasks to extract common features and yield perfor- mance ...treats labels of each task as in- dependent and meaningless one-hot ... See full document
9
Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification
... Entity embedding method is based on the analysis of distributional semantics, ...context words given target ...context words so that the embeddings encode meaningful ...hierarchy embedding (Hu ... See full document
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Distributional Representations of Words for Short Text Classification
... short text classification (STC) through word embeddings – distribu- tional representations of words learned from large unlabeled ...Wikipedia text. We assume that a short text documen- ... See full document
6
Online Embedding Compression for Text Classification Using Low Rank Matrix Factorization
... word embedding matrix often accounts for most of the network ...The embedding ma- trix is typically initialized with pretrained word embeddings like Word2Vec, (Mikolov et ...tagging, classification ... See full document
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Short Text Classification Based on Latent Topic Modeling and Word Embedding
... short text every day, ranging from twitters, movie comments, search snippets to news ...sparse text accurately is always the basic need for us to deal with information ...the words, on which we ... See full document
7
Available online: https://edupediapublications.org/journals/index.php/IJR/ P a g e | 1919 Mapping Bug Reports To Relevant Files:A Ranking Model, A FineGrained Benchmark,And Feature Evaluation
... Existing embedding learning approaches ar totally on the premise of spatial arrangement hypothesis [9], that states that the representations of words ar mirrored by their ...result, words with ... See full document
7
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
... We also use several string similarity features used in past work on NED (Meij et al., 2012). These features aim to capture the similarity be- tween the title of entity e and the surface of men- tion m, and consist of the ... See full document
10
Label Embedding using Hierarchical Structure of Labels for Twitter Classification
... Twitter is used for various applications such as disaster monitoring (Ashktorab et al., 2014; Mizuno et al., 2016) and news material gathering (Vosecky et al., 2013; Hayashi et al., 2015). In these applications, each ... See full document
6
Representing Text for Joint Embedding of Text and Knowledge Bases
... and text graph (Lao et ...and text information by embedding knowledge base entities and the words in their names in the same vector space, but did not model the textual co- occurrences of ... See full document
11
Rationale Augmented Convolutional Neural Networks for Text Classification
... for text classification that jointly exploits labels on documents and their constituent ...document labels and associated rationales demonstrate that our ap- proach consistently outperforms ... See full document
10
Using word embedding for bio event extraction
... word embedding only while uti- lizing the exactly same extraction infrastructure, which is a pipeline converging tokenization, pars- ing and other pre-processing upon Apache ...word embedding with n- gram. ... See full document
6
Jointly Embedding Entities and Text with Distant Supervision
... and text from an unnanotated corpus, using only a list of mappings between entities and sur- face ...human-annotated text or large knowledge graph ...and words capture complementary infor- mation ... See full document
12
Influence of functional words, term weighting measures and classifiers on Text classification
... From the results, it can be observed that the SVM classifier is the best performer when compared with Naive Bayes classifier and K-nearest neighbor classifier in both the cases such as before preprocessing and after ... See full document
12
Approach for Dimensionality Reduction in Web Page Classification
... Hybrid approach of Rough set and Genetic Algorithm for web page classification is proposed by Xiaoyue Wang, Zhen Hua and Rujiang Bai (2012). Rough set is used for dimensionality reduction. GA is based on an ... See full document
6
“ye word kis lang ka hai bhai?” Testing the Limits of Word level Language Identification
... An important area to work on is the code- mixing of transliterated text where standard tech- niques do not work and lexicons are not avail- able. Our results also show that linguistically close or related ... See full document
10
Feature Based Opinion Analysis of Chinese Mobile Reviews
... analysis words and additionally the negative analysis words and their level to that they might be classified, in order that the degree of accuracy can be ... See full document
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