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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
"... Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of compo ..."
Abstract
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Cited by 191 (7 self)
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of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained
Supplementary Material: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
"... This supplementary material contains four sections. The first section details the design and methods we used to create the Sentiment Treebank. The sec-ond provides an analysis of how much this tree-bank helps performance on recursive models. The third section gives several examples of the data we pe ..."
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This supplementary material contains four sections. The first section details the design and methods we used to create the Sentiment Treebank. The sec-ond provides an analysis of how much this tree-bank helps performance on recursive models. The third section gives several examples of the data we
Subsentential Sentiment on a Shoestring: A Crosslingual Analysis of Compositional Classification
"... Sentiment analysis has undergone a shift from document-level analysis, where labels ex-presses the sentiment of a whole document or whole sentence, to subsentential approaches, which assess the contribution of individual phrases, in particular including the composi-tion of sentiment terms and phrase ..."
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and phrases such as negators and intensifiers. Starting from a small sentiment treebank mod-eled after the Stanford Sentiment Treebank of Socher et al. (2013), we investigate suitable methods to perform compositional sentiment classification for German in a data-scarce set-ting, harnessing cross
Neural Networks for Integrating Compositional and Non-compositional Sentiment in Sentiment Composition
"... This paper proposes neural networks for inte-grating compositional and non-compositional sentiment in the process of sentiment compo-sition, a type of semantic composition that op-timizes a sentiment objective. We enable in-dividual composition operations in a recursive process to possess the capabi ..."
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This paper proposes neural networks for inte-grating compositional and non-compositional sentiment in the process of sentiment compo-sition, a type of semantic composition that op-timizes a sentiment objective. We enable in-dividual composition operations in a recursive process to possess
RoseMerry: A Baseline Message-level Sentiment Classification System
"... In this paper, we propose a baseline message-level sentiment classification method, as de-veloped for SemEval-2015 Task 10, Subtask B. This system leverages both hand-crafted features and message-level embedding fea-tures, and uses an SVM classifier for message-level sentiment classification. In pre ..."
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. In pre-training the embedding features, we use one million randomly-selected tweets. We present re-sults over SemEval-2015 Task 10, Subtask B, as well as the Stanford Sentiment Treebank. Our experiments show the effectiveness of our method over both datasets. 1
The French Social Media Bank: a Treebank of Noisy User Generated Content
"... In recent years, statistical parsers have reached high performance levels on well-edited texts. Domain adaptation techniques have improved parsing results on text genres differing from the journalistic data most parsers are trained on. However, such corpora usually comply with standard linguistic, s ..."
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Cited by 3 (0 self)
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, spelling and typographic conventions. In the meantime, the emergence of Web 2.0 communication media has caused the apparition of new types of online textual data. Although valuable, e.g., in terms of data mining and sentiment analysis, such user-generated content rarely complies with standard conventions
Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis
- In AAAI 2014
, 2014
"... Recursive neural models have achieved promising re-sults in many natural language processing tasks. The main difference among these models lies in the com-position function, i.e., how to obtain the vector repre-sentation for a phrase or sentence using the representa-tions of words it contains. This ..."
Abstract
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Cited by 6 (1 self)
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over these composition functions. The composition functions and parameters used for adap-tive selection are learned jointly from data. We inte-grate AdaMC into existing recursive neural models and conduct extensive experiments on the Stanford Senti-ment Treebank. The results illustrate that AdaMC sig
Computational Linguistics (2012)" The French Social Media Bank: a Treebank of Noisy User Generated Content
, 2013
"... In recent years, statistical parsers have reached high performance levels on well-edited texts. Domain adaptation techniques have improved parsing results on text genres differing from the journalistic data most parsers are trained on. However, such corpora usually comply with standard linguistic, s ..."
Abstract
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, spelling and typographic conventions. In the meantime, the emergence of Web 2.0 communication media has caused the apparition of new types of online textual data. Although valuable, e.g., in terms of data mining and sentiment analysis, such user-generated content rarely complies with standard conventions
