Guiding Approximate Text Classification Rules via Context Information

2018 ◽  
pp. 133-139
Author(s):  
Wai Chung Wong ◽  
Sunny Lai ◽  
Wai Lam ◽  
Kwong Sak Leung
Keyword(s):  
Text Classification ◽  
Context Information ◽  
Classification Rules

scholarly journals Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yifei Chen ◽  
Yuxing Sun ◽  
Bing-Qing Han
Keyword(s):  
Feature Selection ◽  
Protein Interaction ◽  
Text Classification ◽  
Protein Interactions ◽  
Reduction Rate ◽  
Importance Measure ◽  
Context Information ◽  
Selection Methods ◽  
Term Frequency ◽  
Context Similarity

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of theF1measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

scholarly journals Solving Sequential Text Classification as Board-Game Playing

2020 ◽  
Vol 34 (05) ◽  
pp. 8640-8648 ◽  
Author(s):  
Chen Qian ◽  
Fuli Feng ◽  
Lijie Wen ◽  
Zhenpeng Chen ◽  
Li Lin ◽  
...  
Keyword(s):  
Text Classification ◽  
Statistical Significance ◽  
Context Information ◽  
Game Playing ◽  
Board Game ◽  
Reading Order ◽  
Game State ◽  
Right Order ◽  
Novel Model ◽  
Label Sequence

Sequential Text Classification (STC) aims to classify a sequence of text fragments (e.g., words in a sentence or sentences in a document) into a sequence of labels. In addition to the intra-fragment text contents, considering the inter-fragment context dependencies is also important for STC. Previous sequence labeling approaches largely generate a sequence of labels in left-to-right reading order. However, the need for context information in making decisions varies across different fragments and is not strictly organized in a left-to-right order. Therefore, it is appealing to label the fragments that need less consideration of context information first before labeling the fragments that need more. In this paper, we propose a novel model that labels a sequence of fragments in jumping order. Specifically, we devise a dedicated board-game to develop a correspondence between solving STC and board-game playing. By defining proper game rules and devising a game state evaluator in which context clues are injected, at each round, each player is effectively pushed to find the optimal move without position restrictions via considering the current game state, which corresponds to producing a label for an unlabeled fragment jumpily with the consideration of the contexts clues. The final game-end state is viewed as the optimal label sequence. Extensive results on three representative datasets show that the proposed approach outperforms the state-of-the-art methods with statistical significance.

scholarly journals EVOLVING TEXT CLASSIFICATION RULES WITH GENETIC PROGRAMMING

2005 ◽  
Vol 19 (7) ◽  
pp. 659-676 ◽  
Author(s):  
Laurence Hirsch ◽  
Masoud Saeedi ◽  
Robin Hirsch
Keyword(s):  
Genetic Programming ◽  
Text Classification ◽  
Classification Rules

scholarly journals Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
DanFeng Yan ◽  
Shiyao Guo
Keyword(s):  
Neural Network ◽  
Text Classification ◽  
Feature Representation ◽  
Context Information ◽  
Attention Model ◽  
Learning Framework ◽  
Word Level ◽  
Sentence Level ◽  
Memory Network ◽  
Short Time

We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network and extracting some additional features from text by traditional methods as the part of representation. We propose two kinds of classification algorithms: one is based on convolutional neural network fusing context information and the other is based on bidirectional long and short time memory network. We integrate the context information into the final feature representation by designing attention structures at sentence level and word level, which increases the diversity of feature information. Our experimental results on two datasets validate the advantages of the two models in terms of time efficiency and accuracy compared to the different models with fundamental AM architectures.

scholarly journals Adaptive Region Embedding for Text Classification

2019 ◽  
Vol 33 ◽  
pp. 7314-7321
Author(s):  
Liuyu Xiang ◽  
Xiaoming Jin ◽  
Lan Yi ◽  
Guiguang Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Context Information ◽  
Great Success ◽  
Recurrent Networks ◽  
Learning Models ◽  
Benchmark Datasets

Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification. In spite of their great success, most deep learning models neglect the importance of modeling context information, which is crucial to understanding texts. In this work, we propose the Adaptive Region Embedding to learn context representation to improve text classification. Specifically, a metanetwork is learned to generate a context matrix for each region, and each word interacts with its corresponding context matrix to produce the regional representation for further classification. Compared to previous models that are designed to capture context information, our model contains less parameters and is more flexible. We extensively evaluate our method on 8 benchmark datasets for text classification. The experimental results prove that our method achieves state-of-the-art performances and effectively avoids word ambiguity.

Automatic Stereotype Activation Is Context Dependent

2010 ◽  
Vol 41 (3) ◽  
pp. 131-136 ◽  
Author(s):  
Catharina Casper ◽  
Klaus Rothermund ◽  
Dirk Wentura
Keyword(s):  
Lexical Decision ◽  
Context Information ◽  
Decision Task ◽  
Social Categories ◽  
Priming Effects ◽  
Stereotype Activation ◽  
Social Stereotypes ◽  
Priming Paradigm ◽  
Automatic Activation ◽  
Mental Schemas

Processes involving an automatic activation of stereotypes in different contexts were investigated using a priming paradigm with the lexical decision task. The names of social categories were combined with background pictures of specific situations to yield a compound prime comprising category and context information. Significant category priming effects for stereotypic attributes (e.g., Bavarians – beer) emerged for fitting contexts (e.g., in combination with a picture of a marquee) but not for nonfitting contexts (e.g., in combination with a picture of a shop). Findings indicate that social stereotypes are organized as specific mental schemas that are triggered by a combination of category and context information.

Impact of Context Information on Metaphor Elaboration

2018 ◽  
Vol 65 (6) ◽  
pp. 370-384 ◽  
Author(s):  
Veronika Lerche ◽  
Ursula Christmann ◽  
Andreas Voss
Keyword(s):  
Diffusion Model ◽  
Processing Speed ◽  
A Priori ◽  
Model Fit ◽  
Verbal Task ◽  
Context Information ◽  
Speed Of Processing ◽  
Good Account ◽  
Dependent Variables ◽  
Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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