Semantic text classification: A survey of past and recent advances

Altınel, Berna; Ganiz, Murat Can

doi:10.1016/j.ipm.2018.08.001

Cited by 152 publications

(60 citation statements)

References 35 publications

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“…Natural language is a common form of categorical data. On the subject of natural language processing, we also have the survey, "Semantic text classification: A survey of past and recent advances" [15] by Altinel and Ganiz. Part of this work covers embeddings for natural language data.…”

Section: Related Workmentioning

confidence: 99%

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Survey on categorical data for neural networks

2020

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This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known. However, researchers continue to discover new variations or entirely new methods for working with categorical data in neural networks. Our primary contribution is to cover these representation techniques in a single work. Practitioners working with big data often have a need to encode categorical values in their datasets in order to leverage machine learning algorithms. Moreover, the size of data sets we consider as big data may cause one to reject some encoding techniques as impractical, due to their running time complexity. Neural networks take vectors of real numbers as inputs. One must use a technique to map qualitative values to numerical values before using them as input to a neural network. These techniques are known as embeddings, encodings, representations, or distributed representations. Another contribution this work makes is to provide references for the source code of various techniques, where we are able to verify the authenticity of the source code. We cover recent research in several domains where researchers use categorical data in neural networks. Some of these domains are natural language processing, fraud detection, and clinical document automation. This study provides a starting point for research in determining which techniques for preparing qualitative data for use with neural networks are best. It is our intention that the reader should use these implementations as a starting point to design experiments to evaluate various techniques for working with qualitative data in neural networks. The third contribution we make in this work is a new perspective on techniques for using categorical data in neural networks. We organize techniques for using categorical data in neural networks into three categories. We find three distinct patterns in techniques that identify a technique as determined, algorithmic, or automated. The fourth contribution we make is to identify several opportunities for future research. The form of the data that one uses as an input to a neural network is crucial for using neural networks effectively. This work is a tool for researchers to find the most effective technique for working with categorical data in neural networks, in big data settings. To the best of our knowledge this is the first in-depth look at techniques for working with categorical data in neural networks.

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Section: Related Workmentioning

confidence: 99%

“…Part of this work covers embeddings for natural language data. However, the focus of [15] is on text classification, so it only covers techniques for using categorical data in neural networks for text classification. This work is not limited to neural networks only.…”

Section: Related Workmentioning

confidence: 99%

Survey on categorical data for neural networks

2020

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“…So far, existing methods for text representation are mainly from two perspectives: semantic‐based term weight methods and statistic‐based term weight methods. The semantic method concentrates not only on meaning of the words but also on hidden semantic relationships between words and consequently between documents . Rao et al presented a novel neural network model with two hidden layers to learn continuous text representation, which can capture semantic information between words and sentences.…”

Section: Introductionmentioning

confidence: 99%

“…The semantic method concentrates not only on meaning of the words but also on hidden semantic relationships between words and consequently between documents. 24 Rao et al 25 presented a novel neural network model with two hidden layers to learn continuous text representation, which can capture semantic information between words and sentences. Nguyen et al 26 proposed a new approach for determining the degree of semantic similarity between pairs of short texts by utilizing word embeddings.…”

Section: Introductionmentioning

confidence: 99%

An improved term weighting scheme for text classification

Zhong

2019

Concurrency and Computation

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Summary Text representation is a necessary and primary procedure in performing text classification (TC), which first needs to be obtained through an information‐rich term weighting scheme to achieve higher TC performance. So far, term frequency‐inverse document frequency (TF‐IDF) is the most widely used term weighting scheme, but it suffers from two deficiencies. First, the global weighting factors IDF in TF‐IDF approaches infinity if a certain term does not occur in a text. Second, the IDF is equal to zero if a certain term appears in any text. To offset these drawbacks, we first conduct an in‐depth analysis of the current term weighting schemes, and subsequently, an improved term weighting scheme called term frequency‐inverse exponential frequency (TF‐IEF) and its various variants are proposed. The proposed method replaces IDF with the new global weighting factor IEF to characterize the global weighting factor log‐like IDF in the corpus, which can greatly reduce the effect of feature (term) with high local weighting factor TF in term weighting. As a result, a more representative feature can be generated. We carried out a series of experiments on two commonly used data sets (corpora) utilizing Naïve Bayes and support vector machine classifiers to validate the performance of our proposed schemes. Experimental results explicitly reveal that the proposed term weighting schemes come with better performance than the compared schemes.

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“…Automatic document classification has many applications in numerous electronic business (ebusiness) scenarios [2]. For example, a medium-sized company may receive quite a few emails daily without accurate and concrete information such as recipient's name or department, which have to be read by an assigned agent so that the destinations can be determined.…”

Section: Introductionmentioning

confidence: 99%

Semantic Document Classification based on Strategies of Semantic Similarity Computation and Correlation Analysis

Yang¹,

Wei²,

Tan³

et al. 2019

6th International Conference on Computer Science, Engineering and Information Technology (CSEIT-2019)

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Document (text) classification is a common method in e-business, facilitating users in the tasks such as document collection, analysis, categorization and storage. Semantic analysis can help to improve the performance of document classification. Though having been considered when designing previous methods for automatic document classification, more focus should be given to semantics with the increase number of content-rich electronic documents, forum posts or blogs online, which can reduce human workload by a great margin. This paper proposes a novel semantic document classification approach aiming to resolve two types of semantic problems: (1) polysemy problem, by using a novel semantic similarity computing strategy (SSC) and (2) synonym problem, by proposing a novel strong correlation analysis method (SCM). Experiments show that our strategies can help to improve the performance of the baseline methods.

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Semantic text classification: A survey of past and recent advances

Cited by 152 publications

References 35 publications

Survey on categorical data for neural networks

Survey on categorical data for neural networks

An improved term weighting scheme for text classification

Semantic Document Classification based on Strategies of Semantic Similarity Computation and Correlation Analysis

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