Text Similarity Measurement of Semantic Cognition Based on Word Vector Distance Decentralization With Clustering Analysis

Zhou, Shenghan; Xu, Xiaobin; Liu, Yinglai; Chang, Runfeng; Xiao, Yiyong

doi:10.1109/access.2019.2932334

Cited by 20 publications

(15 citation statements)

References 16 publications

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“…In the majority of identified research works, word embeddings are utilized to represent text (Dai et al 2017;Ertugrul et al 2017;Jiang et al 2019;Mendonça et al 2019;Miranda et al 2020;Singh and Shashi 2019;Zhou et al 2019). Especially for short texts, word embeddings have shown to be useful to augment traditional features (Comito et al 2019) or to expand a small set of representative terms with semantically similar words (Qiang et al 2019;Viegas et al 2019).…”

Section: Related Workmentioning

confidence: 99%

Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content

Bongard¹,

Kersten²

2021

Preprint

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The Twitter Stream API offers the possibility to develop (near) real-time methods and applications to detect and monitor impacts of crisis events and their changes over time. As demonstrated by various related research, the content of individual tweets or even entire thematic trends can be utilized to support disaster management, fill information gaps and augment results of satellite-based workflows as well as to extend and improve disaster management databases. Considering the sheer volume of incoming tweets, it is necessary to automatically identify the small number of crisis-relevant tweets and present them in a manageable way.Current approaches for identifying crisis-related content focus on the use of supervised models that decide on the relevance of each tweet individually. Although supervised models can efficiently process the high number of incoming tweets, they have to be extensively pre-trained. Furthermore, the models do not capture the history of already processed messages. During a crisis, various and unique sub-events can occur that are likely to be not covered by the respective supervised model and its training data. Unsupervised learning offers both, to take into account tweets from the past, and a higher adaptive capability, which in turn allows a customization to the specific needs of different disasters. From a practical point of view, drawbacks of unsupervised methods are the higher computational costs and the potential need of user interaction for result interpretation.In order to enhance the limited generalization capabilities of pre-trained models as well as to speed up and guide unsupervised learning, we propose a combination of both concepts. A successive clustering of incoming tweets allows to semantically aggregate the stream data, whereas pre-trained models allow to identify potentially crisis-relevant clusters. Besides the identification of potentially crisis-related content based on semantically aggregated clusters, this approach offers a sound foundation for visualizations, and further related tasks, like event detection as well as the extraction of detailed information about the temporal or spatial development of events.Our work focuses on analyzing the entire freely available Twitter stream by combining an interval-based semantic clustering with an supervised machine learning model for identifying crisis-related messages. The stream is divided into intervals, e.g. of one hour, and each tweet is projected into a numerical vector by using state-of-the-art sentence embeddings. The embeddings are then grouped by a parametric Chinese Restaurant Process clustering. At the end of each interval, a pre-trained feed-forward neural network decides whether a cluster contains crisis-related tweets. With a further developed concept of cluster chains and central centroids, crisis-related clusters of different intervals can be linked in a topic- and even subtopic-related manner.Initial results show that the hybrid approach can significantly improve the results of pre-trained supervised methods. This is especially true for categories in which the supervised model could not be sufficiently pre-trained due to missing labels. In addition, the semantic clustering of tweets offers a flexible and customizable procedure, resulting in a practical summary of topic-specific stream content.

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Section: Related Workmentioning

confidence: 99%

Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content

Bongard¹,

Kersten²

2021

Preprint

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“…The mixed measures took advantage of thesaurus‐based and corpus‐based semantic methods to obtain better performance. Afterward, more and more semantic similarity technologies have been applied to a document or text clustering, and have achieved good results 73,74 …”

Section: Applicationsmentioning

confidence: 99%

A survey on the techniques, applications, and performance of short text semantic similarity

Han

Zhang

Yuan

et al. 2020

Concurrency and Computation

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Summary Short text similarity plays an important role in natural language processing (NLP). It has been applied in many fields. Due to the lack of sufficient context in the short text, it is difficult to measure the similarity. The use of semantics similarity to calculate textual similarity has attracted the attention of academia and industry and achieved better results. In this survey, we have conducted a comprehensive and systematic analysis of semantic similarity. We first propose three categories of semantic similarity: corpus‐based, knowledge‐based, and deep learning (DL)‐based. We analyze the pros and cons of representative and novel algorithms in each category. Our analysis also includes the applications of these similarity measurement methods in other areas of NLP. We then evaluate state‐of‐the‐art DL methods on four common datasets, which proved that DL‐based can better solve the challenges of the short text similarity, such as sparsity and complexity. Especially, bidirectional encoder representations from transformer model can fully employ scarce information of short texts and semantic information and obtain higher accuracy and F1 value. We finally put forward some future directions.

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“…Recently, the semantic similarity measures between texts have been studied in many natural language processing applications. A range of researchers have used these measures to improve their study works [64]- [66]. These methods proposed for estimating the similarity between two documents include three different types, i.e., lexical matching, linguistic analysis, and semantic features.…”

Section: Semantic Similarity Measurement and Word Embeddingsmentioning

confidence: 99%

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

et al. 2020

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Although Vietnamese is the 17 th most popular native-speaker language a in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts which are commonly used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexicalbased MRC method that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. We compare the performance of the proposed model with several baseline lexical-based and neural network-based models. Our proposed method achieves 61.81% by accuracy, which is 5.51% higher than the best baseline model. We also measure human performance on our dataset and find that there is a big gap between machine-model and human performances. This indicates that significant progress can be made on this task. The dataset is freely available on our website b for research purposes.

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Text Similarity Measurement of Semantic Cognition Based on Word Vector Distance Decentralization With Clustering Analysis

Cited by 20 publications

References 16 publications

Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content

Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content

A survey on the techniques, applications, and performance of short text semantic similarity

Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

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