Lifelong Learning Augmented Short Text Stream Clustering Method

Qiang, Jipeng; Xu, Wanyin; Li, Yun; Yuan, Yunhao; Zhu, Yi

doi:10.1109/access.2021.3078096

Cited by 5 publications

(11 citation statements)

References 23 publications

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“…As another method in this group, Lifelong Learning Augmented Short Text Stream Clustering (LAST; Qiang et al, 2021) utilizes an episodic memory and an experience replay module. Here, experience replay indicates the update process of CF vectors.…”

Section: Short Text Stream Clustering: Analyzing the Recent Studiesmentioning

confidence: 99%

“…The most commonly used metric among these is Normalized Mutual Information (NMI). The calculation of NMI is given in Equation () (Qiang et al, 2021).

NMI (K, C) = \frac{{normal∑}_{c} {normal∑}_{k} n_{c, k} \log (\frac{N - n_{c, k}}{n_{c} . n_{k}})}{\sqrt{\sum_{c} n_{c} \log ((), \frac{n_{c}}{N}) \sum_{k} n_{k} \log \frac{n_{k}}{N}}}

where n c is the number of documents in class c , n k is the number of documents in cluster k , n c , k is the number of documents in class c as well as in cluster k , and N is the number of documents in the dataset.…”

Section: Clustering Quality Measuresmentioning

confidence: 99%

“…It can be simply calculated as the ratio of correctly clustered documents to all documents, as given in Equation (). Several variations of the accuracy calculation are also used, as proposed by Qiang et al (2021).

Accuracy (K, C) = \frac{1}{N} {normal∑}_{i}^{N} δ (c_{i}, map (k_{i})),

where k i represents the clustering result and c i shows the real label corresponding to data represented with x i .…”

Section: Clustering Quality Measuresmentioning

confidence: 99%

“…Another clustering quality measure is completeness . Completeness can be considered as an index and it is calculated by dividing the members of the same class in the truth group into the same cluster (Qiang et al, 2021). The completeness is calculated with the formula given in Equation ().

Completeness (K, C) = 1 - \frac{H (K| C)}{H (K)},

where

H (K| C)

is the conditional entropy of the class assigned to a given cluster, and H ( K ) is the class entropy (Rosenberg & Hirschberg, 2007).…”

Section: Clustering Quality Measuresmentioning

confidence: 99%

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Recent methods on short text stream clustering: A survey study

Maden

Karagöz

2023

WIREs Computational Stats

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The volume and the velocity of data in social media are increasing and the social media has become a very useful environment to detect and track the real‐world events. However, to fulfill this, it is crucial to group‐related texts according to their topics and clustering takes an essential role at this point since we have no prior knowledge about the topics and their evolution in social media. In this survey, we review the current approaches and techniques proposed for short text stream clustering in recent years. The reviewed techniques are grouped according to their methodology and discussed in detail. Also, the datasets utilized to evaluate the performance of the proposed methods and the results are summarized together with the clustering quality measures used for these evaluations. Furthermore, current challenges about short‐text stream clustering are discussed.This article is categorized under: Data: Types and Structure > Streaming Data

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Section: Short Text Stream Clustering: Analyzing the Recent Studiesmentioning

confidence: 99%

“…The most commonly used metric among these is Normalized Mutual Information (NMI). The calculation of NMI is given in Equation () (Qiang et al, 2021).

NMI (K, C) = \frac{{normal∑}_{c} {normal∑}_{k} n_{c, k} \log (\frac{N - n_{c, k}}{n_{c} . n_{k}})}{\sqrt{\sum_{c} n_{c} \log ((), \frac{n_{c}}{N}) \sum_{k} n_{k} \log \frac{n_{k}}{N}}}

Section: Clustering Quality Measuresmentioning

confidence: 99%

Accuracy (K, C) = \frac{1}{N} {normal∑}_{i}^{N} δ (c_{i}, map (k_{i})),

where k i represents the clustering result and c i shows the real label corresponding to data represented with x i .…”

Section: Clustering Quality Measuresmentioning

confidence: 99%

Completeness (K, C) = 1 - \frac{H (K| C)}{H (K)},

where

H (K| C)

is the conditional entropy of the class assigned to a given cluster, and H ( K ) is the class entropy (Rosenberg & Hirschberg, 2007).…”

Section: Clustering Quality Measuresmentioning

confidence: 99%

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Recent methods on short text stream clustering: A survey study

Maden

Karagöz

2023

WIREs Computational Stats

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“…The crucial problem limiting the performance of jargon synonym finding is the short context of jargon words, i.e., the messages where jargon words exist are generally very short. Short text modeling is a promising research topic in natural language processing area, such as topic modeling [35], [36], clustering [37], [38], and summarization [39], while these studies mainly focus on general texts. In the future, how to utilize these methods to model jargon words to improve the performance of synonym finding should be further investigated.…”

Section: A Implicationsmentioning

confidence: 99%

JargonFM: A Framework with Multiple Interpretation Modes for Jargon Understanding in Online Communities

Guan¹,

Zhang²,

Liu³

et al. 2022

Preprint

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<p>Jargon words are commonly used in the communication of online communities. These words are characterized by special and implicit meanings that can only be comprehended by a small group of users, which brings challenges to community regulation and user engagement. For this problem, we present JargonFM, a framework with multiple interpretation modes for jargon understanding in online communities. JargonFM is designed based on the scientific explanation framework and supports three interpretation modes: jargon category prediction based on a Jargon Classifier, similar word identification based on a Jargon Synonym Selector, and representative text selection based on an Example Sentence Selector. We also implemented a jargon interpreter to demonstrate the usage and usefulness of our interpretation framework. Automatic and human evaluations suggest that JargonFM can explain jargon words more accurately and more efficiently than the existing interpretation methods, leading to its wide acceptance among the evaluation participants.</p>

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Microblog topic detection using evolutionary clustering and social network information

Mashayekhi

Alavi

Rezaie

2023

Concurrency and Computation

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SummaryThe messages in microblogs, like Twitter, present a wealth of information around the hot topics and public opinions. Topic detection and tracking enables a better understanding of the incoming information from social networks. Many of the studies performed in this field consider a set of predefined number of topics (clusters) which remain constant during the detection process. Such approaches are not efficient when data is dynamic and cumulative. In addition, nonparametric evolutionary topic models do not have an accurate performance on short texts due to the data deficiency. In this article, we propose an evolutionary clustering model based on the Bayesian nonparametric Dirichlet process mixture model, which automatically determines the number of clusters. To improve the algorithm performance when dealing with short social media texts, temporal information of tweets and other social network parameters are used in a weighted combination to augment the text similarity in detecting topics. Model evaluation is performed on the data gathered from the Twitter during a 2.5‐month period. Results reveal that the social network information can promote the topic detection performance. The proposed model outperforms previous study in terms of topic coherence and clustering performance, leading to a better clustering on short texts.

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Lifelong Learning Augmented Short Text Stream Clustering Method

Cited by 5 publications

References 23 publications

Recent methods on short text stream clustering: A survey study

Recent methods on short text stream clustering: A survey study

JargonFM: A Framework with Multiple Interpretation Modes for Jargon Understanding in Online Communities

Microblog topic detection using evolutionary clustering and social network information

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