WebClustering users by short text streams is more challenging than in the case of long documents associated with them as it is difficult to track users' dynamic interests in streaming sparse data. To obtain better user clustering performance, we propose a user collaborative interest tracking model (UCIT) that aims at tracking changes of each user ... WebMeasuring semantic similarity between short texts is challenging because the meaning of short texts may vary dramatically even by a few words due to their limited lengths. In this paper, we propose a novel similarity measure for terms that allows better clustering performance than the state-of-the-art method. To achieve such performance, we …
Effects on Time and Quality of Short Text Clustering during Real …
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A Self-Training Approach for Short Text Clustering
WebFeb 1, 2024 · Traditional short text clustering methods such as vector space model cannot solve the problems caused by high-dimensional and sparse features. Some researchers work on expanding and enriching the context of data from Wikipedia or an ontology . Some researchers have proposed short text clustering based on semantics [4, 5]. But these … WebGiven the dynamic nature of social media, there is a need to dynamically cluster users in the context of streams of short texts. User clustering in this setting is more challenging than in the case of long documents, as it is difficult to capture the users’ dynamic topic distributions in sparse data settings. To address this problem, we ... WebIn this article, we present a novel approach to cluster short text messages via transfer learning from auxiliary long text data. We show that while some previous work exists that enhance short text clustering with related long texts, most of them ignore the semantic and topical inconsistencies between the target and auxiliary data and hurt the ... go ge credit card