Network Presentation of Texts and Clustering of Messages

Orekhov, Andrey V.; Kharlamov, Alexander A.; Bodrunova, Svetlana S.

doi:10.1007/978-3-030-34770-3_18

Cited by 3 publications

(2 citation statements)

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“…Several clusters automatic determination is an urgent problem in many cases of preliminary typologization of empirical data. For example, in cytometric blood analysis [40], in automatic analysis of texts [41] both on topics [23,42], and by emotional coloring [43], as well in all other cases when the number of clusters is a priori unknown.…”

Section: Analytical Generalization Of the "Elbow Method" Heuristicmentioning

confidence: 99%

Quasi-Deterministic Processes with Monotonic Trajectories and Unsupervised Machine Learning

Orekhov

2021

Mathematics

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This paper aims to consider approximation-estimation tests for decision-making by machine-learning methods, and integral-estimation tests are defined, which is a generalization for the continuous case. Approximation-estimation tests are measurable sampling functions (statistics) that estimate the approximation error of monotonically increasing number sequences in different classes of functions. These tests make it possible to determine the Markov moments of a qualitative change in the increase in such sequences, from linear to nonlinear type. If these sequences are trajectories of discrete quasi-deterministic random processes, then moments of change in the nature of their growth and qualitative change in the process match up. For example, in cluster analysis, approximation-estimation tests are a formal generalization of the “elbow method” heuristic. In solid mechanics, they can be used to determine the proportionality limit for the stress strain curve (boundaries of application of Hooke’s law). In molecular biology methods, approximation-estimation tests make it possible to determine the beginning of the exponential phase and the transition to the plateau phase for the curves of fluorescence accumulation of the real-time polymerase chain reaction, etc.

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Section: Analytical Generalization Of the "Elbow Method" Heuristicmentioning

confidence: 99%

Quasi-Deterministic Processes with Monotonic Trajectories and Unsupervised Machine Learning

Orekhov

2021

Mathematics

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“…Earlier, we have conceptualized and theoretically modelled text clustering with hierarchic agglomerative methods with the Markov stopping moment for small datasets; the models showed positive results, while pre-processing was not yet a matter of our concern. We have also suggested their application to network-like representation of texts and sentiment analysis [7,8]. In this paper, we test the proposed method of agglomerative clustering by Ward's method with Markov stopping moment for several options of text representation, including more traditional tf-idf and improved word2vec and the sentence embeddings-based text representation.…”

Section: Text Classification Vs Text Clustering: Approaches To Automa...mentioning

confidence: 99%

Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment

et al. 2020

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The paper is dedicated to solving the problem of optimal text classification in the areaof automated detection of typology of texts. In conventional approaches to topicality-based textclassification (including topic modeling), the number of clusters is to be set up by the scholar, andthe optimal number of clusters, as well as the quality of the model that designates proximity oftexts to each other, remain unresolved questions. We propose a novel approach to the automateddefinition of the optimal number of clusters that also incorporates an assessment of word proximityof texts, combined with text encoding model that is based on the system of sentence embeddings.Our approach combines Universal Sentence Encoder (USE) data pre-processing, agglomerativehierarchical clustering by Ward’s method, and the Markov stopping moment for optimal clustering.The preferred number of clusters is determined based on the “e-2” hypothesis. We set up anexperiment on two datasets of real-world labeled data: News20 and BBC. The proposed model istested against more traditional text representation methods, like bag-of-words and word2vec, to showthat it provides a much better-resulting quality than the baseline DBSCAN and OPTICS models withdifferent encoding methods. We use three quality metrics to demonstrate that clustering quality doesnot drop when the number of clusters grows. Thus, we get close to the convergence of text clusteringand text classification.

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