Примеры использования Topic modeling на Английском языке и их переводы на Русский язык
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Topic modeling in natural language texts.
Lda2vec approach should improve quality of topic modeling.
Research Interests: topic modeling, machine learning.
This project aims to implement the thermodynamic approach for topic modeling.
Topic modeling is one of the leading algorithms for analysis of large text collections.
Second, the project seeks to regularize topic modeling algorithms so as to improve their stability.
At the end, the seminar participants discussed in which projects they apply orplan to apply topic modeling.
Data uploading for third-party software(gCluto,Stanford Topic Modeling Toolbox, NodeXL, and TopicMiner);
Topic modeling is a method for building a model of a collection of text documents.
Sergey Nikolenko proposes new metrics to improve the quality of the results of topic modeling at his work.
Konstantin Vorontsov presented topic modeling based on additive regularization of topic models ARTM.
Oleg Nagorny reported about the potential and limitation of the topic modeling method.
It uses local, global, syntactic-based,GloVe, topic modeling and automatic term recognition features.
The new advanced approach‘ARTM' was presented,capable to significantly improve the results of topic modeling.
The competition was held in Kharkiv,Dan spoke on the topic"Modeling handling car" and was awarded a diploma.
According to Sergei, SIGIR was a very inspiring event, had a lot of interesting reports about"deep learning" anda little about"topic modeling.
Denis showed step by step all the stages of topic modeling, from preprocessing of the data to decoding topics and analyzing of the results.
It also develops approaches to metric testing andtheoretical concepts of topic modeling quality and ground truth.
Their presentation focused on the method of topic modeling and different mathematical solutions, which would produce reliable and stable results.
Fourth, the project team invests a lot of effort in developing and maintaining TopicMiner,a GUI-based research software for topic modeling.
The second part of the seminar will focus on the integration of topic modeling into quantitative research and its application for the analysis of online communities.
In his presentation, Prof. Vorontsov reviewed the problems of the ambiguity of stochastic matrix decomposition andits impact on results of topic modeling procedures.
LINIS researchers Olessia Koltsova andSergei Koltcov presented their report on"Topic Modeling Stability and Granulated LDA" at the scientific seminar of the conference.
Ksenia Konstantinova provided the first results of the work in which she analyzes communication of the audience in online games during broadcastings using topic modeling.
The project explores possibilities and limitations of topic modeling for frame detection and analysis in news items related to the 2014-2015 Ukrainian crisis.
Topic modeling is a promising instrument for computational social science and digital humanities as it allows to automatically reveal thematic structure of large text collections- an immensely important task in the era of big Internet data.
For example, methods of Natural Language Processing, such as topic modeling are widely used in game researchers, which are similar to what our research group works on.
Her report"Topic modeling of user-generated content: opportunities and limitations" was devoted to considering the possibilities and limitations of topic models, especially LDA models.
Sergei Koltsov made the report on new possibilities in the sphere of topic-modeling:« Topic Modeling in Online Communication Research: New Possibilities and Challenges».
For this, we offer a number of improvements for topic modeling algorithms whose quality has been tested both manually and with a specially developed quality metric- tf-idf coherence.