Relevant bibliographies by topics / Topic interpretability

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Topic interpretability'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Topic interpretability"

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ZHAI, LIDONG, ZHAOYUN DING, YAN JIA, and BIN ZHOU. "A WORD POSITION-RELATED LDA MODEL." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (2011): 909–25. http://dx.doi.org/10.1142/s0218001411008890.

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LDA (Latent Dirichlet Allocation) proposed by Blei is a generative probabilistic model of a corpus, where documents are represented as random mixtures over latent topics, and each topic is characterized by a distribution over words, but not the attributes of word positions of every document in the corpus. In this paper, a Word Position-Related LDA Model is proposed taking into account the attributes of word positions of every document in the corpus, where each word is characterized by a distribution over word positions. At the same time, the precision of the topic-word's interpretability is im
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Cardenas, Ronald, Kevin Bello, Alberto Coronado, and Elizabeth Villota. "Improving Topic Coherence Using Entity Extraction Denoising." Prague Bulletin of Mathematical Linguistics 110, no. 1 (2018): 85–101. http://dx.doi.org/10.2478/pralin-2018-0004.

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Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by
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Palese, Biagio, and Gabriele Piccoli. "Evaluating Topic Modeling Interpretability Using Topic Labeled Gold Standard Sets." Communications of the Association for Information Systems 47 (2020): 433–51. http://dx.doi.org/10.17705/1cais.04720.

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Chauhan, Uttam, and Apurva Shah. "Improving Semantic Coherence of Gujarati Text Topic Model Using Inflectional Forms Reduction and Single-letter Words Removal." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 1 (2021): 1–18. http://dx.doi.org/10.1145/3447760.

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A topic model is one of the best stochastic models for summarizing an extensive collection of text. It has accomplished an inordinate achievement in text analysis as well as text summarization. It can be employed to the set of documents that are represented as a bag-of-words, without considering grammar and order of the words. We modeled the topics for Gujarati news articles corpus. As the Gujarati language has a diverse morphological structure and inflectionally rich, Gujarati text processing finds more complexity. The size of the vocabulary plays an important role in the inference process an
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Arnold, Corey W., Andrea Oh, Shawn Chen, and William Speier. "Evaluating topic model interpretability from a primary care physician perspective." Computer Methods and Programs in Biomedicine 124 (February 2016): 67–75. http://dx.doi.org/10.1016/j.cmpb.2015.10.014.

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Spasic, Irena, and Kate Button. "Patient Triage by Topic Modeling of Referral Letters: Feasibility Study." JMIR Medical Informatics 8, no. 11 (2020): e21252. http://dx.doi.org/10.2196/21252.

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Background Musculoskeletal conditions are managed within primary care, but patients can be referred to secondary care if a specialist opinion is required. The ever-increasing demand for health care resources emphasizes the need to streamline care pathways with the ultimate aim of ensuring that patients receive timely and optimal care. Information contained in referral letters underpins the referral decision-making process but is yet to be explored systematically for the purposes of treatment prioritization for musculoskeletal conditions. Objective This study aims to explore the feasibility of
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Lauscher, Anne, Pablo Ruiz Fabo, Federico Nanni, and Simone Paolo Ponzetto. "Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability." Italian Journal of Computational Linguistics 2, no. 2 (2016): 67–87. http://dx.doi.org/10.4000/ijcol.392.

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Sitorus, Angga Pratama, Hendri Murfi, Siti Nurrohmah, and Afif Akbar. "Sensing Trending Topics in Twitter for Greater Jakarta Area." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (2017): 330. http://dx.doi.org/10.11591/ijece.v7i1.pp330-336.

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Information and communication technology grows so fast nowadays, especially related to the internet. Twitter is one of internet applications that produce a large amount of textual data called tweets. The tweets may represent real-world situation discussed in a community. Therefore, Twitter can be an important media for urban monitoring. The ability to monitor the situations may guide local government to respond quickly or make public policy. Topic detection is an important automatic tool to understand the tweets, for example, using non-negative matrix factorization. In this paper, we conducted
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Hyun, Soomin, and Woojin Park. "Modelling postural discomfort perception using CHAID decision tree algorithm." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (2019): 1749–50. http://dx.doi.org/10.1177/1071181319631035.

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Developing quantitative models that predict discomfort levels of working postures has been an important ergonomics research topic. Such modeling not only has practical applications, but also may serve as a useful research method to improve our understanding of the human postural discomfort perception process. While the existing models have focused on achieving high prediction accuracy, less attention has been given to model interpretability, which is vital for understanding a process through modeling. Research is needed to identify the model types or modeling methods that offer high interpreta
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Curini, Luigi, and Alessia Damonte. "Capturing causation in political science: the perspective of research design." Italian Political Science Review/Rivista Italiana di Scienza Politica 51, no. 2 (2021): 157–63. http://dx.doi.org/10.1017/ipo.2021.28.

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AbstractIn the last decades, ‘research design’ has become a strategic topic across political science. An emerging discourse relies on it to encompass paradigmatic oppositions and cultivate a pluralist approach to causation. As an introduction to the special issue on the topic, we offer an outline of the roles that the discipline recognizes to design in its relation to models and contend that, in a time of fascination for predictors, political science pluralism allows for balancing interpretability and validity of findings at once.
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Dissertations / Theses on the topic "Topic interpretability"

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Sathi, Veer Reddy, and Jai Simha Ramanujapura. "A Quality Criteria Based Evaluation of Topic Models." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13274.

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Context. Software testing is the process, where a particular software product, or a system is executed, in order to find out the bugs, or issues which may otherwise degrade its performance. Software testing is usually done based on pre-defined test cases. A test case can be defined as a set of terms, or conditions that are used by the software testers to determine, if a particular system that is under test operates as it is supposed to or not. However, in numerous situations, test cases can be so many that executing each and every test case is practically impossible, as there may be many const
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Book chapters on the topic "Topic interpretability"

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Mavrin, Andrey, Andrey Filchenkov, and Sergei Koltcov. "Four Keys to Topic Interpretability in Topic Modeling." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01204-5_12.

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Blekanov, Ivan S., Svetlana S. Bodrunova, Nina Zhuravleva, Anna Smoliarova, and Nikita Tarasov. "The Ideal Topic: Interdependence of Topic Interpretability and Other Quality Features in Topic Modelling for Short Texts." In Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49570-1_2.

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Wang, Jun, and Kanji Uchino. "Automatic Topic Labeling for Facilitating Interpretability of Online Learning Materials." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35758-0_25.

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Luz De Araujo, Pedro Henrique, and Teófilo De Campos. "Topic Modelling Brazilian Supreme Court Lawsuits." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200855.

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The present work proposes the use of Latent Dirichlet Allocation to model Extraordinary Appeals received by Brazil’s Supreme Court. The data consist of a corpus of 45,532 lawsuits manually annotated by the Court’s experts with theme labels, a multi-class and multi-label classification task. We initially train models with 10 and 30 topics and analyze their semantics by examining each topic’s most relevant words and their most representative texts, aiming to evaluate model interpretability and quality. We also train models with 30, 100, 300 and 1,000 topics, and quantitatively evaluate their potential using the topics to generate feature vectors for each appeal. These vectors are then used to train a lawsuit theme classifier. We compare traditional bag-of-words approaches (word counts and tf-idf values) with the topic-based text representation to assess topic relevancy. Our topics semantic analysis demonstrate that our models with 10 and 30 topics were capable of capturing some of the legal matters discussed by the Court. In addition, our experiments show that the model with 300 topics was the best text vectoriser and that the interpretable, low dimensional representations it generates achieve good classification results.
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Conference papers on the topic "Topic interpretability"

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Doogan, Caitlin, and Wray Buntine. "Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures." In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.naacl-main.300.

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Glendowne, Puntitra, and Dae Glendowne. "Interpretability of API Call Topic Models: An Exploratory Study." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2020. http://dx.doi.org/10.24251/hicss.2020.793.

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Hisano, Ryohei. "Learning Topic Models by Neighborhood Aggregation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/347.

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Topic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the net
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Zhang, Ge, Di Jin, Jian Gao, Pengfei Jiao, Françoise Fogelman-Soulié, and Xin Huang. "Finding Communities with Hierarchical Semantics by Distinguishing General and Specialized topics." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/507.

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Using network topology and semantic contents to find topic-related communities is a new trend in the field of community detection. By analyzing texts in social networks, we find that topics in networked contents are often hierarchical. In most cases, they have a two-level semantic structure with general and specialized topics, to respectively denote common and specific interests of communities. However, the existing community detection methods ignore such a hierarchy and take all words used to describe node semantics from an identical perspective. This indiscriminate use of words leads to natu
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Zhu, Hongyuan, Xi Peng, Vijay Chandrasekhar, Liyuan Li, and Joo-Hwee Lim. "DehazeGAN: When Image Dehazing Meets Differential Programming." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/172.

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Single image dehazing has been a classic topic in computer vision for years. Motivated by the atmospheric scattering model, the key to satisfactory single image dehazing relies on an estimation of two physical parameters, i.e., the global atmospheric light and the transmission coefficient. Most existing methods employ a two-step pipeline to estimate these two parameters with heuristics which accumulate errors and compromise dehazing quality. Inspired by differentiable programming, we re-formulate the atmospheric scattering model into a novel generative adversarial network (DehazeGAN). Such a r
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Alokaili, Areej, Nikolaos Aletras, and Mark Stevenson. "Re-Ranking Words to Improve Interpretability of Automatically Generated Topics." In Proceedings of the 13th International Conference on Computational Semantics - Long Papers. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-0404.

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Bodrunova, Svetlana S., Ivan S. Blekanov, and Mikhail Kukarkin. "Topics in the Russian Twitter and Relations between their Interpretability and Sentiment." In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2019. http://dx.doi.org/10.1109/snams.2019.8931725.

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Adamu, Jamilu. "Insight, limitations, criticism, and interpretability of the use of activation functions in deep learning artificial neural networks." In Emerging Topics in Artificial Intelligence 2020, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2020. http://dx.doi.org/10.1117/12.2566098.

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