Bitaraf, Ehsan · Figshare · 2024
In this dataset, a total of 9691 articles within the medical domain were collected for analysis. Topic extraction was conducted utilizing two distinct methodologies: Textrank and LLM. These approaches were applied in conjunction with the keywords present in the articles, forming the dataset for analysis. The dataset encompasses various fields such as article title, publication year, PMID, keyword listings, topics derived through the Textrank algorithm, and topics identified through LLM.
Cited by 0Open access
Bitaraf, Ehsan · Figshare · 2024
In this dataset, a total of 9691 articles within the medical domain were collected for analysis. Topic extraction was conducted utilizing two distinct methodologies: Textrank and LLM. These approaches were applied in conjunction with the keywords present in the articles, forming the dataset for analysis. The dataset encompasses various fields such as article title, publication year, PMID, keyword listings, topics derived through the Textrank algorithm, and topics identified through LLM.
Cited by 0Open access
Marian-andrei Rizoiu, Julien Velcin, Marian-andrei Rizoiu et al. · 2011
HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
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Team Members, Sagarika Adepu, Ha Susmitha et al. · 2012
The extraction of moving objects is an important technique for obtaining semantic feature information in a video sequence. Several approaches for moving object extraction have been proposed. However, a number of these methods require constraint conditions to be satisfied. As a result, a general purpose technique with high accuracy for moving object extraction has not yet been established. In this paper, a
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Shameem Ahamed Puthiya Parambath · 2012
Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summarization, efficient information retrieval etc. Generating article bundles from a large number of input articles, based on the associated features of the articles is tedious and computationally expensive task. In this report we propose an automatic two-step approach for topic extraction and bundling of related articles from a set of scientific articles in real-time. For topic extraction, we make use of Latent Dirichlet Allocation (LDA) topic modeling techniques and for bundling, we make use of hierarchical agglomerative clustering techniques. We run experiments to validate our bundling semantics and compare it with existing models in use. We make use of an online crowdsourcing marketplace provided by Amazon called Amazon Mechanical Turk to carry out experiments. We explain our experimental setup and empirical results in detail and
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Julien Velcin, Jean‐Gabriel Ganascia · Lecture notes in computer science · 2007
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Chaima Messaoudi, Zahia Guessoum, Lotfi Ben Romdhane · SPIRE - Sciences Po Institutional REpository · 2021
International audience
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Jean-Charles Lamirel · HAL (Le Centre pour la Communication Scientifique Directe) · 2018
International audience
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Ralph Grishman, Information Extraction (ie, George Garrick et al. · 2006
identifying the instances of the important entities, relations, and events for a domain from unstructured text. 3
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Bertrand, Florian · Repository of the University of Namur
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Carlos Rubio Alcalá · Borealis – An International Journal of Hispanic Linguistics · 2016
This paper offers new data to support findings about Topic extraction from adverbial clauses. Since such clauses are strong islands, they should not allow extraction of any kind, but we show here that if the appropriate conditions are met, Topics of the CLLD kind in Romance can move out of them. We propose that two conditions must be met for such movement to be possible: the first is that the adverbial clause must have undergone topicalisation in the first place; the second is that the adverbial clause is inherently topical from a semantic viewpoint. Contrast with other language families (Germanic, Quechua and Japanese) is provided and the semantic implications of the proposal are briefly discussed.Keywords: topicalisation; Clitic Left Dislocation; syntactic islands; adverbial clausesEste artículo ofrece nuevos datos sobre la extracción de Tópicos desde oraciones subordinadas adverbiales. Dado que dichas oraciones son islas fuertes, no deberían permitir extracción de ningún tipo, pero mostramos que si se dan las condiciones apropiadas, los Tópicos del tipo CLLD en lenguas románicas pueden desplazarse fuera de ellas. Proponemos que se deben cumplir dos condiciones para que ese movimiento sea posible: la primera es que la propia subordinada adverbial se haya topicalizado en primer lugar; la segunda es que la subordinada adverbial sea inherentemente un Tópico desde el punto de vista semántico. Proporcionamos también algunos contrastes con otras familias lingüísticas (germánica, quechua y japonés) y se discuten brevemente las implicaciones semánticas de la propuesta.Palabras clave: topicalización; dislocación a la izquierda con clítico; islas sintácticas; oraciones adverbiales
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Shahriyar Babaki, Shital Bagankar, Tanya Goyal et al. · Lecture notes in computer science · 2025
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Faleiros, Thiago de Paulo, Lopes, Alneu de Andrade · Scientific Electronic Library Online (São Paulo Research Foundation, Latin American and Caribbean Center on Health Sciences Information, Conselho Nacional de Desenvolvimento Científico e Tecnológico) · 2015
This article presents a bipartite graph propagation method to be applied to different tasks in the machine learning unsupervised domain, such as topic extraction and clustering. We introduce the objectives and hypothesis that motivate the use of graph based method, and we give the intuition of the proposed Bipartite Graph Propagation Algorithm. The contribution of this study is the development of new method that allows the use of heuristic knowledge to discover topics in textual data easier than it is possible in the traditional mathematical formalism based on Latent Dirichlet Allocation (LDA). Initial experiments demonstrate that our Bipartite Graph Propagation algorithm return good results in a static context (offline algorithm). Now, our research is focusing on big amount of data and dynamic context (online algorithm).
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Chaima Messaoudi, Zahia Guessoum, Lotfi BenRomdhane · Computing and Informatics · 2022
The number of Twitter users is increasing and the quantity of produced data is growing. Using this big data to analyze user behavior has become a very active field. The two key challenges of this paper are extracting data from Twitter and extracting topics from user tweets. The proposed approach uses data crawling to collect data from Twitter and a bunch of natural language processing techniques to extract information from the so collected data and build a dataset. Thereafter, we use K-means clustering and Latent Dirichlet Allocation to extract the prevalent topics from this dataset, as they are the most common in the literature. Our proposal is generic, it can be reused by scientists to annotate any text collection.
Cited by 3Open access
Rui Wang, Deyu Zhou, Haiping Huang et al. · IEEE Transactions on Neural Networks and Learning Systems · 2024
To automatically mine structured semantic topics from text, neural topic modeling has arisen and made some progress. However, most existing work focuses on designing a mechanism to enhance topic coherence but sacrificing the diversity of the extracted topics. To address this limitation, we propose the first neural-based topic modeling approach purely based on mutual information maximization, called the mutual information topic (MIT) model, in this article. The proposed MIT significantly improves topic diversity by maximizing the mutual information between word distribution and topic distribution. Meanwhile, MIT also utilizes Dirichlet prior in latent topic space to ensure the quality of mined topics. The experimental results on three publicly benchmark text corpora show that MIT could extract topics with higher coherence values (considering four topic coherence metrics) than competitive approaches and has a significant improvement on topic diversity metric. Besides, our experiments prove that the proposed MIT converges faster and more stable than adversarial-neural topic models.
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Rui Wang, Peng Ren, Xing Liu et al. · Information Processing & Management · 2024
Cited by 2
Yongtao Ye, Yajun Du, Xia Fu · 2016
Microblog, with its wide participation and convenience, has changed the way that people get news about current events. In recent years, lots of breaking news and hot topics are released by microblog platform firstly, as well as its much wider and more distribution than traditional media platform. Extracting these useful information in real-time will help us to grasp the latest and hottest topics which currently discussed by microblog users. However, due to the micrblog content is short and sparse, traditional topic extraction methods can not be used directly. In this paper, we propose a new topic model named Microblog Features Latent Dirichlet Allocation (MF-LDA) to extract microblog topics. We incorporate five microblog's unique features: support, comment, retweet, publish time and user authority into LDA model. These features are utilized to compute each microblog's attention value, authority value and word frequency. And the higher feature value of a term, the greater probability of a hot topic it be. Experimental results on real datasets demonstrated our MF-LDA model is more efficient and accurate than other methods in hot topic extraction.
Cited by 6
Cheng Wang, Ya Zhou · Journal of Physics Conference Series · 2021
Abstract With the advent of the era of intelligent education, online learning has become a new way of learning. Online learning will produce a lot of review data, this paper focuses on how to correctly reflect learners’ emotional attitude towards the curriculum, this paper studies the mining of text topic emotion in MOOC course review data set. Firstly, the balanced algorithm is used for sample equalization, secondly, we use Albert + bilstm model to classify course reviews, finally, the LDA model is used to extract the corresponding topics. Compared with the experimental model, the proposed model has higher accuracy and stability. Machine learning method has some advantages, but the model is more complex, and it can be simplified in the future.
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Sungjick Lee, Hanjoon Kim · 2008
This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can produce a short summary of news articles. As on-line text documents rapidly increase in size with the growth of WWW, keyword extraction has become a basis of several text mining applications such as search engine, text categorization, summarization, and topic detection. Manual keyword extraction is an extremely difficult and time consuming task; in fact, it is almost impossible to extract keywords manually in case of news articles published in a single day due to their volume. For a rapid use of keywords, we need to establish an automated process that extracts keywords from news articles. We propose an unsupervised keyword extraction technique that includes several variants of the conventional TF-IDF model with reasonable heuristics.
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2009
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