Results 81 to 90 of about 1,143,739 (216)
Explainable and Discourse Topic-aware Neural Language Understanding [PDF]
arXiv, 2020 Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics. While introducing topical semantics in language models, existing approaches incorporate latent document topic proportions and ignore topical discourse in sentences of the document.
arxiv
Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling [PDF]
arXiv, 2019 We introduce Topic Grouper as a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. It is governed by a simple generative model, where the likelihood to generate the training documents via ...
arxiv
SELECTED TOPICS IN INTEGRABLE MODELS [PDF]
Particles and Fields, 2002 Talks presented at the Swieca summer school, LaTex 36 ...
openaire +3 more sources
Topic-Aware Multi-turn Dialogue Modeling [PDF]
arXiv, 2020 In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances. As a conversation goes on, topic shift at discourse-level naturally happens through the continuous multi-turn dialogue context. However, all known retrieval-based systems are
arxiv
Modeling discrete dynamic topics
Proceedings of the Symposium on Applied Computing, 2017 Topic modeling is an important area which aims at indexing and exploring massive data streams. In this paper we introduce a discrete Dynamic Topic Modeling (dDTM) algorithm, which is able to model a dynamic topic that is not necessarily present over all time slices in a stream of documents. Our proposed model has applications in modeling dynamic topics
Bahrainian, Seyed Ali +2 more
openaire +3 more sources
A Gentle Introduction to Topic Modeling Using Python
Theological Librarianship, 2018 Topic modeling is a data mining method which can be used to understand and categorize large corpora of data; as such, it is a tool which theological librarians can use in their professional workflows and scholarly practices.
Micah D. Saxton
doaj +1 more source
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity [PDF]
arXiv, 2017 A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies three elements for assessing diversity: words, topics, and documents as collections of words. Topic models play a central role in this approach.
arxiv
Research Community Mining via Generalized Topic Modeling [PDF]
Mehran University Research Journal of Engineering and Technology, 2012 Mining research community on the basis of hidden relationships present between its entities is important from academic recommendation point of view. Previous approaches discovered research community by using network connectivity based distance measures
Ali Daud +2 more
doaj
Topic Modeling for Research Software
, 2022 Topic Modeling for Research Software ABSTRACT Currently, the amount of daily publications in different fields of Machine Learning makes it impossible for researchers to be up to date and even to find what they’re looking for in a reasonable amount of time.
openaire +2 more sources
Automatic Seed Word Selection for Topic Modeling
IEEE AccessTopic modeling is widely used to uncover latent semantic topics from a corpus. However, topic models often struggle to identify minor topics due to their tendency to prioritize dominant patterns in the data. They are also hindered by polysemous words and
Dahyun Jeong +3 more
doaj +1 more source