# Topic Modeling

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/topic-modeling/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers covers a wide range of advancements in natural language processing, including neural network architectures, word representation models, machine translation techniques, text classification algorithms, semantic similarity measures, named entity recognition methods, pretrained language models, sequence-to-sequence learning approaches, topic modeling strategies, and information retrieval systems. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10028 |
| Works | 251 |

## Topic papers all

Showing 15 of 251.

- [Graph neural networks: A review of methods and applications](https://scholariq.org/papers/graph-neural-networks-a-review-of-methods-and-applications-2/)
- [Misinformation and Its Correction](https://scholariq.org/papers/misinformation-and-its-correction/)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://scholariq.org/papers/tinybert-distilling-bert-for-natural-language-understanding/)
- [Graph Neural Networks: A Review of Methods and Applications](https://scholariq.org/papers/graph-neural-networks-a-review-of-methods-and-applications/)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://scholariq.org/papers/a-comprehensive-survey-of-deep-learning-for-image-captioning/)
- [Computational principles of working memory in sentence comprehension](https://scholariq.org/papers/computational-principles-of-working-memory-in-sentence-comprehension/)
- [Toward expert-level medical question answering with large language models](https://scholariq.org/papers/toward-expert-level-medical-question-answering-with-large-language-models/)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://scholariq.org/papers/ctrl-a-conditional-transformer-language-model-for-controllable-generation/)
- [Applying Conditional Random Fields to Japanese Morphological Analysis](https://scholariq.org/papers/applying-conditional-random-fields-to-japanese-morphological-analysis/)
- [Nested incremental modeling in the development of computational theories: The CDP+ model of reading aloud.](https://scholariq.org/papers/nested-incremental-modeling-in-the-development-of-computational-theories-the-cdp/)
- [A Decade Survey of Transfer Learning (2010–2020)](https://scholariq.org/papers/a-decade-survey-of-transfer-learning-2010-2020/)
- [Graph Contextualized Self-Attention Network for Session-based Recommendation](https://scholariq.org/papers/graph-contextualized-self-attention-network-for-session-based-recommendation/)
- [Statistical Dependency Analysis with Support Vector Machines](https://scholariq.org/papers/statistical-dependency-analysis-with-support-vector-machines/)
- [Constructing biological knowledge bases by extracting information from text sources.](https://scholariq.org/papers/constructing-biological-knowledge-bases-by-extracting-information-from-text/)
- [A survey of sentiment analysis in social media](https://scholariq.org/papers/a-survey-of-sentiment-analysis-in-social-media/)

## Topic primary papers

Showing 15 of 93.

- [TinyBERT: Distilling BERT for Natural Language Understanding](https://scholariq.org/papers/tinybert-distilling-bert-for-natural-language-understanding/)
- [Toward expert-level medical question answering with large language models](https://scholariq.org/papers/toward-expert-level-medical-question-answering-with-large-language-models/)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://scholariq.org/papers/ctrl-a-conditional-transformer-language-model-for-controllable-generation/)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://scholariq.org/papers/luke-deep-contextualized-entity-representations-with-entity-aware-self-attention/)
- [Incorporating domain knowledge into topic modeling via Dirichlet Forest priors](https://scholariq.org/papers/incorporating-domain-knowledge-into-topic-modeling-via-dirichlet-forest-priors/)
- [Long Text Generation via Adversarial Training with Leaked Information](https://scholariq.org/papers/long-text-generation-via-adversarial-training-with-leaked-information/)
- [Welcome to the Era of ChatGPT et al.](https://scholariq.org/papers/welcome-to-the-era-of-chatgpt-et-al/)
- [Using large language models in psychology](https://scholariq.org/papers/using-large-language-models-in-psychology/)
- [Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach](https://scholariq.org/papers/knowledge-transfer-for-out-of-knowledge-base-entities-a-graph-neural-network/)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://scholariq.org/papers/bloom-a-176b-parameter-open-access-multilingual-language-model/)
- [Topic Modeling Using Latent Dirichlet allocation](https://scholariq.org/papers/topic-modeling-using-latent-dirichlet-allocation/)
- [What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment](https://scholariq.org/papers/what-to-talk-about-and-how-selective-generation-using-lstms-with-coarse-to-fine-2/)
- [Towards Knowledge-Based Recommender Dialog System](https://scholariq.org/papers/towards-knowledge-based-recommender-dialog-system/)
- [Cognitive Graph for Multi-Hop Reading Comprehension at Scale](https://scholariq.org/papers/cognitive-graph-for-multi-hop-reading-comprehension-at-scale/)
- [GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://scholariq.org/papers/gear-graph-based-evidence-aggregating-and-reasoning-for-fact-verification/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.