| Challenge: | Labeled Anchors is an interactive and supervised topic model based on the anchor words algorithm . |
| Approach: | They propose an interactive supervised topic model based on the anchor words algorithm . they propose a classifier which requires no training beyond topic inference . |
| Outcome: | The proposed model is human-interpretable and fast, and can be interactive. |
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The Strength of the Weakest Supervision: Topic Classification Using Class Labels (N19-3)
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| Challenge: | a topic classifier can understand only class labels when training for tasks that require a large amount of labeled documents. |
| Approach: | They propose an algorithm that can initialize a topic classifier using only class labels . they propose a method that combines word embedding and naive Bayes classification . |
| Outcome: | The proposed approach saves significant initial labeling effort by providing a "warm start" the proposed approach can be fine-tuned with more labeled documents to reach a certain performance level. |
Unsupervised Hierarchical Topic Modeling via Anchor Word Clustering and Path Guidance (2024.findings-emnlp)
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| Challenge: | Existing hierarchical topic models often ignore the role of anchor words that guide text generation. |
| Approach: | They propose to use a clustering algorithm to detect anchor words that are highly consistent with every topic and add a causal path to the popular Variational Auto-Encoder framework. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three datasets. |
Joint Embedding of Words and Labels for Text Classification (P18-1)
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Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
| Challenge: | Existing approaches to text classification use word embeddings to capture semantic regularities between words. |
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Hierarchical Label Generation for Text Classification (2023.findings-eacl)
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| Challenge: | None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document. |
| Approach: | They propose a method that captures the label hierarchy for real-world classification applications by using a taxonomic hierarchy. |
| Outcome: | The proposed method can generate unseen labels in subword level. |
Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm (D19-1)
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| Challenge: | spectral topic models lack reliability in real data and lack of practical implementations. |
| Approach: | They propose to use a spectral topic inference method to infer correlations between topics in real data and a matrix-based approach to inference. |
| Outcome: | The proposed method outperforms tensor-based methods and probabilistic methods in real data and provides a complete guide to correlated topic modeling. |
Topic Modeling: Contextual Token Embeddings Are All You Need (2024.findings-emnlp)
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| Challenge: | Current neural approaches to topic modeling have not been able to solve all of the problems. |
| Approach: | They propose a topic modeling approach that uses document contextual token embeddings to find topics and find topic spans within documents. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on a comprehensive set of topic model evaluation metrics. |
Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors (2020.emnlp-main)
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| Challenge: | Recent research has shown that contextualized models generate dynamic embeddings for words in context, but static embedds are often overlooked in this trend towards contextualized modeling. |
| Approach: | They propose a method that learns a transformation through static anchors and requires only another pre-trained model. |
| Outcome: | The proposed method improves a range of benchmark tasks that test contextual variations of meaning across different usages of a word and across different words as they are used in context. |
Augmented Natural Language for Generative Sequence Labeling (2020.emnlp-main)
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| Challenge: | generative framework for joint sequence labeling and sentence-level classification is general purpose, performing well on few-shot learning, low resource, and high resource tasks. |
| Approach: | They propose a generative framework for joint sequence labeling and sentence-level classification . their framework incorporates label semantics and shares knowledge across tasks . |
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Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics (2022.naacl-main)
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| Challenge: | Recent work incorporates pre-trained word embeddings into Neural Topic Models (NTMs), generating highly coherent topics. |
| Approach: | They conduct thorough experiments to investigate whether embeddings directly with an appropriate word selection method can generate more coherent and diverse topics than NTMs. |
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Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence (2021.acl-short)
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| Challenge: | Recent neural topic models extract words from documents, but they are not coherent . coherence is crucial for topic models, but many use bag-of-words document representations as input . pre-trained language models are becoming ubiquitous in natural language processing . |
| Approach: | They combine contextualized representations with neural topic models to produce more coherent topics . they say that future improvements in language models will translate into better topic models . |
| Outcome: | The proposed approach produces more meaningful and coherent topics than bag-of-words models and recent neural models. |