| Challenge: | Label-specific topics are widely used for supporting personality psychology, aspectlevel sentiment analysis, and crossdomain sentiment classification. |
| Approach: | They propose a supervised topic model based on the Siamese network which trades off label-specific word distributions with document-specific label distributions in a uniform framework. |
| Outcome: | The proposed model can trade off label-specific word distributions with document-specific label distributions in a uniform framework. |
Similar Papers
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings (2020.tacl-1)
Copied to clipboard
| Challenge: | Experimental results show that the proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
| Approach: | They propose a generative model that explores local and global context for joint learning topics and topic-specific word embeddings. |
| Outcome: | The proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
LLM-Guided Semantic-Aware Clustering for Topic Modeling (2025.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that topic modeling is competitive compared to closed-source methods. |
| Approach: | They propose a semi-supervised topic modeling method that combines LLMs with clustering to improve topic generation and distribution. |
| Outcome: | The proposed method outperforms state-of-the-art methods that utilize GPT-4 on topic alignment and exhibits competitive performance compared to Neural Topic Models on topic quality. |
Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to detect sentiment toward aspect categories ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance. |
| Approach: | They propose a multi-instance multi-label learning network for Aspect-Category sentiment analysis that treats sentences as bags, words as instances, and the words indicating an aspect category as key instances of the aspect category. |
| Outcome: | The proposed model is based on three public datasets showing that it performs well. |
A Query-Driven Topic Model (2021.findings-acl)
Copied to clipboard
| Challenge: | Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. |
| Approach: | They propose a query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics. |
| Outcome: | The proposed model is particularly attractive when the query has a low occurrence in a text corpus, making it difficult for traditional topic models to identify relevant topics. |
Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for aspect-based sentiment analysis of review text use only a few keywords describing each aspect/sentiment without using any labeled examples. |
| Approach: | They propose a weakly-supervised approach for aspect-based sentiment analysis which uses only a few keywords describing each aspect/sentiment without using any labeled examples. |
| Outcome: | The proposed method generates quality joint topics and outperforms baselines significantly on benchmark datasets. |
TAN-NTM: Topic Attention Networks for Neural Topic Modeling (2021.acl-long)
Copied to clipboard
| Challenge: | Topic models have been widely used to learn text representations and gain insight into document corpora. |
| Approach: | They propose a framework which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. |
| Outcome: | The proposed model improves on two downstream tasks: document classification and topic guided keyphrase generation. |
Topic Modeling: Contextual Token Embeddings Are All You Need (2024.findings-emnlp)
Copied to clipboard
| 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. |
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work has used pre-trained word embeddings to address data sparsity in short-text or small document collections. |
| Approach: | They propose a neural topic modeling framework using multi-view embedding spaces to improve topic quality and deal with polysemy. |
| Outcome: | The proposed framework improves topic quality and deal with polysemy. |
BART-TL: Weakly-Supervised Topic Label Generation (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for topic labeling use weak labelers to train rankers . recent studies show that weakly-supervised methods can produce meaningful labels . |
| Approach: | They propose a weakly-supervised method for assigning topic labels to models by using weak labelers. |
| Outcome: | The proposed model can generate valuable and novel labels in a weakly-supervised manner and can be improved by adding other weak labelers or distant supervision on similar tasks. |
Label-Specific Document Representation for Multi-Label Text Classification (D19-1)
Copied to clipboard
| Challenge: | Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels. |
| Approach: | They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four datasets . it can predict low-frequency labels, and it can be used in sentimental analysis . |