Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification
Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words(GoW) model in which each document is represented by a graph that encodes relationships between the different terms. Based on this formulation, the importance of a term is determined by weighting the corresponding node in the document, collection and label graphs, using node centrality criteria. We also introduce novel graph-based weighting schemes by enriching graphs with word-embedding similarities, in order to reward or penalize semantic relationships. Our methods produce more discriminative feature weights for text categorization, outperforming existing frequency-based criteria.
Code (1)
Tasks
General ClassificationSentiment AnalysisText Categorizationtext-classificationText ClassificationWord EmbeddingsSimilar Papers 제목 키워드 기반
A Neural Autoregressive Topic Model
We describe a new model for learning meaningful representations of text documents from an unlabeled collection of documents. This model is inspired by the recently proposed Replicated Softmax, an undirected graphical mod…
modelRepresentation LearningTriTopic: Tri-Modal Graph-Based Topic Modeling with Iterative Refinement and Archetypes
Topic modeling extracts latent themes from large text collections, but leading approaches like BERTopic face critical limitations: stochastic instability, loss of lexical precision ("Embedding Blur"), and reliance on a s…
Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering
Answering time-sensitive questions from long documents requires temporal reasoning over the times in questions and documents. An important open question is whether large language models can perform such reasoning solely …
Question AnsweringTemporal Information ExtractionFrom Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction
Confusing charge prediction is a challenging task in legal AI, which involves predicting confusing charges based on fact descriptions. While existing charge prediction methods have shown impressive performance, they face…
Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Topic Labels
Probabilistic topic models are important tools for indexing, summarizing, and analyzing large document collections by their themes. However, promoting end-user understanding of topics remains an open research problem. We…
Topic Models