Labeling Topics with Images using Neural Networks
Topics generated by topic models are usually represented by lists of $t$ terms or alternatively using short phrases and images. The current state-of-the-art work on labeling topics using images selects images by re-ranking a small set of candidates for a given topic. In this paper, we present a more generic method that can estimate the degree of association between any arbitrary pair of an unseen topic and image using a deep neural network. Our method has better runtime performance $O(n)$ compared to $O(n^2)$ for the current state-of-the-art method, and is also significantly more accurate.
Code (0)
등록된 구현이 없습니다.
Tasks
Re-RankingTopic ModelsSimilar Papers 제목 키워드 기반
Transfer Topic Labeling with Domain-Specific Knowledge Base: An Analysis of UK House of Commons Speeches 1935-2014
Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models use unsupervised methods and hence require the additiona…
Topic ModelsA Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing
Recently, topic modeling has been widely applied in data mining due to its powerful ability. A common, major challenge in applying such topic models to other tasks is to accurately interpret the meaning of each topic. To…
ChunkingTopic ModelsA Dynamic Topic Identification and Labeling Approach of COVID-19 Tweets
This paper formulates the problem of dynamically identifying key topics with proper labels from COVID-19 Tweets to provide an overview of wider public opinion. Nowadays, social media is one of the best ways to connect pe…
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 ModelsAn Online Topic Modeling Framework with Topics Automatically Labeled
In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic. Th…