25/07/2020

Automatic generation of topic labels

Areej Alokaili, Nikolaos Aletras, Mark Stevenson

Keywords: topic modeling, topic representation, neural network

Abstract Paper Similar Papers

Abstract: Topic modelling is a popular unsupervised method for identifying the underlying themes in document collections that has many applications in information retrieval. A topic is usually represented by a list of terms ranked by their probability but, since these can be difficult to interpret, various approaches have been developed to assign descriptive labels to topics. Previous work on the automatic assignment of labels to topics has relied on a two-stage approach: (1) candidate labels are retrieved from a large pool (e.g. Wikipedia article titles); and then (2) re-ranked based on their semantic similarity to the topic terms. However, these extractive approaches can only assign candidate labels from a restricted set that may not include any suitable ones. This paper proposes using a sequence-to-sequence neural-based approach to generate labels that does not suffer from this limitation. The model is trained over a new large synthetic dataset created using distant supervision. The method is evaluated by comparing the labels it generates to ones rated by humans.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3397271.3401185#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at SIGIR 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 13:29 
 0
 0
 0
 1
 9:57 
 0
 0
 0
 0
 14:08 
 0
 0
 0
 0
 14:26 
 0
 0
 0
 0
 12:39 
 0
 0
 0
 0
 6:33 
 0
 0
 0
 0
 5:14 
 0
 0
 0
 0
 9:53 
 0
 0
 0
 0
 15:06 
 0
 0
 0
 0
 6:29 
 0
 0
 0
 0
 3:46 
 0
 0
 0
 0
 14:31 
 0
 0
 0
 0
 15:46 
 0
 0
 0
 0
 14:13 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 1
 5:00 
 0
 0
 0
 0
 16:45 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 13:14 
 0
 0
 0
 0
 3:19 
 0
 0
 0
 0
 3:25 
 0
 0
 0
 0
 3:18 
 0
 0
 0
 0
 15:09 
 0
 0
 0
 0
 11:40 
 0
 0
 0
 0
 15:30 
 0
 0
 0
 0
 10:38 
 0
 0
 0
 0
 3:19 
 0
 0
 0
 0
 5:14 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 14:37 
 0
 0
 0
 0
 12:23 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 11:23 
 0
 0
 0
 0
 18:50 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 4:52 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 14:02 
 0
 0
 0
 0
 12:46