DeepAI AI Chat
Log In Sign Up

Auto-tagging of Short Conversational Sentences using Transformer Methods

06/03/2021
by   D. Emre Taşar, et al.
0

The problem of categorizing short speech sentences according to their semantic features with high accuracy is a subject studied in natural language processing. In this study, a data set created with samples classified in 46 different categories was used. Examples consist of sentences taken from chat conversations between a company's customer representatives and the company's website visitors. The primary purpose is to automatically tag questions and requests from visitors in the most accurate way for 46 predetermined categories for use in a chat application to generate meaningful answers to the questions asked by the website visitors. For this, different BERT models and one GPT-2 model, pre-trained in Turkish, were preferred. The classification performances of the relevant models were analyzed in detail and reported accordingly.

READ FULL TEXT

page 1

page 6

06/09/2021

Auto-tagging of Short Conversational Sentences using Natural Language Processing Methods

In this study, we aim to find a method to auto-tag sentences specific to...
09/13/2022

Automated classification for open-ended questions with BERT

Manual coding of text data from open-ended questions into different cate...
09/20/2020

Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging

Ezafe is a grammatical particle in some Iranian languages that links two...
04/02/2022

BERT-Assisted Semantic Annotation Correction for Emotion-Related Questions

Annotated data have traditionally been used to provide the input for tra...
10/05/2018

From direct tagging to Tagging with sentences compression

In essence, the two tagging methods (direct tagging and tagging with sen...
06/03/2022

Extracting Similar Questions From Naturally-occurring Business Conversations

Pre-trained contextualized embedding models such as BERT are a standard ...
11/10/2022

Decomposing the Fundamentals of Creepy Stories

Fear is a universal concept; people crave it in urban legends, scary mov...