Comparison and Analysis of Embedding Methods for Patent Documents

Patent text mining is an important task that requires domain knowledge. The patent text is sometimes not clear and contains many ambiguous and technical words. Traditional text mining approaches are not satisfactory enough for patent text mining. In this paper, we consider various embedding techniqu...

Full description

Saved in:
Bibliographic Details
Main Authors: Roudsari, Arousha Haghighian, Afshar, Jafar, Lee, Suan, Lee, Wookey
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Computational modeling
Conferences
Context modeling
contextualized word embedding
patent analysis
static word embedding
Task analysis
text classification
Text mining
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Patent text mining is an important task that requires domain knowledge. The patent text is sometimes not clear and contains many ambiguous and technical words. Traditional text mining approaches are not satisfactory enough for patent text mining. In this paper, we consider various embedding techniques for patent documents and try to find how to represent the patent text for other downstream tasks such as patent classification, patent recommendation, finding similar patents, knowledge mining, etc. We compare several embedding approaches with the patent classification task. The experimental results demonstrate that using contextual word embeddings can perform better than the conventional static word embedding approaches.
ISSN:2375-9356
DOI:10.1109/BigComp51126.2021.00037