A Framework for Adaptive Deep Reinforcement Semantic Parsing of Unstructured Data

Shubham Jain, Amy De Buitleir, Enda Fallon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Semantic parsing of telecommunications data from nodes is an important topic of research in network monitoring and diagnostics. This data contains insights to achieve higher data rates, low latency and reliability. The current machine learning algorithm relies heavily on iterative meta-heuristics tools for data mining. Such tools often fail to capture the consistency, and complementary information inadequately which results in loss of high-level semantic information that can be used to extract features inside unstructured text data. The features learned by the current Machine learning algorithm can perform classification of unstructured data with high accuracy. However, the same methods fail to extract entities from data with high variation in structure and uneven density of words. Manual steps to parse and extract relevant information have failed to provide higher efficiency in extracting useful information which delays the process of network analysis. In this paper, we present an Adaptive Deep Reinforcement (ADR) framework fused with a Deep Q Network (DQN) for feature extraction and LSTM to extract relational dependency of words in unstructured data. A novel dynamic optimizer component based on LSTM and fully connected layers is adopted for dynamic state reformulation and Q values greatly enhance the parsing accuracy by extracting robust features to measure variation in data and identifying relationships between entities in raw data to parse them to a structured format. We compare ADR with state-of-the-art Drain parser. The complexity comparison of parsed files with state-of-the-art and ADR demonstrates the effectiveness of the proposed framework for semantic parsing of unstructured data.

Original languageEnglish
Title of host publicationICTC 2021 - 12th International Conference on ICT Convergence
Subtitle of host publicationBeyond the Pandemic Era with ICT Convergence Innovation
PublisherIEEE Computer Society
Pages1055-1060
Number of pages6
ISBN (Electronic)9781665423830
ISBN (Print)9781665423830
DOIs
Publication statusPublished - 2021
Event12th International Conference on Information and Communication Technology Convergence, ICTC 2021 - Jeju Island, Korea, Republic of
Duration: 20 Oct 202122 Oct 2021

Publication series

NameInternational Conference on ICT Convergence
Volume2021-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference12th International Conference on Information and Communication Technology Convergence, ICTC 2021
Country/TerritoryKorea, Republic of
CityJeju Island
Period20/10/2122/10/21

Keywords

  • Deep Learning
  • Reinforcement Learning
  • Semantic Data Extraction

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