End-to-End Information Extraction and Dynamic Topic Modeling Using Deep NLP Pipelines

Volume no :

6 |

Issue no :

1

Article Type :

Scholarly Article

Author :

Arul Selvan M

Published Date :

May, 2025

Publisher :

Journal of Science Technology and Research (JSTAR)

Page No: 1 - 12

Abstract : The rapid growth of unstructured textual data in domains such as news media, social networks, scientific literature, and enterprise documents has created an urgent need for automated methods that can efficiently extract relevant information and uncover underlying thematic structures over time. This paper presents a novel end-to-end framework that integrates advanced deep learning-based Natural Language Processing (NLP) techniques to perform robust information extraction (IE) combined with dynamic topic modeling. Our approach leverages state-of-the-art transformer architectures, such as BERT and its derivatives, to build a flexible pipeline that extracts entities, relations, events, and salient textual features, feeding directly into a dynamic topic modeling component that tracks evolving topics across temporal slices of data. The pipeline starts with pre-processing modules including tokenization, part-of-speech tagging, and syntactic parsing, followed by specialized neural models for named entity recognition (NER), relation extraction, and event detection. By employing contextualized embeddings, our system captures nuanced semantic representations that significantly improve the precision and recall of extracted information compared to traditional rule-based or shallow learning methods. The extracted entities and relations are then structured into a knowledge graph format, enabling downstream reasoning and facilitating interpretability. For topic modeling, we introduce a dynamic variant of neural topic models that incorporate temporal dependencies and allow topics to evolve continuously as new data arrives. Unlike classical latent Dirichlet allocation (LDA)-based methods, our model integrates deep contextual embeddings and recurrent mechanisms to model the temporal evolution of topic distributions and word-topic associations. This enables the system to adaptively capture emerging trends and fading interests within the corpus, providing richer and more actionable insights. We validate our framework on multiple large-scale real-world datasets including news articles, scientific publications, and social media streams. Experimental results demonstrate that our end-to-end pipeline significantly outperforms baseline methods in both extraction accuracy and topic coherence metrics. Furthermore, qualitative analysis reveals that the dynamic topic model effectively captures meaningful topic shifts that correspond to real-world events and trends, underscoring the practical utility of our approach for continuous monitoring and analysis. In summary, this work contributes a comprehensive, modular, and scalable deep NLP pipeline that unifies information extraction and dynamic topic modeling, addressing key challenges in processing large volumes of temporal text data. By combining the strengths of deep contextual embeddings, advanced sequence modeling, and probabilistic topic inference, our system offers a powerful tool for researchers and practitioners aiming to extract structured knowledge and track thematic evolution in diverse textual domains. Future work will focus on integrating multi-modal data, enhancing real-time processing capabilities, and extending interpretability features for broader adoption in industry applications.

Keyword: Information extraction, dynamic topic modeling, deep learning, natural language processing, transformer models, BERT, named entity recognition, relation extraction, event detection, knowledge graph, neural topic models, temporal text analysis, contextual embeddings, sequence modeling, topic evolution, text mining, unstructured data, deep NLP pipelines, trend detection, text analytics

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End-to-End Information Extraction

End-to-End Information Extraction In today’s data-driven world, the proliferation of unstructured textual data presents both
tremendous opportunities and significant challenges. Vast amounts of text are continuously
generated from diverse sources such as news media, social networks, scientific publications,
corporate reports, and government documents. Extracting meaningful insights from this ever-
growing textual landscape is critical for decision-making, knowledge discovery, and trend analysis
across many domains. Traditional manual methods of information analysis are not scalable,
prompting a surge in automated techniques for processing and understanding text. Among these,
information extraction (IE) and topic modeling stand out as essential tasks for transforming raw
text into structured knowledge and thematic summaries, respectively.

Related Works

Information extraction focuses on identifying and structuring key elements such as named
entities, relationships, and events within text. Accurate IE enables the construction of knowledge
graphs, facilitates question answering, and supports downstream applications like
recommendation systems and automated reasoning. However, conventional IE approaches
relying on handcrafted rules or shallow machine learning models often struggle with linguistic
complexity, ambiguity, and domain variability. The advent of deep learning, particularly
transformer-based models like BERT, has revolutionized NLP by providing contextualized word
representations that capture semantic nuances and syntactic dependencies more effectively.
This advancement has significantly improved the precision and recall of IE tasks, making it
possible to build robust pipelines that handle large-scale and heterogeneous datasets.

The introduction of deep learning revolutionized IE by enabling automatic feature extraction
from raw text. Collobert et al. (2011) pioneered neural architectures for sequence labeling,
followed by bidirectional LSTM models that captured long-range dependencies (Huang et al.,
2015). More recently, transformer-based models such as BERT (Devlin et al., 2019), RoBERTa (Liu
et al., 2019), and SpanBERT (Joshi et al., 2020) have set new state-of-the-art benchmarks in IE
tasks by leveraging contextualized embeddings and attention mechanisms. These models excel
at understanding subtle semantic and syntactic cues and have been adapted for joint entity and
relation extraction (Wang et al., 2020).
Event extraction, which identifies occurrences and their participants, has also benefited from
deep learning. Techniques include graph neural networks (GNNs) to model dependencies
between entities and events (Wadden et al., 2019) and multitask learning approaches to jointly
detect events and arguments (Liu et al., 2020). Despite these advances, most IE systems still treat
extraction as a static problem, lacking mechanisms to incorporate temporal context or handle
evolving language use.