Unifying context with labeled property graph: A pipeline-based system for comprehensive text representation in NLP

Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed approach introduces specialized patterns and algorithms to enhance specific NLP tasks, including nominal mention detection, named entity disambiguation, event enrichments, event participant detection, and temporal link detection. The evaluation of the proposed approach, using the MEANTIME corpus comprising manually annotated documents, provides encouraging results and valuable insights into the system\u27s strengths. The proposed pipeline-based framework serves as a solid foundation for future research, aiming to refine and optimize LPG-based graph structures to generate comprehensive and semantically rich text representations, addressing the challenges associated with efficient information extraction and analysis in NLP

A Semantic-Based Framework for Summarization and Page Segmentation in Web Mining

This chapter addresses two crucial issues that arise when one applies Web-mining techniques for extracting relevant information. The first one is the acquisition of useful knowledge from textual data; the second issue stems from the fact that a web page often proposes a considerable amount of \u2018noise\u2019 with respect to the sections that are truly informative for the user's purposes. The novelty contribution of this work lies in a framework that can tackle both these tasks at the same time, supporting text summarization and page segmentation. The approach achieves this goal by exploiting semantic networks to map natural language into an abstract representation, which eventually supports the identification of the topics addressed in a text source. A heuristic algorithm uses the abstract representation to highlight the relevant segments of text in the original document. The verification of the approach effectiveness involved a publicly available benchmark, the DUC 2002 dataset, and satisfactory results confirmed the method effectiveness

The role of statistical and semantic features in single-document extractive summarization

This paper reports on the further results of the ongoing research analyzing the impact of a range of commonly used statistical and semantic features in the context of extractive text summarization. The features experimented with include word frequency, inverse sentence and term frequencies, stopwords filtering, word senses, resolved anaphora and textual entailment. The obtained results demonstrate the relative importance of each feature and the limitations of the tools available. It has been shown that the inverse sentence frequency combined with the term frequency yields almost the same results as the latter combined with stopwords filtering that in its turn proved to be a highly competitive baseline. To improve the suboptimal results of anaphora resolution, the system was extended with the second anaphora resolution module. The present paper also describes the first attempts of the internal document data representation

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure