Construction of a Syntactic Analysis Map for Yi Shui School through Text Mining and Natural Language Processing Research

• URL: http://arxiv.org/abs/2402.10743v1
• Date: Fri, 16 Feb 2024 14:59:55 GMT
• Title: Construction of a Syntactic Analysis Map for Yi Shui School through Text Mining and Natural Language Processing Research
• Authors: Hanqing Zhao and Yuehan Li
• Abstract summary: This study constructs a word segmentation and entity relationship extraction model based on conditional random fields. The dependency network is used to analyze the grammatical relationship between entities in each ancient book article.
• Score: 5.015294834550435
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Entity and relationship extraction is a crucial component in natural language processing tasks such as knowledge graph construction, question answering system design, and semantic analysis. Most of the information of the Yishui school of traditional Chinese Medicine (TCM) is stored in the form of unstructured classical Chinese text. The key information extraction of TCM texts plays an important role in mining and studying the academic schools of TCM. In order to solve these problems efficiently using artificial intelligence methods, this study constructs a word segmentation and entity relationship extraction model based on conditional random fields under the framework of natural language processing technology to identify and extract the entity relationship of traditional Chinese medicine texts, and uses the common weighting technology of TF-IDF information retrieval and data mining to extract important key entity information in different ancient books. The dependency syntactic parser based on neural network is used to analyze the grammatical relationship between entities in each ancient book article, and it is represented as a tree structure visualization, which lays the foundation for the next construction of the knowledge graph of Yishui school and the use of artificial intelligence methods to carry out the research of TCM academic schools.

Related papers

• Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema [60.42231674887294]
  We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base. We ground generation of KG with the authored ontology based on extracted relations to ensure consistency and interpretability. Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs.
  arXiv  Detail & Related papers  (2024-12-30T13:36:05Z)
• Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature [5.773921786449337]
  Section identification is an important task for library science, especially knowledge management. We study the paper section identification problem in the context of Chinese medical literature analysis.
  arXiv  Detail & Related papers  (2024-12-15T09:11:14Z)
• Building Tamil Treebanks [0.0]
  Treebanks are important linguistic resources, which are structured and annotated corpora with rich linguistic annotations. This paper discusses the creation of Tamil treebanks using three distinct approaches: manual annotation, computational grammars, and machine learning techniques.
  arXiv  Detail & Related papers  (2024-09-23T01:58:50Z)
• Finding structure in logographic writing with library learning [55.63800121311418]
  We develop a computational framework for discovering structure in a writing system. Our framework discovers known linguistic structures in the Chinese writing system. We demonstrate how a library learning approach may help reveal the fundamental computational principles that underlie the creation of structures in human cognition.
  arXiv  Detail & Related papers  (2024-05-11T04:23:53Z)
• Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic [51.967603572656266]
  We introduce a consistent and theoretically grounded approach to annotating decompositional entailment. We find that our new dataset, RDTE, has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality.
  arXiv  Detail & Related papers  (2024-02-22T18:55:17Z)
• Semantic Similarity Measure of Natural Language Text through Machine Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study Using UCGIS GIS&T Body of Knowledge [2.4909170697740968]
  GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics. This research evaluates the effectiveness of multiple natural language processing (NLP) techniques in extracting semantics from text. It also offers a new perspective on the use of machine learning techniques for analyzing scientific publications.
  arXiv  Detail & Related papers  (2023-05-17T01:17:57Z)
• A Survey of Text Representation Methods and Their Genealogy [0.0]
  In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. We provide a survey of current approaches, by arranging them in a genealogy, and by conceptualizing a taxonomy of text representation methods to examine and explain the state-of-the-art.
  arXiv  Detail & Related papers  (2022-11-26T15:22:01Z)
• Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis [96.53859361560505]
  We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information. KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based. Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
  arXiv  Detail & Related papers  (2022-01-13T08:25:53Z)
• Latin writing styles analysis with Machine Learning: New approach to old questions [0.0]
  In the Middle Ages texts were learned by heart and spread using oral means of communication from generation to generation. Taking into account such a specific construction of literature composed in Latin, we can search for and indicate the probability patterns of familiar sources of specific narrative texts.
  arXiv  Detail & Related papers  (2021-09-01T20:21:45Z)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [64.22926988297685]
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
  arXiv  Detail & Related papers  (2019-10-23T17:37:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.