Relationship extraction methods based on co-occurrence in web pages and files

Song, Qiang; Watanabe, Yousuke; Yokota, Haruo

doi:10.1145/2095536.2095552

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…There are generally three approaches of relationship extraction: (1) co-occurrence based, (2) machine-learning based [42], [43] and (3) rule-based. The co-occurrencebased approach [44], [45] is built on the assumption that entities occurring frequently together within a document and have higher chances of being related [43]. However, this is often not the case, and the precision of relation extraction is low [43].…”

Section: Lexical Syntactic Pattern For Relation Extractionmentioning

confidence: 99%

A Novel Patent Knowledge Extraction Method for Innovative Design

et al. 2023

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As an important source of inspiration, the great number of patent documents provides designers with valuable knowledge of design rationale (DR), including issues, intent, pros and cons of the solutions. Researchers have carried out a number of data analysis studies based on patent information, which is now a new discipline called Patinformatics, including the analysis of patent information from a macro perspective and the identification and extraction of patent knowledge from a micro perspective. If DR knowledge could be extracted automatically from the patent documents and provided to designers as a source of inspiration, it would greatly promote innovative design, and at the same time promote the reuse of patent documents and the wide application of DR theory, which can be like killing three birds with one stone. To address this issue, this study proposes an improved lexical-syntactic pattern method for DR centric patent knowledge extraction, including DR Vector Space model (DRVS), DRV Trigger Word (DRV-TW), Design Rationale Vector (DRV), DR credibility (DRC) and others, and DRV based knowledge extraction algorithms. Knowledge extraction experiments were conducted on 1491 patent documents to verify the feasibility and performance of the method. In addition, two other sets of comparative experiments were conducted using the FastText and BERT machine learning methods, and the results further confirmed the reliability of the proposed method for lowresource corpus.INDEX TERMS Patent analysis, design rationale, knowledge extraction, design knowledge network.

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Section: Lexical Syntactic Pattern For Relation Extractionmentioning

confidence: 99%

A Novel Patent Knowledge Extraction Method for Innovative Design

et al. 2023

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“…cooccurrence based methods due to their simplicity and flexibility are most widely used methods in biomedical and clinical domain. In co-occurrence analysis it is assumed that if two entities are coming together in many sentences, their must be a relation between them (Bunescu et al, 2006;Song et al, 2011). Quite obviously this method can not differentiate types of relations and suffers from low precision and recall.…”

Section: Introductionmentioning

confidence: 99%

Relation extraction from clinical texts using domain invariant convolutional neural network

Sahu¹,

Anand²,

Oruganty³

et al. 2016

Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject of many research efforts and shared challenges. Relation extraction is the process of detecting and classifying the semantic relation among entities in a given piece of texts. Existing models for this task in biomedical domain use either manually engineered features or kernel methods to create feature vector. These features are then fed to classifier for the prediction of the correct class. It turns out that the results of these methods are highly dependent on quality of user designed features and also suffer from curse of dimensionality. In this work we focus on extracting relations from clinical discharge summaries. Our main objective is to exploit the power of convolution neural network (CNN) to learn features automatically and thus reduce the dependency on manual feature engineering. We evaluate performance of the proposed model on i2b2-2010 clinical relation extraction challenge dataset. Our results indicate that convolution neural network can be a good model for relation exaction in clinical text without being dependent on expert's knowledge on defining quality features.

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“…The cooccurrence of term or their synonyms can be treated as an indicator of relationship between to artifacts. [5] Co-reference Resolution -Co-reference resolution plays a very important role in information extractions. It is the process of marking up two expressions with the same entity.…”

Section: Introductionmentioning

confidence: 99%

Domain Independent Natural Language Processing -- A Case Study for Hospital Readmission with COPD

Agarwal

Behara

Mulpura

et al. 2014

2014 IEEE International Conference on Bioinformatics and Bioengineering

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Natural language processing is a field of computer science, which focuses on interactions between computers and human (natural) languages. The human languages are ambiguous unlike Computer languages, which make its analysis and processing difficult. Most of the data present these days is in unstructured form (such as: Accident reports, Patient discharge summary, Criminal records etc), which makes it hard for computers to understand for further use and analysis. This unstructured text needs to be converted into structured form by clearly defining the sentence boundaries, word boundaries and context dependent character boundaries for further analysis. This paper proposes a component-based domain-independent text analysis system for processing of the natural language known as Domain-independent Natural Language Processing System (DINLP). Further the paper discusses the system capability and its application in the area of bioinformatics through the case study

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Relationship extraction methods based on co-occurrence in web pages and files

Cited by 5 publications

References 8 publications

A Novel Patent Knowledge Extraction Method for Innovative Design

A Novel Patent Knowledge Extraction Method for Innovative Design

Relation extraction from clinical texts using domain invariant convolutional neural network

Domain Independent Natural Language Processing -- A Case Study for Hospital Readmission with COPD

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