scholarly journals Relationship Classification based on Dependency Parsing and Pre-training Model

2021 ◽  
Author(s):  
Baosheng Yin ◽  
Yifei Sun
Keyword(s):  
Information Extraction ◽  
Extraction Method ◽  
Training Model ◽  
Context Information ◽  
Dependency Parsing ◽  
Improve Performance ◽  
Natural Language Text ◽  
Relationship Extraction ◽  
Entity Relationship ◽  
Language Text

Abstract As an important part of information extraction, relationship extraction aims to extract the relationships between given entities from natural language text. On the basis of the pre-training model R-BERT, this paper proposes an entity relationship extraction method that integrates entity dependency path and pre-training model, which generates a dependency parse tree by dependency parsing, obtains the dependency path of entity pair via a given entity, and uses entity dependency path to exclude such information as modifier chunks and useless entities in sentences. This model has achieved good F1 value performance on the SemEval2010 Task 8 dataset. Experiments on dataset show that dependency parsing can provide context information for models and improve performance.

Automated Extraction of Function Knowledge From Text

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Hyunmin Cheong ◽  
Wei Li ◽  
Adrian Cheung ◽  
Andy Nogueira ◽  
Francesco Iorio
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Energy Flow ◽  
Extraction Method ◽  
Current Work ◽  
Automated Extraction ◽  
Natural Language Text ◽  
Functional Basis ◽  
Knowledge Repositories ◽  
Language Text

This paper presents a method to automatically extract function knowledge from natural language text. The extraction method uses syntactic rules to acquire subject-verb-object (SVO) triplets from parsed text. Then, the functional basis taxonomy, WordNet, and word2vec are utilized to classify the triplets as artifact-function-energy flow knowledge. For evaluation, the function definitions associated with 30 most frequent artifacts compiled in a human-constructed knowledge base, Oregon State University's design repository (DR), were compared to the definitions identified by extraction the method from 4953 Wikipedia pages classified under the category “Machines.” The method found function definitions for 66% of the test artifacts. For those artifacts found, 50% of the function definitions identified were compiled in the DR. In addition, 75% of the most frequent function definitions found by the method were also defined in the DR. The results demonstrate the potential of the current work in enabling automated construction of function knowledge repositories.

scholarly journals Sentiment Analysis Using Common-Sense and Context Information

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Basant Agarwal ◽  
Namita Mittal ◽  
Pooja Bansal ◽  
Sonal Garg
Keyword(s):  
Sentiment Analysis ◽  
Common Sense ◽  
Research Community ◽  
Context Information ◽  
Analysis Model ◽  
Natural Language Text ◽  
Domain Specific ◽  
Wide Range ◽  
Social Applications ◽  
Language Text

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.

scholarly journals Novel rules for extracting the entities of entity relationship models

2021 ◽  
Vol 20 (2) ◽  
pp. 29-35
Author(s):  
Mussa Omar ◽  
Abdulrhman Alsheky ◽  
Balha Faiz
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
Performance Comparison ◽  
Dramatic Improvement ◽  
Natural Language Text ◽  
Learning Rules ◽  
Linguistic Rules ◽  
Entity Relationship ◽  
Entity Relationships ◽  
Language Text

Extracting entities from natural language text to design conceptual models of the entity relationships is not trivial and novice designers and students can find it especially difficult. Researchers have suggested linguistic rules/guidelines for extracting entities from natural language text. Unfortunately, while these guidelines are often correct they can, also, be invalid. There is no rule that is true at all times. This paper suggests novel rules based on the machine learning classifiers, the RIPPER, the PART and the decision trees. Performance comparison was made between the linguistic and the machine learning rules. The results shows that there was a dramatic improvement when machine learning rules were used.

scholarly journals A Compression-Based Toolkit for Modelling and Processing Natural Language Text

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 294 ◽  
Author(s):  
William Teahan
Keyword(s):  
Natural Language ◽  
Model Building ◽  
Channel Model ◽  
Text Processing ◽  
Training Model ◽  
Second Phase ◽  
Two Phase ◽  
Natural Language Text ◽  
Language Text

A novel compression-based toolkit for modelling and processing natural language text is described. The design of the toolkit adopts an encoding perspective—applications are considered to be problems in searching for the best encoding of different transformations of the source text into the target text. This paper describes a two phase `noiseless channel model’ architecture that underpins the toolkit which models the text processing as a lossless communication down a noise-free channel. The transformation and encoding that is performed in the first phase must be both lossless and reversible. The role of the verification and decoding second phase is to verify the correctness of the communication of the target text that is produced by the application. This paper argues that this encoding approach has several advantages over the decoding approach of the standard noisy channel model. The concepts abstracted by the toolkit’s design are explained together with details of the library calls. The pseudo-code for a number of algorithms is also described for the applications that the toolkit implements including encoding, decoding, classification, training (model building), parallel sentence alignment, word segmentation and language segmentation. Some experimental results, implementation details, memory usage and execution speeds are also discussed for these applications.

scholarly journals A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chengyao Lv ◽  
Deng Pan ◽  
Yaxiong Li ◽  
Jianxin Li ◽  
Zong Wang
Keyword(s):  
Markov Model ◽  
Maximum Entropy ◽  
Extraction Method ◽  
Semantic Analysis ◽  
Short Term Memory ◽  
Knowledge Bases ◽  
Relationship Extraction ◽  
Intelligent Information ◽  
Entity Relationship ◽  
The Relationship

To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.

scholarly journals Multi-language Information Extraction with Text Pattern Recognition

2021 ◽  
Author(s):  
Johannes Lindén ◽  
Tingting Zhang ◽  
Stefan Forsström ◽  
Patrik Österberg
Keyword(s):  
Information Extraction ◽  
News Media ◽  
State Of The Art ◽  
Meta Data ◽  
Natural Language Text ◽  
New Information ◽  
Extraction Algorithm ◽  
Knowledge Graphs ◽  
Multiple Languages ◽  
Language Text

Information extraction is a task that can extract meta-data information from text. The research in this article proposes a new information extraction algorithm called GenerateIE. The proposed algorithm identifies pairs of entities and relations described in a piece of text. The extracted meta-data is useful in many areas, but within this research the focus is to use them in news-media contexts to provide the gist of the written articles for analytics and paraphrasing of news information. GenerateIE algorithm is compared with existing state of the art algorithms with two benefits. Firstly, the GenerateIE provides the co-referenced word as the entity instead of using he, she, it, etc. which is more beneficial for knowledge graphs. Secondly GenerateIE can be applied on multiple languages without changing the algorithm itself apart from the underlying natural language text-parsing. Furthermore, the performance of GenerateIE compared with state-of-the-art algorithms is not significantly better, but it offers competitive results.

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