Syntactic analysis of natural language sentences based on rewriting systems and adaptivity

Cereda, Paulo Roberto Massa; Miura, Newton Kiyotaka; Neto, João José

doi:10.1016/j.procs.2018.04.164

Cited by 6 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In NLP, Syntactic Analysis is based on the grammatical aspect of the language and helps to figure out the alignment of natural language with grammatical dogmas [ 34 ]. Certain techniques can be used to apply these grammatical rules to the words and infer their meaning [ 35 ]. Semantic Analysis is based on the meaning that is conveyed by the text.…”

Section: Methodsmentioning

confidence: 99%

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gupta

Wu²,

Arora

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and is provided by the Memorial Sloan Kettering Cancer Center (MSKCC). The world-class researchers and oncologists contribute the dataset. Three text transformation models, namely, CountVectorizer, TfidfVectorizer, and Word2Vec, are utilized for the conversion of text to a matrix of token counts. Three machine learning classification models, namely, Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), along with the Recurrent Neural Network (RNN) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. The accuracy score of all the proposed classifiers is evaluated by using the confusion matrix. Finally, the empirical results show that the RNN model of deep learning has performed better than other proposed classifiers with the highest accuracy of 70%.

show abstract

Section: Methodsmentioning

confidence: 99%

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gupta

Wu²,

Arora

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…The meaning of a sentence for any language can therefore only be established if that sentence is correctly constructed following the syntactic rules of the language. This is why syntactic analysis is very fundamental to other NLP theories as well as NLP technologies [1,2]. Syntactic analysis forms the framework for developing parser technologies for various languages.…”

Section: Introductionmentioning

confidence: 99%

Ga-RBP: A Rule-based Parser for the Syntactic Analysis of the Ga Language of Ghana

Otto,

Nakpih

2024

Asian J. Res. Com. Sci.

View full text Add to dashboard Cite

This research presents a Parts of Speech (POS) corpus and a static rule-based technique, which we refer to as Ga Rule-based Parser (Ga-RBP), for the syntactic analysis and the parsing of sentences for the Ga language of Ghana. The technique is developed to parse sentences by utilising the POS tagged corpus; the corpus was developed by manually tagging the Ga words with their corresponding POS tags following a standard Ga-English dictionary and custom Tagset for the language. The syntax rules were computationally defined using production rules, which establish how a word should follow the other in the right sequence to form a correct grammatical statement based on their POS. The model generally analyses the sentence structure of the language to assert its syntactic state for correctness or otherwise.

show abstract

Classification of clinically actionable genetic mutations in cancer patients using machine learning-based precision diagnostics

Thakare¹,

Chaudhari²,

Baradkar³

2023

Nanotechnology Principles in Drug Targeting and Diagnosis

View full text Add to dashboard Cite

Syntactic analysis of natural language sentences based on rewriting systems and adaptivity

Cited by 6 publications

References 9 publications

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection

Ga-RBP: A Rule-based Parser for the Syntactic Analysis of the Ga Language of Ghana

Classification of clinically actionable genetic mutations in cancer patients using machine learning-based precision diagnostics

Contact Info

Product

Resources

About