Extracting phrase-content pairs for Turkish sentences

Dönmez, İlknur; Adalı, Eşref

doi:10.1109/icaict.2015.7338531

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In the work of Molla et al [5] one thing can increase the accuracy of the retrieved summaries that is the removal of ambiguity from the input sentence/query. In [7] Dönmez et al formed a phrase-content finder system for the Turkish sentences. They have done this study by underlying the importance of subject, verb and object relation with actionable things.…”

Section: Related Workmentioning

confidence: 99%

Developing Disease Classification System based on Keyword Extraction and Supervised Learning

Suffian¹,

Khan²,

Wasi³

2018

ijacsa

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The Evidence-Based Medicine (EBM) is emerged as the helpful practice for medical practitioners to make decisions with available shreds of evidence along with their professional expertise. In EBM, the medical practitioners suggest the medication on the basis of underlying information of patients descriptions and medical records (mostly available in textual form). This paper presents a novel and efficient method for predicting the correct disease. Since these type of tasks are generally accounted as the multi-class classifying problem, therefore, a large number of records are needed, so a large number of records will be entertained in higher n-dimensional space. Our system, as proposed in this paper, will utilise the key-phrases extraction techniques to scoop out the meaningful information to reduce the size of textual dimension, and, the suite of machine learning algorithms for classifying the diseases efficiently. We have tested the proposed approach on 6 different diseases i.e. Asthma, Hypertension, Diabetes, Fever, Abdominal issues, and Heart problems over the dataset of 690 patients. With key-phrases tested in the range [3,7] features, SVM has shown the highest (93.34%, 95%) F1-score and accuracy.

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Section: Related Workmentioning

confidence: 99%

Developing Disease Classification System based on Keyword Extraction and Supervised Learning

Suffian¹,

Khan²,

Wasi³

2018

ijacsa

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“…Each phrase type has a role in the sentence. There are lots of studies related with Turkish phrase structure [25], [26], [27]. The phrases that are seen on Table 1 are used in this study.…”

Section: Phrase Structure With Suffixesmentioning

confidence: 99%

Contex Free Grammer For Turkish

Dönmez

Adalı

2018

SDÜ Fen Bil Enst Der

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Formal Grammar which is introduced by Chomsky is one of the most important development in Natural Language Processing, a branch of Artificial Intelligence. The mathematical reresentation of languages can be possible using Formal Grammars. Almost all natural languages have word classes such as noun, adjective, verb. In addition to this one sentence consist of noun phrase and verb phrase. Noun phrase may consist of location, destination and source elements. Despite many similarities between the languages, there exist important dissimilarities in grammar rules of the languages belonging to different language families. In our study the most appropriate formal grammar representing Turkish language is investigated. Accuracy of the suggested grammars' rules is evaluated in two different corpus. This study is the enhanced version of "Turkish Context Free Grammar Rules with Case Suffix and Phrase Relation" that was presented on UBMK 2016 International Conference on Computer Science & Engineering [1]. Different from the first study, this study includes all word and sentence types of Turkish. Adjectives and prepositions are considered. The quoted sentences, incomplete sentences and question sentences are included. The genitive phrase structures including verbal word are included. In this study, the noun phrases are also defined in detail.

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