Hierarchical Clustering for Keyphrase Extraction from Arabic Documents Based on Word Context

2011 ◽  
pp. CSP6
Author(s):  
Rehab Duwairi
Keyword(s):  
Hierarchical Clustering ◽  
Keyphrase Extraction ◽  
Word Context

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

The fāṣila in the Qur'an: Word, Context and Meaning

1999 ◽  
Vol 1 (1) ◽  
pp. 264-269 ◽  
Author(s):  
Ahmad Mukhtar Omar
Keyword(s):  
Rhythmic Structure ◽  
Word Context ◽  
The Subject ◽  
Apparent Similarity ◽  
Narrative Account

Fāsila is a termed used to denote the last word in each Qur'anic āya. In this article, we explore this Qur'anic usage, examining in particular the connection between the choice of word, its semantic and rhythmic role in its immediate context, and its wider signification in the narrative. Previous writers on the subject drew attention to the apparent similarity between the fāṣila and the rhythmic schemes of poetry and rhyming prose. We argue that tire fāṣila, while certainly playing a role in the rhythmic structure of the text, has a wider significance, and that an examination of each occurrence underlines the organic connection between the ‘content’ of each sentence and its fāṣila. In a number of instances, it can be shown that the fāṣila and the rhythmic and semantic demands of the narrative account for differences between standard usage and the Qur'anic text. We discuss a number of specific instances of fāṣila, and, examine these in the light of the views of classical exegetes on this feature of the Qur'an.

IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Alifia Puspaningrum ◽  
Nahya Nur ◽  
Ozzy Secio Riza ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Hierarchical Clustering ◽  
Gabor Filter ◽  
Hierarchical Cluster ◽  
Experimental Result ◽  
Main Process ◽  
Percentile Method ◽  
Cluster Analysis Method ◽  
2D Gabor Filter

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

HCVS: Pinpointing Chromatin States Through Hierarchical Clustering and Visualization Scheme

2019 ◽  
Vol 14 (2) ◽  
pp. 148-156
Author(s):  
Nighat Noureen ◽  
Sahar Fazal ◽  
Muhammad Abdul Qadir ◽  
Muhammad Tanvir Afzal
Keyword(s):  
Hierarchical Clustering ◽  
Real Data ◽  
Cell Types ◽  
Computational Scheme ◽  
Data Set ◽  
Chromatin States ◽  
Functional Regions ◽  
Visualization Strategy ◽  
Hidden States ◽  
Next Generation Sequencing Ngs

Background: Specific combinations of Histone Modifications (HMs) contributing towards histone code hypothesis lead to various biological functions. HMs combinations have been utilized by various studies to divide the genome into different regions. These study regions have been classified as chromatin states. Mostly Hidden Markov Model (HMM) based techniques have been utilized for this purpose. In case of chromatin studies, data from Next Generation Sequencing (NGS) platforms is being used. Chromatin states based on histone modification combinatorics are annotated by mapping them to functional regions of the genome. The number of states being predicted so far by the HMM tools have been justified biologically till now. Objective: The present study aimed at providing a computational scheme to identify the underlying hidden states in the data under consideration. </P><P> Methods: We proposed a computational scheme HCVS based on hierarchical clustering and visualization strategy in order to achieve the objective of study. Results: We tested our proposed scheme on a real data set of nine cell types comprising of nine chromatin marks. The approach successfully identified the state numbers for various possibilities. The results have been compared with one of the existing models as well which showed quite good correlation. Conclusion: The HCVS model not only helps in deciding the optimal state numbers for a particular data but it also justifies the results biologically thereby correlating the computational and biological aspects.

Effects of Context on Processing Emotionally Neutral Abstract and Concrete Concepts

2016 ◽  
Vol 44 (7) ◽  
pp. 1191-1200 ◽  
Author(s):  
Liusheng Wang ◽  
Hongmei Qiu ◽  
Jianjun Yin
Keyword(s):  
College Students ◽  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional Valence ◽  
Decision Task ◽  
Abstract Concepts ◽  
Context Condition ◽  
Word Context ◽  
Concrete Concepts ◽  
Concrete Words

The abstractness effect describes the phenomenon of individuals processing abstract concepts faster and more accurately than they process concrete concepts. In this study, we explored the effects of context on how 43 college students processed words, controlling for the emotional valence of the words. The participants performed a lexical decision task in which they were shown individual abstract and concrete words, or abstract and concrete words embedded in sentences. The results showed that in the word-context condition the participants' processing of concrete concepts improved, whereas in the sentence-context condition their processing of abstract concepts improved. These findings support the embodied cognition theory of concept processing.

scholarly journals Market Structure Analysis: Hierarchical Clustering of Products Based on Substitution-in-Use

1981 ◽  
Vol 45 (3) ◽  
pp. 38 ◽  
Author(s):  
Rajendra K. Srivastava ◽  
Robert P. Leone ◽  
Allan D. Shocker
Keyword(s):  
Market Structure ◽  
Structure Analysis ◽  
Hierarchical Clustering ◽  
Market Structure Analysis
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