Kluster Bag of Word Menggunakan Weka
نویسندگان
چکیده
منابع مشابه
Optimization of Word Sense Disambiguation Using Clustering in Weka
In the Natural Language Processing (NLP) community, Word Sense Disambiguation (WSD) has been described as the task which selects the appropriate meaning (sense) to a given word in a text or discourse where this meaning is distinguishable from other senses potentially attributable to that word. These senses could be seen as the target labels of a classification problem. Clustering and classifica...
متن کاملBag-of-word normalized n-gram models
The Bag-Of-Word (BOW) model uses a fixed length vector of word counts to represent text. Although the model disregards word sequence information, it has been shown to be successful in capturing long range word-word correlations and topic information. In contrast, n-gram models have been shown to be an effective way to capture short term dependencies by modeling text as a Markovian sequence. In ...
متن کاملDocument Recovery from Bag-of-Word Indices
Motivated by computer privacy issues, we present the novel problem of document recovery from an index: given only a document’s bag-of-words (BOW) vector or other type of index, reconstruct the original ordered document. We investigate a variety of index types, including count-based BOW vectors, stopwords-removed count BOW vectors, indicator BOW vectors, and bigram count vectors. We formulate th...
متن کاملArabic Handwritten Word Category Classification Using Bag of Features
Human writing is highly variable and inconsistent, and this makes the offline recognition of handwritten words extremely challenging. This paper describes a novel approach that can be employed for the offline recognition of handwritten Arabic words. Through conceptualizing each word as single, inseparable objects, the proposed approach aims to recognize words in accordance with their complete s...
متن کاملWord Spotting in Handwritten Arabic Documents Using Bag-Of-Descriptors
This paper presents a query-by-example word spotting in handwritten Arabic documents, based on Scale Invariant Feature Transform (SIFT), without using any text word or line segmentation approach, because any errors affect to the subsequent word representation. First the interest points are automatically extracted from the images using SIFT detector, then, we use SIFT descriptor to represent eac...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Jurnal Edukasi dan Penelitian Informatika (JEPIN)
سال: 2015
ISSN: 2548-9364,2460-0741
DOI: 10.26418/jp.v1i1.10145