Extracting Networks of Characters and Places from Written Works with CHAPLIN
We are proposing a tool able to gather information on social networks from narrative texts. Its name is CHAPLIN, CHAracters and PLaces Interaction Network, implemented in VB.NET. Characters and places of the narrative works are extracted in a list of raw words. Aided by the interface, the user selects names out of them. After this choice, the tool allows the user to enter some parameters, and, according to them, creates a network where the nodes are the characters and places, and the edges their interactions. Edges are labelled by performances. The output is a GV file, written in the DOT graph scripting language, which is rendered by means of the free open source software Graphviz.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Analysis of a Play by Means of CHAPLIN, the Characters and Places Interaction Network Software
Recently, we have developed a software able of gathering information on social networks from written texts. This software, the CHAracters and PLaces Interaction Network (CHAPLIN) tool, is implemented in Visual Basic. By …
Enhancing Indic Handwritten Text Recognition Using Global Semantic Information
Handwritten Text Recognition (HTR) is more interesting and challenging than printed text due to uneven variations in the handwriting style of the writers, content, and time. HTR becomes more challenging for the Indic lan…
DecoderHandwritten Text RecognitionHTRLanguage ModellingHandwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural Networks
In spite of advances in object recognition technology, Handwritten Bangla Character Recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla …
Object RecognitionTranslationGenerating Handwritten Chinese Characters using CycleGAN
Handwriting of Chinese has long been an important skill in East Asia. However, automatic generation of handwritten Chinese characters poses a great challenge due to the large number of characters. Various machine learnin…
Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network
Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank's cheque processing, and digitization and preservation of handwritten manuscript…