English2MindMap: An Automated System for MindMap Generation from English Text (original) (raw)
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English2MindMap: Automated system for Mind Map generation from Text
MindMapping is a well-known technique used in note taking and is known to encourage learning and studying. Besides, MindMapping can be a very good way to present knowledge and concepts in a visual form. Unfortunately there is no reliable automated tool that can generate MindMaps from Natural Language text. This paper fills in this gap by developing the first evaluated automated system that takes a text input and generates a MindMap visualization out of it. The system also could visualize large text documents in multilevel MindMaps in which a high level MindMap node could be expanded into child MindMaps. The proposed approach involves understanding of the input text converting it into intermediate Detailed Meaning Representation (DMR). The DMR is then visualized with two proposed approaches; Single level or Multiple levels which is convenient for larger text. The generated MindMaps from both approaches were evaluated based on Human Subject experiments performed on Amazon Mechanical T...
Multimedia Tools and Applications, 2015
is a well-known technique for note-taking, which encourages learning and studying. MindMapping has been manually adopted to help present knowledge and concepts in a visual form. Unfortunately, there is no reliable automated approach to generate MindMaps from Natural Language text. This work firstly introduces the MindMap Multi-level Visualization concept that jointly visualize and summarize textual information. The visualization is achieved pictorially across multiple levels using semantic information (i.e. ontology), while the summarization is achieved by the information in the highest levels as they represent abstract information in the text. This work also presents the first automated approach that takes a text input and generates a MindMap visualization out of it. The approach could visualize text documents in multi-level MindMaps, in which a high-level MindMap node could be expanded into child MindMaps. The proposed method involves understanding of the input text and converting it into intermediate Detailed Meaning Representation (DMR). The DMR is then visualized with two modes; Single level or Multiple levels, which is convenient for larger text. The generated MindMaps from both approaches were evaluated based on human subject experiments performed on Amazon Mechanical Turk with various parameter settings.
MindMapping [1] is a well-known technique used in note taking, and is known to encourage learning and studying. MindMapping has been manually adopted to help present knowledge and concepts in a visual form. Unfortunately, there is no reliable automated approach that can generate MindMaps from Natural Language text. This work firstly introduces MindMap Multilevel Visualization concept which is to jointly visualize and summarize textual information. The visualization is achieved pictorially across multiple levels using semantic information (i.e. ontology), while the summarization is achieved by the information in the highest levels as they represent abstract information in the text. This work also presents the first automated approach that takes a text input and generates a MindMap visualization out of it. The approach could visualize text documents in multilevel MindMaps, in which a high-level MindMap node could be expanded into child MindMaps. The proposed method involves understanding of the input text and converting it into intermediate Detailed Meaning Representation (DMR). The DMR is then visualized with two modes; Single level or Multiple levels, which is convenient for larger text. The generated MindMaps from both approaches were evaluated based on Human Subject experiments performed on Amazon Mechanical Turk with various parameter settings.
Multimedia Tools and Applications, 2015
MindMapping [47] is a well-known technique for note-taking, which encourages learning and studying. MindMapping has been manually adopted to help present knowledge and concepts in a visual form. Unfortunately, there is no reliable automated approach to generate MindMaps from Natural Language text. This work firstly introduces the MindMap Multi-level Visualization concept that jointly visualize and summarize textual information. The visualization is achieved pictorially across multiple levels using semantic information (i.e. ontology), while the summarization is achieved by the information in the highest levels as they represent abstract information in the text. This work also presents the first automated approach that takes a text input and generates a MindMap visualization out of it. The approach could visualize text documents in multi-level MindMaps, in which a high-level MindMap node could be expanded into child MindMaps. The proposed method involves understanding of the input text and converting it into intermediate Detailed Meaning Representation (DMR). The DMR is then visualized with two modes; Single level or Multiple levels, which is convenient for larger text. The generated MindMaps from both approaches were evaluated based on human subject experiments performed on Amazon Mechanical Turk with various parameter settings.
IRJET- IMAGINE: MIND MAP GENERATION TOOL USING AI TECHNOLOGIES
IRJET, 2020
A mind map is a diagram used to represent words, ideas, or other items linked to and arranged around a central keyword or idea. The propounded idea helps to organize and summarize textual contexts efficiently using Mind Mapping. This tool provides a prospect to transform many literatures automatically into mind maps. Mind maps are used to generate, visualize, structure, and classify ideas, and as an aid in organization, study, project management, problem solving, decision making, and writing. It has been long used in brainstorming and as an effective educational tool. Many students find it easier to follow and remember information presented in the mind map form rather than pure text. Mind map is used in Organizing, meetings, planning, note taking, presentation, and above all, in education. It's much easier to understand well-structured data instead of unstructured. Mind maps can be used as a tool to model semi structured documents, to organize data in a more intuitive way. This model would bring mind maps with association, back-tracking, comparison and cognitive functionality together with a new way of connecting elements of mind maps. IMAPGINE takes text from the any data sources (doc, docx, pdf, txt, rtf, xlsx, web-page), extract text data from the source document through standard algorithms for document manipulation then it determines position of the text in the code through global code structure Refine text positioning through selective tag encapsulation extract text from the code. IMAPGINE can process images and charts encountered in the documents too. If process models or flow charts present in the source document, it will convert it too into a mind map by extracting focus data in process models and links between them are determined by comparing them based on their names. If a term with the same word base is found, it is then converted to a mind map. Generate titles for mind map.
2009
Natural language processing has many applications that always make it one of the most active research areas. Application can include text summarization, headline generation, question answering system _--, etc. Our work here has a novel application to represent. Actually it has the leadership to be the first contribution. This comes as the first contribution in this area covering Mind Maps automation (MMA) process; this paper includes the overall structure of mind maps automation system and contributed algorithms in different stages of the MMA System. It also covers previous contributions in NLP that work as essential components in MMA system. This paper simply helps a big dream exist in the real life.
Here, we describe our work in developing Indonesian Mind Map Generator that employs several Indonesian natural language understanding tools as its main engine. The Indonesian Mind Map Generator 1 aims to help the user in easily making a Mind Map object. The system consists of several Indonesian natural language understanding tools such as Indonesian POS Tagger, Indonesian Syntactic Parser, and Indonesian Semantic Analyzer. The methods used for developing each of Indonesian natural language understanding tools are devised to such an extend that they are enable to alleviate the low availability of Indonesian language resources. For Indonesian POS Tagger, we employed HMM and subsequently enhanced the result by using affix tree. As for the Indonesian Syntactic Parser, we compared the performance of CYK and Earley parser, which are known as common dynamic algorithms in PCFG. The Indonesian Semantic Analyzer consists of several components such as lexical semantic attachment, reference resolution, and Semantic Analyzer itself that transforms the parse tree result into first order logic representation. In our work, instead of using a rich resource on semantic information for each vocabulary, we defined several rules for the lexical semantic attachment based on POS Tags and certain words. Finally, to develop the Mind Map generator, we used the radial drawing method to visualize the first order logic representation and we also built a Mind Map editor to allow a user in modifying the Mind Map result. To evaluate the result, we conducted the experiments for each component mentioned previously. The POS Tagger accuracy achieved 96.5%, the Syntactic Parser achieved accuracy of 47.22%, and the Semantic Analyzer achieved accuracy of 62.5%. The final result of Mind Map object was evaluated by 5 respondents. The results of evaluationshowed that, for the simple sentence, the Mind Map object can be easily understood.
Mind Maps Automation (MMA) System
2009
Natural language processing has many applications that always make it one of the most active research areas. Application can include text summarization, headline generation, question answering system _--, etc. Our work here has a novel application to represent. Actually it has the leadership to be the first contribution. This comes as the first contribution in this area covering Mind Maps automation (MMA) process; this paper includes the overall structure of mind maps automation system and contributed algorithms in different stages of the MMA System. It also covers previous contributions in NLP that work as essential components in MMA system. This paper simply helps a big dream exist in the real life.
An Exploratory Analysis of Mind Maps
The results presented in this paper come from an exploratory study of 19,379 mind maps created by 11,179 users from the mind mapping applications ‘Docear’ and ‘MindMeister’. The objective was to find out how mind maps are structured and which information they contain. Results include: A typical mind map is rather small, with 31 nodes on average (median), whereas each node usually contains between one to three words. In 66.12% of cases there are few notes, if any, and the number of hyperlinks tends to be rather low, too, but depends upon the mind mapping application. Most mind maps are edited only on one (60.76%) or two days (18.41%). A typical user creates around 2.7 mind maps (mean) a year. However, there are exceptions which create a long tail. One user created 243 mind maps, the largest mind map contained 52,182 nodes, one node contained 7,497 words and one mind map was edited on 142 days.
Developing a Formal Model for Mind Maps, 2011
First Workshop on …, 2011
Mind map is a graphical technique, which is used to represent words, concepts, tasks or other connected items or arranged around central topic or idea. Mind maps are widely used, therefore exist plenty of software programs to create or edit them, while there is none format for the model representation, neither a standard format. This paper presents and effort to propose a formal mind map model aiming to describe the structure, content, semantics and social connections. The structure describes the basic mind map graph consisted of a node set, an edge set, a cloud set and a graphical connections set. The content includes the set of the texts and objects linked to the nodes. The social connections are the mind maps of other users, which form the neighborhood of the mind map owner in a social networking system. Finally, the mind map semantics is any true logic connection between mind map textual parts and a concept. Each of these elements of the model is formally described building the suggested mind map model. Its establishment will support the application of algorithms and methods towards their information extraction.