Intelligent Database Systems Lab Presenter : WU, MIN-CONG Authors : Jorge Villalon and Rafael A. Calvo 2011, EST Concept Maps as Cognitive Visualizations.

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Presentation transcript:

Intelligent Database Systems Lab Presenter : WU, MIN-CONG Authors : Jorge Villalon and Rafael A. Calvo 2011, EST Concept Maps as Cognitive Visualizations of Writing Assignments

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Intelligent Database Systems Lab Motivation This is a significant improvement over previous efforts that focused on providing feedback on the final product that students submit, Concept map visualization can help students reflect about their own writing.

Intelligent Database Systems Lab Objectives We have also showed new approaches to help students reflect on their writing and how students understand the use of these new tools(CMM).

Intelligent Database Systems Lab Methodology- The Concept Map Miner C:set of concepts R: set of relationships between concepts T:the map's topology or spatial distribution of the concepts. First step Second step Third step

Intelligent Database Systems Lab Methodology- The Concept Map Miner (Concept Identification) Objectives : identified that compound nouns Input: sentence’s dependency tree dependency tree linking words

Intelligent Database Systems Lab Methodology- The Concept Map Miner (Concept Identification) using the extracted terminological maps with all terminological map rules applied to obtain a reduced map. vertices it corresponds to the compound noun ‘artificial language’.

Intelligent Database Systems Lab Methodology- The Concept Map Miner (Relationship Identification) Objectives : identify concept’s relationships Input: terminological map and a set of concepts using Dijkstra's algorithm

Intelligent Database Systems Lab Methodology- The Concept Map Miner (summarization) using Latent Semantic Analysis (LSA)

Intelligent Database Systems Lab Methodology- Relationship Extraction and CMM requires that a group of human annotators build a ‘gold standard’ corpus with annotations. compare those extracted automatically. problem Identifying knowledge in text is a subjective task Solve annotated by two or more human coders who are required to identify

Intelligent Database Systems Lab Experiment - Data Dataset: A set of essays (N=43) collected as a writing proficiency diagnostic activity for first year-university students Average word Total words Each essay 468 words set of essays 18,431 words

Intelligent Database Systems Lab Experiment - Annotation Method A first version of the benchmarking corpus the main problem found was that coders created relationships that were not explicitly present in the essay, but were an interpretation of several propositions.

Intelligent Database Systems Lab Experiment - Comparative Measures for CMs Lexical term Precision (LP) Taxonomic Overlap Precision (TP)

Intelligent Database Systems Lab Experiment - Results

Intelligent Database Systems Lab Experiment - Integration of CMM as Writing Support Tool

Intelligent Database Systems Lab Conclusions Student : The results show that the automatic generation of CMs from documents is feasible, despite the complexities of noisy data. Instructor: averaging 94% for LP with human coders.

Intelligent Database Systems Lab Comments Advantages – Tutors assess the essays faster and more accurately and consistently Applications – Concept Map Mining.