Identifying relationships between people is fundamental for the understanding of literatures. Main characters may have very certain strong relationship such as love, family, enemy, friend with others.
CharacterGo is a character interpretation system that be able to guide the interpretation of narrative events, explain character behaviors and steer the reader’s expectation about the plot using self-generated character relationship graphs. As such, it can have value for applications such as document summarization and machine reading.
- Python 2.7
- Stanford CoreNLP
- book-nlp
- Spacy
- scikit-learn
- Bash Shell
- Flask
- vis.js
- Name Entity Recognition
- Pronominal Reference Resolve
- Name Normalization/ Clustering
- "Tom", "Tom Sawyer", "Mr. Sawyer", "Thomas Sawyer" -> TOM_SAWYER
- Use book-nlp parse raw text to get tokenizers as well as name clusters
- Load and parse html output from book-nlp in Python
- Build supervised models including random forest, boosting, SVM and unsupervised representation using sklearn, plotly in Python
- Wrap all the functionalities in Flask framework, and set up local host server to handle raw input and output predictions and visualizations.
- Good performance (Accruacy 0.70) of character relationship extraction on plot summary
- Explore use more comprehensive NLP annotations in modeling (e.g. Action, noun chunks)
- Improve web app performance
