NameHarvester is a program that extracts names of people from text files. It was developed as a group effort and utilizes a token, text block, and document object abstraction to read in files. The program then categorizes the tokens using lists of common word types and creates a feature vector that is used for training and inferencing a Support Vector Machine (SVM) learning model.
- Extracts names of people from text files
- Tokenizes text into individual words or tokens
- Categorizes tokens using lists of common word types (e.g., nouns, verbs, adjectives)
- Creates a feature vector based on the categorized tokens
- Trains and inferences an SVM learning model using the feature vector
This project is licensed under the MIT License.
This project was developed as a collaborative effort by the following group members:
Matthew Haydon
Mikel Douangdara (Mike)
Corey Brady
Peter Spadaro
Ralph Mpanu-Mpanu
Iizalaarab Elhaimeur (Izzy)