Inspiration
Most current autocompletion tools predict only individual words. We wanted to predict sentences.
What it does
- Reads in text and creates templates of groups of similar sentences
- Takes in the start of a sentence and recommends an ending
How we built it
- Started out by building the base of our infrastructure on a Flask server
- Developed our method of grouping sentences together
- Used sk-learn's CountVectorizer to convert individual sentences into vectors
- Used sk-learn's DBSCAN to group together similar vectors
- After obtaining groups of similar sentences, created templates that represent their structure
- Inserted templates into a trie for easy lookup
Challenges we ran into
- How to best represent sentences as vectors
- Determining the type of machine learning to use to cluster similar sentences
- Teasing out a common sentence structure from similar sentences
Accomplishments that we're proud of
- Accurately group similar sentences and create templates that represent them
What we learned
- Utilizing sci-kit learn's machine learning library
- Different methods of unsupervised learning
- Ways to represent sentences as vectors
- Creating backend services using Flask
- Trie implementation and traversal
What's next?
Keep up with what we’re doing:
Daniel Jiang - danieljiang.me
Scott Numamoto - scottnumamoto.com
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