Improving existing algorithms for Vectorizing sentences, Scoring semantic similarity, and Topic Clustering - ON-141
Preferred Disciplines: Computer Science and Computer Engineering (Master, PhD, Post-Doc)
Project Length: 8-12 months
Desired start date: ASAP
Location: Toronto, Ontario
No. of Positions: 1-2
Preferences: Waterloo, UofT, Queens
About the Company:
DeepPiXEL Inc. delivers solutions to corporations looking to improve and increase customer engagement for their products, services or engagement channels. We take questions that come into any given channel and provide answers to them with high accuracy. We focus on answering the repetitive questions, which constitute up to 80% of all customer inquiries. The most complicated questions are left to live agents to answer, with CARA assisting them by providing suggestions. This allows us to increase the number of simultaneous chats handled by support agents while reducing the time it takes for customers to get answers.
By utilizing CARA to serve their clients, our customers are able to improve their customers’ brand experience. Benefits include improvements in response times, average handling time, customer satisfaction, first call resolution, quality scores, agent satisfaction and adherence, conformance and agent productivity.
Improvements to our existing algorithms for: 1) Vectorizing English Language sentences, 2) Scoring semantic similarity/paraphrasing, 3) Topic Modelling and Clustering
Our product uses natural language models in order to identify similarity of phrases. We also score the similarity of two vectors using a proprietary scoring algorithm. When studying the data we collect, we analyze that data to get insights into how our product is used, and quantify the benefits it produces.
- Quick and accurate vectorizing of English Language phrases and paraphraphs.
- Improve scoring algorithms so that it reliably correlates to human level semantic similarity and paraphrasing, including for negative cases.
- Quickly and reliably produce insights by analyzing data collected.
- Vectorizing algorithms: word2vec, doc2vec, skip-thought.
- Cluster analysis: k-means, nearest neighbour, approximate nearest neighbour.
- Classification analysis: logistic regression, naive bayes, random forest.
Expertise and Skills Needed:
- Python, Web Server (Flask/Sanic), Database (PostGres), Git
- Natural Language Processing
- Artificial Intelligence algorithm knowledge
- Ability to research different approaches and conduct experiments independently
For more info or to apply to this applied research position, please
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Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform or directly to Jillian Hatnean e at, jhatnean(at)mitacs.ca.