Develop data model for conversational analysis - ON-174
Preferred Disciplines: Machine Learning, Natural Language Processing (Masters or PhD)
Project Length: 4-6 months (1 unit)
Desired start date: As soon as possible
Location: Waterloo, ON
No. of Positions: 1
About the Company:
Summatti (legal corporate name - Microfluence Inc.) is a technology start-up based in Waterloo, Ontario & part of the WLU Launchpad incubator. They are the Google Analytics platform for support centers, giving organization an unprecedented level of insight into their customers’ experience.
The founders Rashmi & Sid come from marketing and technology backgrounds, respectively and have been part of the Waterloo community and more recently, the community tech scene for 17 years.
Summatti is a platform that analyzes data from various channels in a support center – email, chat, voice, web, etc. – to provide real-time insights into the customers’ experience.
We are currently piloting our platform with customers and looking to build/improve our machine learning models to analyze the context of the conversations/discussion that are being analyzed. The data sets obtained from customers contain conversations between a representative and one or more customers.
The main goal of this project would be to research methodologies/models to improve the platform’s NLP capabilities and provide more accurate results.
- Develop models that tag important parts of a conversation/sentence based on context of the conversation
- Research/develop/augment models for relationship extraction and topic segmentation
- Highlight patterns of issues based on an intrinsic understanding of the context of conversations analyzed
- Explore the use of word2vec models to process textual data. (Voice recordings are currently being processed with speech2text algorithms.)
- Current stack allows for integrations into IBM Watson and Google Tensorflow to build models and utilize off-the-shelf AI capabilities in the project.
- Open source data used from various sources – conversational corpus, social media, etc. – to train the models to tune out ‘noise’ in conversations.
Expertise and Skills Needed:
- Familiarity with supervised/unsupervised machine learning concepts.
- Experience with Python/Tensorflow and/or some NLP toolkits such as spaCy/OpenNLP/CoreNLP
- Good-to-have : Knowledge of Google Cloud Platform, data pipelines, big data storage/management
For more info or to apply to this applied research position, please