Reference number : GC-2017-Q4-01161, amendment number : , agreement type : G, recipient legal name : Makrehchi, Masoud (University of Ontario Institute of Technology), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : One key data component for engaging and acquiring customers is being able to identify names of products in_x000D_
online forums. Therefore, the extraction of product/brand names will generate data to help vendors and thus_x000D_
increase overall business revenue. The traditional approach to extract named entities requires time-consuming_x000D_
and expensive manual human-labelled training sets. In addition, we deal with many product or service sectors_x000D_
which come with different size, language, and content. Thus fitting a supervised model which is based on_x000D_
human-annotated data, for each product/brand to each sector (vertical) can be very expensive. Three major_x000D_
challenges are: 1) the high cost of generating training data for each type of product, 2) covering large divergent_x000D_
types of products from all verticals, and 3) disambiguating different types of entities based on context._x000D_
Therefore, in the absence or lack of training data, alternative solution is semi-supervised learning algorithms_x000D_
such as bootstrapping and meta-learning methods such as self-training and co-training. In these methods, we_x000D_
can train a model either with no or very few annotated data. In this research, a hybrid approach combining_x000D_
transfer learning and semi-supervised learning is investigated to identify and extract named entities in our_x000D_
domain of interest. In transfer learning, the solution for the lack of annotated data in the target domain, is to_x000D_
adapt annotated data from other domains._x000D_
VerticalScope (the industry partner) is a Canadian company that is becoming a leading player in data science_x000D_
research and development for understanding user-generated content on the Internet in a variety of sectors. The_x000D_
proposed project contributes to the growth of VerticalScope, and to the Canadian economy as a result, by_x000D_
allowing the company to apply cutting-edge research for improving user experience on their forums, and hence_x000D_
the attractiveness of its service to the businesses., program purpose french : One key data component for engaging and acquiring customers is being able to identify names of products in_x000D_
online forums. Therefore, the extraction of product/brand names will generate data to help vendors and thus_x000D_
increase overall business revenue. The traditional approach to extract named entities requires time-consuming_x000D_
and expensive manual human-labelled training sets. In addition, we deal with many product or service sectors_x000D_
which come with different size, language, and content. Thus fitting a supervised model which is based on_x000D_
human-annotated data, for each product/brand to each sector (vertical) can be very expensive. Three major_x000D_
challenges are: 1) the high cost of generating training data for each type of product, 2) covering large divergent_x000D_
types of products from all verticals, and 3) disambiguating different types of entities based on context._x000D_
Therefore, in the absence or lack of training data, alternative solution is semi-supervised learning algorithms_x000D_
such as bootstrapping and meta-learning methods such as self-training and co-training. In these methods, we_x000D_
can train a model either with no or very few annotated data. In this research, a hybrid approach combining_x000D_
transfer learning and semi-supervised learning is investigated to identify and extract named entities in our_x000D_
domain of interest. In transfer learning, the solution for the lack of annotated data in the target domain, is to_x000D_
adapt annotated data from other domains._x000D_
VerticalScope (the industry partner) is a Canadian company that is becoming a leading player in data science_x000D_
research and development for understanding user-generated content on the Internet in a variety of sectors. The_x000D_
proposed project contributes to the growth of VerticalScope, and to the Canadian economy as a result, by_x000D_
allowing the company to apply cutting-edge research for improving user experience on their forums, and hence_x000D_
the attractiveness of its service to the businesses., agreement title english : Identifying General Product and Brand Names in Online Forums, agreement title french : Identifying General Product and Brand Names in Online Forums, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada