Webinar: Understanding Names with Neural Networks
Please join us for a webinar on May 12 at 6:00 am GMT (Asia, EMEA) or 3:00 pm GMT (US, EMEA).
Understanding Names with Neural Networks
Matching names across languages and writing systems is a critical issue in a variety of consumer and governmental domains. Historically, computers have attempted to solve this problem with ad-hoc methods such as edit distance, sound indexing, and Hidden Markov Models, but these have a variety of practical limitations in this problem space, which we will explore. To address these issues, we present our research and development team’s work on doing English/Japanese name matching using deep neural networks, which provides a substantial boost in accuracy.
A 40-minute presentation will be followed by a Q&A session.
May 12 at 6:00 am GMT
9:00 am: Tel Aviv
10:00 am: Dubai
2:00 pm: Singapore
3:00 pm: Tokyo
May 12 at 3:00 pm GMT
8:00 am: PT
11:00 am: ET
4:00 pm: London
6:00 pm: Tel Aviv
7:00 pm: Dubai
Kfir Bar is the Chief Scientist at Basis Technology. He has spent many years working in a wide range of natural language processing (NLP) disciplines, including neural machine translation and named entity recognition for anti-crime/terror applications. Kfir focuses on combining linguistic knowledge with sophisticated AI algorithms to extract the most important information from a piece of text. The AI applications he builds are integrated into many of the world’s leading anti-money laundering platforms.
In addition, Kfir is a lecturer at Tel Aviv University where he teaches courses in computer science, digital humanities, machine learning, and natural language processing. He holds a Ph.D. in computer science from Tel Aviv University.
Before Basis, he worked for Intuview as CTO, supporting national security and counter-terrorism missions by deducing authorship, sentiment, intent, and other contextual information. In 2013, he co-founded Comprendi, which transforms big data into actionable marketing insights.
Senior Research Engineer
Philip Blair is a Senior Research Engineer on Basis Technology’s R&D team. He investigates practical applications of deep learning technologies for use in text analytics. Philip also leads Basis Technology’s machine learning infrastructure team, focused on deploying cutting-edge algorithms into the field. Philip has published research which explores intrinsic evaluations of text embeddings to better predict their suitability for downstream applications in other natural language processing tasks.
Carmel Eliav is a research engineer on Basis Technology's R&D team. She investigates and develops machine learning and deep learning solutions for a wide range of NLP problems, including taking the lead on the English and Japanese neural name matcher. Carmel holds a bachelor’s degree in computer science, and has spent eight years in the Israeli Defense Force as an intelligence officer.