ODSC APAC 2021 is right around the corner this September 15-16th, and while there’s something for everyone, NLP is sticking out as one of the focal points of this conference.
Natural language processing is indeed special in the APAC region, namely because there’s a greater need for diverse datasets, due to the number of different languages spoken in the region. This has lead researchers to develop novel and exciting techniques to address these concerns. At ODSC APAC in a few weeks, you’ll be able to hear from these data scientists about NLP, and hear from some research institutions that focus on NLP
Highlighted ODSC APAC 2021 NLP Sessions:
Fairness in Natural Language Processing: Tim Baldwin, PhD Dir. / VP / Laureate Professor | ARC Centre for Cognitive Computing in Medical Technologies / ACL / Uni. Melbourne
Natural language processing (NLP) has made truly impressive progress in recent years and is being deployed in an ever-increasing range of user-facing settings. Accompanied by this progress has been a growing realization of inequities in the performance of naively-trained NLP models for users of different demographics, with minorities typically experiencing lower performance levels. In this talk, Tim will illustrate the nature and magnitude of the problem, and outline a number of approaches that can be used to train fairer models based on different data settings.
Uncover Hidden Business Insights from Unstructured Data: Dr. Lau Cher Han | CEO / Founder | LEAD / CoronaTracker
This workshop covers the current state of development and application in using Natural Language Processing (NLP) in businesses. Participants will learn the basic techniques of NLPs, and also practical use cases on how to apply these techniques in their own industries.
NLP in Ecommerce: Mathangi Sri | Head of Data – GoFood | Gojek | Related Blog
This tutorial will cover an overview of different areas of using NLP in eCommerce. Specifically, we will drill down to sentiment analysis of reviews and attribute extraction. We can cover a brief introduction to different types of sentiment analysis.
Data Science Supporting Clinical Decision Making: What, Why, How?: Professor Karin Verspoor | Dean / Fellow | School of Computing Technologies, RMIT University / Australasian Institute of Digital Health
The adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilize that information to support clinical decisions. In this talk, I will discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to model it effectively.
Model, Task and Data Engineering for NLP: Shafiq Rayhan Joty, PhD | Assistant Professor / Senior Manager | NTU Natural Language Processing Group, Nanyang Technological University / Salesforce AI
With the advent of deep learning and neural methods, NLP research over the last decade has shifted from feature engineering to model engineering, primarily focusing on inventing new architectures for NLP problems. Two other related factors that are getting more attention only recently are: (i) which objectives (or tasks) to optimize, and (ii) how to better use the available data; referred to as task engineering and data engineering, respectively. In this talk, Shafiq will present our recent work along these three dimensions. In particular, Shafiq will first present novel neural architectures for parsing texts into hierarchical structures and efficient parallel encoding of such structures for better language understanding and generation.
On Summarization Systems: Dr. Sriparna Saha | Group Member / Associate Professor, Department of Computer Science and Engineering | AI-NLP-ML Research Lab / IIT Patna
Different facets of summarization, like document summarization, figure-summarization, microblog summarization, and multi-modal microblog summarization, will be discussed in the talk.
Finding Rare Events in Text: Debanjana Banerjee | Senior Data Scientist | Walmart Labs
The session will focus on identifying rare events in text with positive unlabeled data. PU learners are massively used for one-class classification but the challenge becomes far steeper when the event under consideration has a low probability of occurrence.
How to do NLP When You Don’t Have a Labeled Dataset?: Sowmya Vajjala, PhD | Research Officer, Digital Technologies | National Research Council | Related Blog
In this workshop, Sowmya will introduce some strategies to create labeled datasets for a new task and build your first models with that data. At the end of this session, the participants are expected to get some ideas for solving the data bottleneck in their organization.
AI and ODSC APAC NLP Research Labs
The University of Tokyo Miyao Lab
The Miyao Group, lead by Yusuke Miyao, heavily focuses on the mathematical models and algorithms behind NLP, such as syntactic parsing, semantic parsing, knowledge discovery, question answering, and more. Recently, they build the NIILC Question Answering Dataset, which scours Wikipedia for answers to questions.
The University of Tokyo Aizawa Lab
As another lab that focuses on NLP, the Aizawa Lab researches subjects in text and media studies such as text mining, machine reading comprehension, and human language activities. They provide a lot of their research openly, including one of their main topics in “establishing a common understanding between humans and computers through natural language texts.”
Similar to the University of Tokyo, and some other APAC AI research universities, there are a number of interesting AI labs within the Kyoto University Artificial Intelligence Research Unit. This includes the Shimodaira Lab (statistics and machine learning), the Yoshikawa & Ma Laboratory (big data and data mining), the Kashima Machine Learning and Data Mining Research Laboratory (data analysis technologies based on statistical machine learning), and the Kurohashi-Chu-Murawaki Lab (NLP and NLU).
Tokyo Institute of Technology: Okazaki Laboratory
Focusing on NLP, the Okazaki Laboratory researches for both theory and applications. The lab studies all things NLP, such as sentiment analysis, machine translation, natural language generation, and natural language understanding. Their latest research focused on Multimodal Pretraining and BERT.
Also featuring a unique assortment of individual labs, including ones devoted to NLP, the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST GSAI), aka just KAIST AI, is one of the highest-ranked AI research institutes in the world. With research that covers nearly every topic under the AI umbrella, their research is not just theoretical but has found applications with tech companies including Google Brain, IBM Watson lab, Disney Research, NVIDIA, and so on. The lab is making news constantly, such as their work on developing a new chip for efficient deep reinforcement learning, working with K-pop groups on creating a metaverse, and more.
NUS (National University of Singapore) – Natural Language Processing Group
As the name suggests, the NUS Natural Language Processing Group exclusively studies NLP. Lead by Professor NG Hwee Tou, this group offers open-source software, data, and insights into their publications. As paraphrasing is common, they’ve developed PEM, which is an automatic metric designed to evaluate the quality of paraphrases.
SUTD (Singapore University of Technology and Design) – StatNLP
Also devoted to NLP, the StatNLP lab offers a lot of resources and datasets for anyone interested. For example, the Multilingual Geoquery, which is a multilingual dataset for Geoquery. Each instance is a sentence annotated with its meaning representations.
Machine Learning Lab
The Machine Learning Lab at the Department of Computer Science at the India Institute of Science has the ambitious goal of understanding artificial intelligence and its many facets, including NLP, machine learning, numerical optimization, and deep learning. Currently, the Machine Learning Lab is working on developing simultaneous localization and mapping (SLAM), a core component of achieving level 5 autonomy in self-driving vehicles.
School of Artificial Intelligence (ScAI)
One of ScAI’s main initiatives is to create multilingual NLP systems for knowledge base completion, information extraction, and machine translation. Their applied research is focused on the areas of health care, robotics, materials, and industry 4.0.
The Centre for Machine Intelligence and Data Science (C-MInDS)
A relatively new research institution, C-MInDS was founded in February 2020. Already its members have delved into research in multiple fields, including AI, machine learning, NLP, agent-based learning, and much more. C-MInDS’s recent research in social networking seeks to understand how fake news spreads in communities and groups.
Learn more with the ODSC APAC NLP Track
There are more AI research labs that will be represented at ODSC APAC 2021. Learn about them in our summaries of APAC AI research labs and India AI research labs. By attending ODSC APAC 2021, you’ll get hands-on experience with building NLP projects that can be used in real-world settings. Learn everything from the basics to advanced NLP skills, so even if you’re a beginner, you can finish the event with a strong understanding of NLP. Register now for 10% off all ticket types!