Natural Language Understanding

Natural Language Understanding (NLU) encompasses statistical natural language understanding, dialogue planning, and understanding across multiple turns. We leverage language models to power classification, extraction, summarization, and dialogue management

Publications

Paper
|
March 6, 2024
ICDE 2024

An Effective, Efficient, and Stable Framework for Query Clustering

Paper
|
August 22, 2023
CIKM 2023 (applied research track)

Content-Based Email Classification at Scale

Paper
|
May 10, 2023
ACL 2023 (Industry Track)

Consistent Text Categorization Using Data Augmentation in E-Commerce

Paper
|
August 24, 2022
CIKM 2022

Improving Text-Based Similar Product Recommendation for Dynamic Product Advertising at Yahoo

Paper
|
June 15, 2022
KDD 2022

Multilingual Taxonomic Web Page Classification for Contextual Targeting at Yahoo

Paper
|
December 10, 2021
AAAI 2022

Classifying Emails into Human vs Machine Category

Paper
|
August 10, 2021
CIKM 2021

TSI: An Ad Text Strength Indicator Using Text-to-CTR and Semantic-Ad-Similarity

Paper
|
May 28, 2021

An Evaluation and Annotation Methodology for Product Category Matching in E-Commerce

Paper
|
May 17, 2021
KDD 2021

VisualTextRank: Unsupervised Graph-Based Content Extraction for Automating Ad Text to Image Search

Paper
|
December 8, 2020
COLING 2020

Effective Few Shot Classification with Transfer Learning

Paper
|
November 12, 2020
ASONAM 2020

Locally Constructing Product Taxonomies from Scratch Using Representation Learning

Paper
|
October 15, 2020
IEEE Control Systems Magazine

Feedback Control in Programmatic Advertising: The Frontier of Optimization in Real-Time Bidding

Paper
|
September 15, 2020
EMNLP 2020

TNT: Text Normalization Based Pre-Training of Transformers for Content Moderation

Paper
|
November 10, 2019
AAAI 2020

DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

Paper
|
January 1, 2019
ACL

Hierarchical Transfer Learning for Multi-label Text Classification

Paper
|
January 1, 2019
Companion Proceedings of The 2019 World Wide Web Conference

Understanding Travel from Web Queries Using Domain Knowledge from Wikipedia

Paper
|
January 1, 2019
Companion Proceedings of The 2019 World Wide Web Conference

Inferring Advertiser Sentiment in Online Articles Using Wikipedia Footnotes

Paper
|
January 1, 2019
EMNLP 2019 Workshop on Noisy User Text

Unsupervised Neologism Normalization Using Embedding Space Mapping

Paper
|
January 1, 2019
ICDM'19

User Response Driven Content Understanding with Causal Inference