Search Results for author:
Found 8 papers, 2 papers with code
Middleman Bias in Advertising: Aligning Relevance of Keyphrase Recommendations with Search
E-commerce sellers are recommended keyphrases based on their inventory on which they advertise to increase buyer engagement (clicks/sales).
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation
Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
Open-vocabulary Extreme Multi-label Classification (OXMC) extends traditional XMC by allowing prediction beyond an extremely large, predefined label set (typically $10^3$ to $10^{12}$ labels), addressing the dynamic nature of real-world labeling tasks.
Graphite: A Graph-based Extreme Multi-Label Short Text Classifier for Keyphrase Recommendation
We present Graphite, a graph-based classifier model that provides real-time keyphrase recommendations that are on par with standard text classification models.
Triple GNNs: Introducing Syntactic and Semantic Information for Conversational Aspect-Based Quadruple Sentiment Analysis
This necessitates a dual focus on both the syntactic information of individual utterances and the semantic interaction among them.
MedDM:LLM-executable clinical guidance tree for clinical decision-making
To address this issue, we first propose LLM-executavle clinical guidance tree(CGT), which can be directly used by large language models, and construct medical diagnostic decision-making dataset (MedDM), from flowcharts in clinical practice guidelines.
Dynamic Multi-Scale Context Aggregation for Conversational Aspect-Based Sentiment Quadruple Analysis
Conversational aspect-based sentiment quadruple analysis (DiaASQ) aims to extract the quadruple of target-aspect-opinion-sentiment within a dialogue.
Online Camera-to-ground Calibration for Autonomous Driving
Online camera-to-ground calibration is to generate a non-rigid body transformation between the camera and the road surface in a real-time manner.
