Health Information Processing
9th China Health Information Processing Conference, CHIP 2023, Hangzhou, China, October 27–29, 2023, Proceedings
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55 Result(s)
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Book and Conference Proceedings
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Book and Conference Proceedings
Health Information Processing. Evaluation Track Papers
9th China Conference, CHIP 2023, Hangzhou, China, October 27–29, 2023, Proceedings
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Chapter and Conference Paper
Data and Privacy Crisis on Internet Application of Artificial Intelligence
The widely use of intelligent applications makes us live with data and algorithms every day. While improving the quality of our lives, its potential misuse of personal information poses a threat to our privacy...
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Article
Trustworthy machine reading comprehension with conditional adversarial calibration
Machine Reading Comprehension (MRC) has achieved impressive answer inference performance in recent years but rarely considers the trustworthiness and reliability of the deployed systems. However, it is crucial...
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Chapter
Experiment Platform for Dialogue Intent Recognition Based on Deep Learning
This chapter propose the first open intent recognition plat form TEXTOIR, which integrates two complete modules: open intent detection and open intent discovery. It provides toolkits for each module with commo...
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Chapter
Dialogue System
With the rapid development of Internet technology and the increasing popularity of intelligent robots and intelligent hardware devices, traditional keyword-based search engine information retrieval is no longe...
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Chapter
Summary
This book focuses on the study of conversation intent understanding in Artificial Intelligence. The research objective is to enhance machine understanding of human intents in the real world. With the increasin...
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Chapter
A Dual RNN Semantic Analysis Framework for Intent Classification and Slot
The research on spoken language understanding (SLU) system has progressed extremely fast during the past decades. Intent detection and slot filling are two main tasks for building a SLU system. Multiple deep l...
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Chapter
Unknown Intent Detection Based on Large-Margin Cosine Loss
Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. This chapter presents a two-stage method for detecting unknown intents. T...
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Book
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Book
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Chapter
Intent Classification Based on Single Model
Intent classification is an important preprocessing step in natural language processing tasks, used to categorize input text into specific intents, in order to assign them to corresponding subsystems or proces...
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Chapter
Unknown Intention Detection Method Based on Dynamic Constraint Boundary
Open intent classification is a challenging task in dialogue systems. On the one hand, ensuring the classification quality of known intents is crucial. On the other hand, identifying open (unknown) intent duri...
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Chapter
Discovering New Intents with Deep Aligned Clustering
Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. These methods also have difficulties in p...
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Chapter
Discovering New Intents Via Constrained Deep Adaptive Clustering with Cluster Refinement
Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existin...
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Chapter
Multimodal Sentiment Analysis Platform and Application
This chapter introduces the need for an integrated platform specifically designed for Multimodal Sentiment Analysis (MSA) tasks. To address this gap, the M-SENA platform is introduced as an open-source tool ai...
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Chapter
Unknown Intent Detection Method Based on Model Post-Processing
With the maturity and popularity of dialogue systems, detecting user’s unknown intent in dialogue systems has become an important task. It is also one of the most challenging tasks, as it is difficult to obtai...
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Chapter
Intent Recognition
In practical scenarios of the human-machine dialogue system, intent detection is an important and challenging problem. The human-machine dialogue system firstly converts the user’s query into a structured sema...
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Chapter
Multimodal Sentiment Analysis Data Sets and Preprocessing
This chapter emphasizes the importance of standard datasets in multimodal sentiment analysis. Deep learning algorithms have significantly improved the performance of multimodal sentiment analysis, making it a ...
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Chapter
Unimodal Sentiment Analysis
This chapter explores the realm of sentiment analysis, covering diverse domains such as text, audio, and facial expressions. It introduces novel approaches that address the limitations of existing methods, emp...
