Erik Cambria

Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis

2016

The advent of the Social Web has provided netizens with new tools for creating and sharing, in a time- and cost-efficient way, their contents, ideas, and opinions with virtually the millions of people connected to …

Open Secrets and Wrong Rights

2017

Satire is an element of figurative language which often conveys feelings contrary to what is literally stated. It refers to a trenchant wit, irony, or sarcasm used to expose discredit vice or folly. The presence …

Adaptive two-stage feature selection for sentiment classification

2017

Sentiment analysis is able to automatically extract valuable customer information from large amount of unstructured text data to support decision making in manufacturing applications such as product design and demand planning. One of the key …

Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

2019 · arXiv (Cornell University)

Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes. In this paper, we introduce: 1) an agreement score to evaluate the …

GECKA3D: A 3D Game Engine for Commonsense Knowledge Acquisition.

2016 · arXiv (Cornell University)

Commonsense knowledge representation and reasoning is key for tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In …

SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis

2020

Deep learning has unlocked new paths towards the emulation of the peculiarly-human capability of learning from examples. While this kind of bottom-up learning works well for tasks such as image classification or object detection, it …

Disentangled Retrieval and Reasoning for Implicit Question Answering

2022 · IEEE Transactions on Neural Networks and Learning Systems

To date, most of the existing open-domain question answering (QA) methods focus on explicit questions where the reasoning steps are mentioned explicitly in the question. In this article, we study implicit QA where the reasoning …

PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis

2024 · arXiv (Cornell University)

While existing Aspect-based Sentiment Analysis (ABSA) has received extensive effort and advancement, there are still gaps in defining a more holistic research target seamlessly integrating multimodality, conversation context, fine-granularity, and also covering the changing sentiment …

Medical Report Generation via Multimodal Spatio-Temporal Fusion

2024

Medical report generation aims at automating the synthesis of accurate and comprehensive diagnostic reports from radiological images. The task can significantly enhance clinical decision-making and alleviate the workload on radiologists. Existing works normally generate reports …

Towards Multimodal Empathetic Response Generation: A Rich Text-Speech-Vision Avatar-based Benchmark

2025 · arXiv (Cornell University)

Empathetic Response Generation (ERG) is one of the key tasks of the affective computing area, which aims to produce emotionally nuanced and compassionate responses to user's queries. However, existing ERG research is predominantly confined to …

Language Models as Inductive Reasoners

2024

Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, Furu Wei. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). …

Deep Cut-informed Graph Embedding and Clustering

2025 · arXiv (Cornell University)

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there …

A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks

2016 · arXiv (Cornell University)

Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an "apparently positive" sentence and, hence, negatively affect polarity detection performance. To …

Label Embedding for Zero-shot Fine-grained Named Entity Typing.

2016 · International Conference on Computational Linguistics

Named entity typing is the task of detecting the types of a named entity in context. For instance, given “Eric is giving a presentation”, our goal is to infer that ‘Eric’ is a speaker or …

Recent Trends in Deep Learning Based Natural Language Processing

2017 · arXiv (Cornell University)

Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of …

Sentiment Analysis Is a Big Suitcase

2017 · IEEE Intelligent Systems

Although most works approach it as a simple categorization problem, sentiment analysis is actually a suitcase research problem that requires tackling many natural language processing (NLP) tasks. The expression “sentiment analysis” itself is a big …

Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph

2018

AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, Louis-Philippe Morency. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

2018 · IEEE Computational Intelligence Magazine

Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of …

Augmenting End-to-End Dialogue Systems With Commonsense Knowledge

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human utterances in an …

Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

The goal of building intelligent dialogue systems has largely been separately pursued under two paradigms: task-oriented dialogue (TOD) systems, which perform task-specific functions, and open-domain dialogue (ODD) systems, which focus on non-goal-oriented chitchat. The two …