Search Results for author: Erik Cambria

Found 83 papers, 30 papers with code

WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts

no code implementations GWC 2018 Anupam Mondal, Dipankar Das, Erik Cambria, Sivaji Bandyopadhyay

Information extraction in the medical domain is laborious and time-consuming due to the insufficient number of domain-specific lexicons and lack of involvement of domain experts such as doctors and medical practitioners.

WME: Sense, Polarity and Affinity based Concept Resource for Medical Events

no code implementations GWC 2016 Anupam Mondal, Dipankar Das, Erik Cambria, Sivaji Bandyopadhyay

In order to overcome the lack of medical corpora, we have developed a WordNet for Medical Events (WME) for identifying medical terms and their sense related information using a seed list.


Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings

no code implementations15 Sep 2022 Sooji Han, Rui Mao, Erik Cambria

Automatic depression detection on Twitter can help individuals privately and conveniently understand their mental health status in the early stages before seeing mental health professionals.

Decision Making Depression Detection

The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress

1 code implementation23 Jun 2022 Lukas Christ, Shahin Amiriparian, Alice Baird, Panagiotis Tzirakis, Alexander Kathan, Niklas Müller, Lukas Stappen, Eva-Maria Meßner, Andreas König, Alan Cowen, Erik Cambria, Björn W. Schuller

For this year's challenge, we feature three datasets: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset that contains audio-visual recordings of German football coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in which reactions of individuals to emotional stimuli have been annotated with respect to seven emotional expression intensities, and (iii) the Ulm-Trier Social Stress Test (Ulm-TSST) dataset comprising of audio-visual data labelled with continuous emotion values (arousal and valence) of people in stressful dispositions.

Emotion Recognition Humor Detection +1

Deep-Attack over the Deep Reinforcement Learning

no code implementations2 May 2022 Yang Li, Quan Pan, Erik Cambria

Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack.

Adversarial Attack reinforcement-learning

Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

no code implementations14 Jan 2022 Ranjan Satapathy, Shweta Pardeshi, Erik Cambria

The proposed approach reports baseline performances for both polarity detection and subjectivity detection.

MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare

no code implementations LREC 2022 Shaoxiong Ji, Tianlin Zhang, Luna Ansari, Jie Fu, Prayag Tiwari, Erik Cambria

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.

Pretrained Language Models

Fusing task-oriented and open-domain dialogues in conversational agents

1 code implementation9 Sep 2021 Tom Young, Frank Xing, Vlad Pandelea, Jinjie Ni, Erik Cambria

It features inter-mode contextual dependency, i. e., the dialogue turns from the two modes depend on each other.

Dialogue Generation

Multitask Balanced and Recalibrated Network for Medical Code Prediction

2 code implementations6 Sep 2021 Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen

Nevertheless, automated medical coding is still challenging because of the imbalanced class problem, complex code association, and noise in lengthy documents.

Medical Code Prediction Multi-Task Learning

Graph Routing between Capsules

no code implementations22 Jun 2021 Yang Li, Wei Zhao, Erik Cambria, Suhang Wang, Steffen Eger

Therefore, in this paper, we introduce a new capsule network with graph routing to learn both relationships, where capsules in each layer are treated as the nodes of a graph.

text-classification Text Classification

MICE: A Crosslinguistic Emotion Corpus in Malay, Indonesian, Chinese and English

no code implementations9 Jun 2021 Ng Bee Chin, Yosephine Susanto, Erik Cambria

MICE is a corpus of emotion words in four languages which is currently working progress.

Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey

no code implementations10 May 2021 Jinjie Ni, Tom Young, Vlad Pandelea, Fuzhao Xue, Erik Cambria

To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques.

Information Retrieval Question Answering

BLM-17m: A Large-Scale Dataset for Black Lives Matter Topic Detection on Twitter

no code implementations4 May 2021 Hasan Kemik, Nusret Özateş, Meysam Asgari-Chenaghlu, Erik Cambria

In this paper, our aim is to provide a dataset which covers one of the most significant human rights contradiction in recent months affected the whole world, George Floyd incident.

The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress

1 code implementation14 Apr 2021 Lukas Stappen, Alice Baird, Lukas Christ, Lea Schumann, Benjamin Sertolli, Eva-Maria Messner, Erik Cambria, Guoying Zhao, Björn W. Schuller

Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual, language, and biological signal modalities.

Emotion Recognition Multimodal Sentiment Analysis

Multitask Recalibrated Aggregation Network for Medical Code Prediction

1 code implementation2 Apr 2021 Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen

Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement.

Medical Code Prediction Representation Learning

A Survey on Personality-Aware Recommendation Systems

no code implementations28 Jan 2021 Sahraoui Dhelim, Nyothiri Aung, Mohammed Amine Bouras, Huansheng Ning, Erik Cambria

With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems.

Recommendation Systems

Multitask Learning for Emotion and Personality Detection

1 code implementation7 Jan 2021 Yang Li, Amirmohammad Kazameini, Yash Mehta, Erik Cambria

In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual.

Language Modelling Model Optimization

A Survey on Deep Reinforcement Learning for Audio-Based Applications

no code implementations1 Jan 2021 Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria

We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain.

Audio Signal Processing reinforcement-learning

Improving Zero Shot Learning Baselines with Commonsense Knowledge

no code implementations11 Dec 2020 Abhinaba Roy, Deepanway Ghosal, Erik Cambria, Navonil Majumder, Rada Mihalcea, Soujanya Poria

Zero shot learning -- the problem of training and testing on a completely disjoint set of classes -- relies greatly on its ability to transfer knowledge from train classes to test classes.

Word Embeddings Zero-Shot Learning

JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation

no code implementations SEMEVAL 2020 Ali Fadel, Mahmoud Al-Ayyoub, Erik Cambria

As for the last subtask, our models reach 16. 10 BLEU score and 1. 94 human evaluation score placing our team in the 5th and 3rd places according to these two metrics, respectively.

Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets

no code implementations COLING 2020 Frank Xing, Lorenzo Malandri, Yue Zhang, Erik Cambria

The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors.

Autonomous Driving Sentiment Analysis

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

no code implementations EMNLP (ClinicalNLP) 2020 Shaoxiong Ji, Erik Cambria, Pekka Marttinen

Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems.

BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis

1 code implementation31 May 2020 Wei Li, Wei Shao, Shaoxiong Ji, Erik Cambria

Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e. g., sentiment analysis, recommender systems, and human-robot interaction.

Emotion Recognition in Conversation

A review of sentiment analysis research in Arabic language

no code implementations25 May 2020 Oumaima Oueslati, Erik Cambria, Moez Ben HajHmida, Habib Ounelli

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention.

Arabic Sentiment Analysis Machine Translation +2

MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop

1 code implementation30 Apr 2020 Lukas Stappen, Alice Baird, Georgios Rizos, Panagiotis Tzirakis, Xinchen Du, Felix Hafner, Lea Schumann, Adria Mallol-Ragolta, Björn W. Schuller, Iulia Lefter, Erik Cambria, Ioannis Kompatsiaris

Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 is a Challenge-based Workshop focusing on the tasks of sentiment recognition, as well as emotion-target engagement and trustworthiness detection by means of more comprehensively integrating the audio-visual and language modalities.

Emotion Recognition Multimodal Sentiment Analysis

Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks

no code implementations16 Apr 2020 Shaoxiong Ji, Xue Li, Zi Huang, Erik Cambria

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment.

Deep Learning Based Text Classification: A Comprehensive Review

1 code implementation6 Apr 2020 Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao

Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference.

BIG-bench Machine Learning Classification +6

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

1 code implementation2 Feb 2020 Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu

In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.

Knowledge Graph Embedding Relational Reasoning

Recent Trends in Deep Learning Based Personality Detection

no code implementations7 Aug 2019 Yash Mehta, Navonil Majumder, Alexander Gelbukh, Erik Cambria

This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches.

BIG-bench Machine Learning Personality Trait Recognition

Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

5 code implementations ACL 2019 Wei Zhao, Haiyun Peng, Steffen Eger, Erik Cambria, Min Yang

Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes.

 Ranked #1 on Text Classification on RCV1 (P@1 metric)

General Classification Multi Label Text Classification +3

PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis

no code implementations24 Apr 2019 Ranjan Satapathy, Aalind Singh, Erik Cambria

The usage of microtext poses a considerable performance issue in concept-level sentiment analysis, since models are trained on standard words.

Sentiment Analysis

"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses

no code implementations12 Mar 2019 Mimansa Jaiswal, Sairam Tabibu, Erik Cambria

In the past few years, social media has risen as a platform where people express and share personal incidences about abuse, violence and mental health issues.

Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining

no code implementations22 Feb 2019 Rajiv Bajpai, Devamanyu Hazarika, Kunal Singh, Sruthi Gorantla, Erik Cambria, Roger Zimmerman

With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task.

Opinion Mining Sentiment Analysis

Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning

no code implementations23 Jan 2019 Haiyun Peng, Yukun Ma, Soujanya Poria, Yang Li, Erik Cambria

Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text.

Chinese Sentiment Analysis reinforcement-learning +1

Discovering Power Laws in Entity Length

no code implementations8 Nov 2018 Xiaoshi Zhong, Erik Cambria, Jagath C. Rajapakse

This paper presents a discovery that the length of the entities in various datasets follows a family of scale-free power law distributions.

DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

2 code implementations1 Nov 2018 Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, Erik Cambria

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.

Emotion Classification Emotion Recognition in Conversation +2

Concept-Based Embeddings for Natural Language Processing

no code implementations15 Jul 2018 Yukun Ma, Erik Cambria

In this work, we focus on effectively leveraging and integrating information from concept-level as well as word-level via projecting concepts and words into a lower dimensional space while retaining most critical semantics.

Automatic Speech Recognition General Classification +2

Anaphora and Coreference Resolution: A Review

no code implementations30 May 2018 Rhea Sukthanker, Soujanya Poria, Erik Cambria, Ramkumar Thirunavukarasu

Entity resolution aims at resolving repeated references to an entity in a document and forms a core component of natural language processing (NLP) research.

Coreference Resolution Entity Resolution +3

A Deep Learning Approach for Multimodal Deception Detection

no code implementations1 Mar 2018 Gangeshwar Krishnamurthy, Navonil Majumder, Soujanya Poria, Erik Cambria

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications.

Deception Detection

Discovering Bayesian Market Views for Intelligent Asset Allocation

1 code implementation27 Feb 2018 Frank Z. Xing, Erik Cambria, Lorenzo Malandri, Carlo Vercellis

Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction.

Opinion Mining Stock Market Prediction

Memory Fusion Network for Multi-view Sequential Learning

2 code implementations3 Feb 2018 Amir Zadeh, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, Louis-Philippe Morency

In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time.

Basic tasks of sentiment analysis

no code implementations18 Oct 2017 Iti Chaturvedi, Soujanya Poria, Erik Cambria

Subjectivity detection is the task of identifying objective and subjective sentences.

Aspect Extraction Sentiment Analysis

Augmenting End-to-End Dialog Systems with Commonsense Knowledge

no code implementations16 Sep 2017 Tom Young, Erik Cambria, Iti Chaturvedi, Minlie Huang, Hao Zhou, Subham Biswas

Building dialog agents that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence.

Disentangled Variational Auto-Encoder for Semi-supervised Learning

no code implementations15 Sep 2017 Yang Li, Quan Pan, Suhang Wang, Haiyun Peng, Tao Yang, Erik Cambria

The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.

Recent Trends in Deep Learning Based Natural Language Processing

3 code implementations9 Aug 2017 Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria

Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains.

Benchmarking Multimodal Sentiment Analysis

no code implementations29 Jul 2017 Erik Cambria, Devamanyu Hazarika, Soujanya Poria, Amir Hussain, R. B. V. Subramaanyam

We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities.

Emotion Recognition Multimodal Sentiment Analysis

Tensor Fusion Network for Multimodal Sentiment Analysis

no code implementations EMNLP 2017 Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, Louis-Philippe Morency

Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language.

Multimodal Sentiment Analysis

Developing a concept-level knowledge base for sentiment analysis in Singlish

no code implementations14 Jul 2017 Rajiv Bajpai, Soujanya Poria, Danyun Ho, Erik Cambria

In this paper, we present Singlish sentiment lexicon, a concept-level knowledge base for sentiment analysis that associates multiword expressions to a set of emotion labels and a polarity value.

Common Sense Reasoning Graph Mining +1

Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups

no code implementations1 Mar 2017 Ceyda Sanli, Anupam Mondal, Erik Cambria

Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time.

Decision Making Opinion Mining

SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives

no code implementations COLING 2016 Erik Cambria, Soujanya Poria, Rajiv Bajpai, Bjoern Schuller

An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions.

Dimensionality Reduction Sentiment Analysis

From Node Embedding To Community Embedding

2 code implementations31 Oct 2016 Vincent W. Zheng, Sandro Cavallari, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria

Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space.

Graph Embedding Node Classification

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