Search Results for author: Erik Cambria

Found 111 papers, 46 papers with code

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.

POS Relation

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.


SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)

no code implementations29 Feb 2024 Shivani Kumar, Md Shad Akhtar, Erik Cambria, Tanmoy Chakraborty

We present SemEval-2024 Task 10, a shared task centred on identifying emotions and finding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues.

How Interpretable are Reasoning Explanations from Prompting Large Language Models?

1 code implementation19 Feb 2024 Wei Jie Yeo, Ranjan Satapathy, Goh Siow Mong, Rick, Erik Cambria

We present a comprehensive and multifaceted evaluation of interpretability, examining not only faithfulness but also robustness and utility across multiple commonsense reasoning benchmarks.

Prompt Engineering

Plausible Extractive Rationalization through Semi-Supervised Entailment Signal

1 code implementation13 Feb 2024 Wei Jie Yeo, Ranjan Satapathy, Erik Cambria

The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative.

Natural Language Inference Question Answering

Rethinking Large Language Models in Mental Health Applications

no code implementations19 Nov 2023 Shaoxiong Ji, Tianlin Zhang, Kailai Yang, Sophia Ananiadou, Erik Cambria

Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications.

A Survey on Semantic Processing Techniques

no code implementations22 Oct 2023 Rui Mao, Kai He, Xulang Zhang, Guanyi Chen, Jinjie Ni, Zonglin Yang, Erik Cambria

We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks.

named-entity-recognition Named Entity Recognition +1

A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics

1 code implementation9 Oct 2023 Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria

This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a shift from model-centered methodologies to datacentered methodologies.

Ethics Fairness

A Comprehensive Review on Financial Explainable AI

no code implementations21 Sep 2023 Wei Jie Yeo, Wihan van der Heever, Rui Mao, Erik Cambria, Ranjan Satapathy, Gianmarco Mengaldo

The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns.

Decision Making

Large Language Models for Automated Open-domain Scientific Hypotheses Discovery

1 code implementation6 Sep 2023 Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, Erik Cambria

Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations.


A Wide Evaluation of ChatGPT on Affective Computing Tasks

no code implementations26 Aug 2023 Mostafa M. Amin, Rui Mao, Erik Cambria, Björn W. Schuller

In this work, we widely study the capabilities of the ChatGPT models, namely GPT-4 and GPT-3. 5, on 13 affective computing problems, namely aspect extraction, aspect polarity classification, opinion extraction, sentiment analysis, sentiment intensity ranking, emotions intensity ranking, suicide tendency detection, toxicity detection, well-being assessment, engagement measurement, personality assessment, sarcasm detection, and subjectivity detection.

Aspect Extraction Sarcasm Detection +1

GPTEval: A Survey on Assessments of ChatGPT and GPT-4

no code implementations24 Aug 2023 Rui Mao, Guanyi Chen, Xulang Zhang, Frank Guerin, Erik Cambria

The emergence of ChatGPT has generated much speculation in the press about its potential to disrupt social and economic systems.

EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition

no code implementations23 Jul 2023 Amirhossein Aminimehr, Amirali Molaei, Erik Cambria

Through experiments on benchmark scene classification datasets, EnTri has demonstrated superiority in terms of recognition accuracy, achieving competitive performance compared to state-of-the-art approaches, with an accuracy of 87. 69%, 75. 56%, and 99. 17% on the MIT67, SUN397, and UIUC8 datasets, respectively.

Classification Ensemble Learning +3

Can ChatGPT's Responses Boost Traditional Natural Language Processing?

1 code implementation6 Jul 2023 Mostafa M. Amin, Erik Cambria, Björn W. Schuller

In this work, we extend this by exploring if ChatGPT has novel knowledge that would enhance existing specialised models when they are fused together.

Language Modelling Sentiment Analysis

Recent Developments in Recommender Systems: A Survey

no code implementations22 Jun 2023 Yang Li, Kangbo Liu, Ranjan Satapathy, Suhang Wang, Erik Cambria

The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems.

Fairness Recommendation Systems

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

1 code implementation16 Jun 2023 Fangzhi Xu, Qika Lin, Jiawei Han, Tianzhe Zhao, Jun Liu, Erik Cambria

Firstly, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings.

Benchmarking Evidence Selection +2

Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning

1 code implementation23 May 2023 Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Chunyan Miao

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data.

Cross-Lingual NER named-entity-recognition +4

Finding the Pillars of Strength for Multi-Head Attention

2 code implementations22 May 2023 Jinjie Ni, Rui Mao, Zonglin Yang, Han Lei, Erik Cambria

Specifically, the heads of MHA were originally designed to attend to information from different representation subspaces, whereas prior studies found that some attention heads likely learn similar features and can be pruned without harming performance.

feature selection

A Review of Deep Learning for Video Captioning

no code implementations22 Apr 2023 Moloud Abdar, Meenakshi Kollati, Swaraja Kuraparthi, Farhad Pourpanah, Daniel McDuff, Mohammad Ghavamzadeh, Shuicheng Yan, Abduallah Mohamed, Abbas Khosravi, Erik Cambria, Fatih Porikli

Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction.

Dense Video Captioning Question Answering +3

Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health

no code implementations20 Apr 2023 Shaoxiong Ji, Tianlin Zhang, Kailai Yang, Sophia Ananiadou, Erik Cambria, Jörg Tiedemann

In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions.

Logical Reasoning over Natural Language as Knowledge Representation: A Survey

1 code implementation21 Mar 2023 Zonglin Yang, Xinya Du, Rui Mao, Jinjie Ni, Erik Cambria

This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation and pretrained language models as reasoners, including philosophical definition and categorization of logical reasoning, advantages of the new paradigm, benchmarks and methods, challenges of the new paradigm, possible future directions, and relation to related NLP fields.

Logical Reasoning

FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis

no code implementations5 Mar 2023 Keane Ong, Wihan van der Heever, Ranjan Satapathy, Erik Cambria, Gianmarco Mengaldo

This paper presents a novel approach for explainability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis, Pearson correlation, Granger causality & uncertainty coefficient.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT

no code implementations3 Mar 2023 Mostafa M. Amin, Erik Cambria, Björn W. Schuller

We utilise three baselines, a robust language model (RoBERTa-base), a legacy word model with pretrained embeddings (Word2Vec), and a simple bag-of-words baseline (BoW).

Language Modelling Sentiment Analysis +2

Language Models as Inductive Reasoners

1 code implementation21 Dec 2022 Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, Furu Wei

To this end, we propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts, and create a dataset termed DEER containing 1. 2k rule-fact pairs for the task, where rules and facts are written in natural language.


ConNER: Consistency Training for Cross-lingual Named Entity Recognition

1 code implementation17 Nov 2022 Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropoutbased consistency training on labeled source-language data.

Cross-Lingual NER Knowledge Distillation +3

Knowledge Representation for Conceptual, Motivational, and Affective Processes in Natural Language Communication

no code implementations26 Sep 2022 Seng-Beng Ho, Zhaoxia Wang, Boon-Kiat Quek, Erik Cambria

However, in human-robot collaborative social communication and in using natural language for delivering precise instructions to robots, a deeper representation of the conceptual, motivational, and affective processes is needed.

Machine Translation Sentence +1

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

no code implementations COLING 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 +1

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.

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.

Relation text-classification +1

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

1 code implementation4 May 2021 Hasan Kemik, Nusret Özateş, Meysam Asgari-Chenaghlu, Yang Li, 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 +1

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 Cultural Vocal Bursts Intensity Prediction +1

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 Sentence +1

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

2 code implementations6 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 General Classification +5

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 +1

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 +1

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 +2

Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities

1 code implementation8 Nov 2018 Xiaoshi Zhong, Xiang Yu, Erik Cambria, Jagath C. Rajapakse

Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts.

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 Automatic Speech Recognition (ASR) +3

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.

Benchmarking Emotion Recognition +1

Tensor Fusion Network for Multimodal Sentiment Analysis

1 code implementation 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.

Clustering Dimensionality Reduction +1

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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