no code implementations • LREC 2022 • Erik Cambria, Qian Liu, Sergio Decherchi, Frank Xing, Kenneth Kwok
In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society.
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.
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.
no code implementations • 11 Jun 2024 • Shahin Amiriparian, Lukas Christ, Alexander Kathan, Maurice Gerczuk, Niklas Müller, Steffen Klug, Lukas Stappen, Andreas König, Erik Cambria, Björn Schuller, Simone Eulitz
The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems: In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of individuals such as assertiveness, dominance, likability, and sincerity based on the provided audio-visual data.
1 code implementation • 19 May 2024 • Fanfan Wang, Heqing Ma, Jianfei Yu, Rui Xia, Erik Cambria
The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social interactions.
2 code implementations • 26 Apr 2024 • Zheng Lian, Haiyang Sun, Licai Sun, Zhuofan Wen, Siyuan Zhang, Shun Chen, Hao Gu, Jinming Zhao, Ziyang Ma, Xie Chen, Jiangyan Yi, Rui Liu, Kele Xu, Bin Liu, Erik Cambria, Guoying Zhao, Björn W. Schuller, JianHua Tao
In addition to expanding the dataset size, we introduce a new track around open-vocabulary emotion recognition.
no code implementations • 29 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.
1 code implementation • 19 Feb 2024 • Wei Jie Yeo, Ranjan Satapathy, Rick Siow Mong Goh, 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.
1 code implementation • 13 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.
no code implementations • 19 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.
no code implementations • 22 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.
1 code implementation • 9 Oct 2023 • Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria
Secondly, we conduct a comparison between the previous PLMs and the latest LLMs, as well as comparing various LLMs with each other.
no code implementations • 21 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.
1 code implementation • 6 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.
1 code implementation • 26 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.
no code implementations • 24 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.
no code implementations • 23 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.
no code implementations • 19 Jul 2023 • Amirhossein Aminimehr, Pouya Khani, Amirali Molaei, Amirmohammad Kazemeini, Erik Cambria
Additionally, objects and regions of the input image that are relevant to the model prediction are frequently not entirely differentiated by heatmaps.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
1 code implementation • 6 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.
no code implementations • 22 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.
1 code implementation • 16 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.
1 code implementation • 23 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.
2 code implementations • 22 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.
1 code implementation • 5 May 2023 • Lukas Christ, Shahin Amiriparian, Alice Baird, Alexander Kathan, Niklas Müller, Steffen Klug, Chris Gagne, Panagiotis Tzirakis, Eva-Maria Meßner, Andreas König, Alan Cowen, Erik Cambria, Björn W. Schuller
Participants predict the presence of spontaneous humour in a cross-cultural setting.
no code implementations • 22 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.
no code implementations • 20 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.
3 code implementations • 18 Apr 2023 • Zheng Lian, Haiyang Sun, Licai Sun, Kang Chen, Mingyu Xu, Kexin Wang, Ke Xu, Yu He, Ying Li, Jinming Zhao, Ye Liu, Bin Liu, Jiangyan Yi, Meng Wang, Erik Cambria, Guoying Zhao, Björn W. Schuller, JianHua Tao
The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia.
1 code implementation • 21 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.
no code implementations • 7 Mar 2023 • Jinjie Ni, Yukun Ma, Wen Wang, Qian Chen, Dianwen Ng, Han Lei, Trung Hieu Nguyen, Chong Zhang, Bin Ma, Erik Cambria
Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models.
no code implementations • 5 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) +2
no code implementations • 3 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).
1 code implementation • 21 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.
1 code implementation • 17 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.
no code implementations • 26 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.
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.
no code implementations • 14 Jul 2022 • Alice Baird, Panagiotis Tzirakis, Gauthier Gidel, Marco Jiralerspong, Eilif B. Muller, Kory Mathewson, Björn Schuller, Erik Cambria, Dacher Keltner, Alan Cowen
The first, ExVo-MultiTask, requires participants to train a multi-task model to recognize expressed emotions and demographic traits from vocal bursts.
1 code implementation • 23 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.
2 code implementations • 3 May 2022 • Alice Baird, Panagiotis Tzirakis, Gauthier Gidel, Marco Jiralerspong, Eilif B. Muller, Kory Mathewson, Björn Schuller, Erik Cambria, Dacher Keltner, Alan Cowen
ExVo 2022, includes three competition tracks using a large-scale dataset of 59, 201 vocalizations from 1, 702 speakers.
no code implementations • 2 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.
no code implementations • 14 Jan 2022 • Ranjan Satapathy, Shweta Pardeshi, Erik Cambria
The proposed approach reports baseline performances for both polarity detection and subjectivity detection.
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.
1 code implementation • 9 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.
Ranked #1 on Dialogue Generation on FusedChat
2 code implementations • 6 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.
1 code implementation • ACL 2022 • Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao
Data augmentation is an effective solution to data scarcity in low-resource scenarios.
1 code implementation • 25 Jul 2021 • Lukas Stappen, Lea Schumann, Benjamin Sertolli, Alice Baird, Benjamin Weigel, Erik Cambria, Björn W. Schuller
With this in mind, the MuSe-Toolbox provides the functionality to run exhaustive searches for meaningful class clusters in the continuous gold standards.
no code implementations • 22 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.
no code implementations • 9 Jun 2021 • Ng Bee Chin, Yosephine Susanto, Erik Cambria
MICE is a corpus of emotion words in four languages which is currently working progress.
no code implementations • 10 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.
1 code implementation • 4 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.
1 code implementation • 14 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.
1 code implementation • 2 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.
no code implementations • 3 Mar 2021 • Kia Dashtipour, Mandar Gogate, Erik Cambria, Amir Hussain
Most recent works on sentiment analysis have exploited the text modality.
no code implementations • 28 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.
1 code implementation • 7 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.
no code implementations • 1 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.
no code implementations • 11 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.
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
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.
no code implementations • 3 Oct 2020 • Amirmohammad Kazameini, Samin Fatehi, Yash Mehta, Sauleh Eetemadi, Erik Cambria
In this work, we present a novel deep learning-based approach for automated personality detection from 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.
1 code implementation • 31 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.
Ranked #39 on Emotion Recognition in Conversation on IEMOCAP
no code implementations • 25 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.
1 code implementation • 30 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.
no code implementations • 16 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.
2 code implementations • 6 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.
no code implementations • 21 Feb 2020 • David Camacho, Àngel Panizo-LLedot, Gema Bello-Orgaz, Antonio Gonzalez-Pardo, Erik Cambria
Social network based applications have experienced exponential growth in recent years.
1 code implementation • 2 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.
no code implementations • 23 Oct 2019 • Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, Zi Huang
Suicide is a critical issue in modern society.
no code implementations • 7 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.
1 code implementation • WS 2019 • Haodong Bai, Frank Z. Xing, Erik Cambria, Win-Bin Huang
Business taxonomies are indispensable tools for investors to do equity research and make professional decisions.
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)
no code implementations • 22 May 2019 • Nidhi Mishra, Manoj Ramanathan, Ranjan Satapathy, Erik Cambria, Nadia Magnenat-Thalmann
In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment.
no code implementations • 24 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.
no code implementations • 12 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.
no code implementations • 22 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.
no code implementations • 23 Jan 2019 • Navonil Majumder, Soujanya Poria, Haiyun Peng, Niyati Chhaya, Erik Cambria, Alexander Gelbukh
We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa.
no code implementations • 23 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.
1 code implementation • 8 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.
2 code implementations • 1 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.
Ranked #3 on Emotion Recognition in Conversation on SEMAINE
Emotion Classification Emotion Recognition in Conversation +2
no code implementations • 16 Oct 2018 • Xiaoshi Zhong, Erik Cambria, Jagath C. Rajapakse
Most previous research treats named entity extraction and classification as an end-to-end task.
8 code implementations • ACL 2019 • Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, Rada Mihalcea
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
1 code implementation • EMNLP 2018 • Navonil Majumder, Soujanya Poria, Alex Gelbukh, er, Md. Shad Akhtar, Erik Cambria, Asif Ekbal
Sentiment analysis has immense implications in e-commerce through user feedback mining.
1 code implementation • EMNLP 2018 • Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, Roger Zimmermann
Emotion recognition in conversations is crucial for building empathetic machines.
Ranked #50 on Emotion Recognition in Conversation on IEMOCAP
Emotion Recognition in Conversation General Classification +2
no code implementations • 12 Sep 2018 • Ikhlas Alhussien, Erik Cambria, Zhang NengSheng
Commonsense knowledge is paramount to enable intelligent systems.
no code implementations • 15 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
no code implementations • ACL 2018 • AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, Louis-Philippe Morency
Analyzing human multimodal language is an emerging area of research in NLP.
Ranked #11 on Multimodal Sentiment Analysis on CMU-MOSEI (using extra training data)
1 code implementation • NAACL 2018 • Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann
Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +4
1 code implementation • NAACL 2018 • Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, Roger Zimmermann
Emotion recognition in conversations is crucial for the development of empathetic machines.
Ranked #52 on Emotion Recognition in Conversation on IEMOCAP
no code implementations • 30 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.
1 code implementation • COLING 2018 • Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, Rada Mihalcea
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text.
Ranked #1 on Sarcasm Detection on SARC (all-bal)
no code implementations • The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) 2018 • Yukun Ma, Haiyun Peng, Erik Cambria
Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding.
Ranked #4 on Aspect-Based Sentiment Analysis (ABSA) on Sentihood
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3
no code implementations • 19 Mar 2018 • Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Erik Cambria, Alexander Gelbukh, Amir Hussain
We compile baselines, along with dataset split, for multimodal sentiment analysis.
no code implementations • 1 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.
1 code implementation • 27 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.
2 code implementations • 3 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.
2 code implementations • 3 Feb 2018 • Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, Louis-Philippe Morency
AI must understand each modality and the interactions between them that shape human communication.
Ranked #10 on Multimodal Sentiment Analysis on MOSI
no code implementations • 18 Oct 2017 • Iti Chaturvedi, Soujanya Poria, Erik Cambria
Subjectivity detection is the task of identifying objective and subjective sentences.
no code implementations • 16 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.
no code implementations • 15 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.
3 code implementations • 9 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.
no code implementations • 29 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.
2 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.
no code implementations • 14 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.
2 code implementations • ACL 2017 • Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Navonil Majumder, Amir Zadeh, Louis-Philippe Morency
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
Ranked #3 on Emotion Recognition in Conversation on CPED
Emotion Recognition in Conversation General Classification +4
1 code implementation • ACL 2017 • Xiaoshi Zhong, Aixin Sun, Erik Cambria
Extracting time expressions from free text is a fundamental task for many applications.
no code implementations • 1 Mar 2017 • Ceyda Sanli, Anupam Mondal, Erik Cambria
Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time.
no code implementations • COLING 2016 • Yukun Ma, Erik Cambria, Sa Gao
Named entity typing is the task of detecting the types of a named entity in context.
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.
2 code implementations • 31 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.
3 code implementations • COLING 2016 • Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Prateek Vij
Sarcasm detection is a key task for many natural language processing tasks.
no code implementations • LREC 2012 • Erik Cambria, Yunqing Xia, Amir Hussain
Thanks to the advent of Web 2. 0, the potential for opinion sharing today is unmatched in history.