Erik Cambria


2020

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Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text
Shaoxiong Ji | Erik Cambria | Pekka Marttinen
Proceedings of the 3rd Clinical Natural Language Processing Workshop

Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems. The emergence of deep models in natural language processing has boosted the development of automatic assignment methods. However, recent advanced neural architectures with flat convolutions or multi-channel feature concatenation ignore the sequential causal constraint within a text sequence and may not learn meaningful clinical text representations, especially for lengthy clinical notes with long-term sequential dependency. This paper proposes a Dilated Convolutional Attention Network (DCAN), integrating dilated convolutions, residual connections, and label attention, for medical code assignment. It adopts dilated convolutions to capture complex medical patterns with a receptive field which increases exponentially with dilation size. Experiments on a real-world clinical dataset empirically show that our model improves the state of the art.

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JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation
Ali Fadel | Mahmoud Al-Ayyoub | Erik Cambria
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper, we describe our team’s (JUSTers) effort in the Commonsense Validation and Explanation (ComVE) task, which is part of SemEval2020. We evaluate five pre-trained Transformer-based language models with various sizes against the three proposed subtasks. For the first two subtasks, the best accuracy levels achieved by our models are 92.90% and 92.30%, respectively, placing our team in the 12th and 9th places, respectively. 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. The latter is only 0.16 away from the 1st place human evaluation score.

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Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets
Frank Xing | Lorenzo Malandri | Yue Zhang | Erik Cambria
Proceedings of the 28th International Conference on Computational Linguistics

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. Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving. This study, instead of proposing any “new model”, investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors. These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.

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Proceedings of the First International Workshop on Natural Language Processing Beyond Text
Giuseppe Castellucci | Simone Filice | Soujanya Poria | Erik Cambria | Lucia Specia
Proceedings of the First International Workshop on Natural Language Processing Beyond Text

2019

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MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Soujanya Poria | Devamanyu Hazarika | Navonil Majumder | Gautam Naik | Erik Cambria | Rada Mihalcea
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http://affective-meld.github.io.

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Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
Wei Zhao | Haiyun Peng | Steffen Eger | Erik Cambria | Min Yang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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: (i) an agreement score to evaluate the performance of routing processes at instance-level; (ii) an adaptive optimizer to enhance the reliability of routing; (iii) capsule compression and partial routing to improve the scalability of capsule networks. We validate our approach on two NLP tasks, namely: multi-label text classification and question answering. Experimental results show that our approach considerably improves over strong competitors on both tasks. In addition, we gain the best results in low-resource settings with few training instances.

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Business Taxonomy Construction Using Concept-Level Hierarchical Clustering
Haodong Bai | Frank Xing | Erik Cambria | Win-Bin Huang
Proceedings of the First Workshop on Financial Technology and Natural Language Processing

2018

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Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos
Devamanyu Hazarika | Soujanya Poria | Amir Zadeh | Erik Cambria | Louis-Philippe Morency | Roger Zimmermann
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Emotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, we address recognizing utterance-level emotions in dyadic conversational videos. We propose a deep neural framework, termed Conversational Memory Network (CMN), which leverages contextual information from the conversation history. In particular, CMN uses multimodal approach comprising audio, visual and textual features with gated recurrent units to model past utterances of each speaker into memories. These memories are then merged using attention-based hops to capture inter-speaker dependencies. Experiments show a significant improvement of 3 − 4% in accuracy over the state of the art.

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Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis
Devamanyu Hazarika | Soujanya Poria | Prateek Vij | Gangeshwar Krishnamurthy | Erik Cambria | Roger Zimmermann
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.

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ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
Devamanyu Hazarika | Soujanya Poria | Rada Mihalcea | Erik Cambria | Roger Zimmermann
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Emotion recognition in conversations is crucial for building empathetic machines. Present works in this domain do not explicitly consider the inter-personal influences that thrive in the emotional dynamics of dialogues. To this end, we propose Interactive COnversational memory Network (ICON), a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. Such memories generate contextual summaries which aid in predicting the emotional orientation of utterance-videos. Our model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets.

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IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
Navonil Majumder | Soujanya Poria | Alexander Gelbukh | Md. Shad Akhtar | Erik Cambria | Asif Ekbal
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6% on average in two distinct domains: restaurant and laptop.

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CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
Devamanyu Hazarika | Soujanya Poria | Sruthi Gorantla | Erik Cambria | Roger Zimmermann | Rada Mihalcea
Proceedings of the 27th International Conference on Computational Linguistics

The literature in automated sarcasm detection has mainly focused on lexical-, syntactic- and semantic-level analysis of text. However, a sarcastic sentence can be expressed with contextual presumptions, background and commonsense knowledge. In this paper, we propose a ContextuAl SarCasm DEtector (CASCADE), which adopts a hybrid approach of both content- and context-driven modeling for sarcasm detection in online social media discussions. For the latter, CASCADE aims at extracting contextual information from the discourse of a discussion thread. Also, since the sarcastic nature and form of expression can vary from person to person, CASCADE utilizes user embeddings that encode stylometric and personality features of users. When used along with content-based feature extractors such as convolutional neural networks, we see a significant boost in the classification performance on a large Reddit corpus.

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

Analyzing human multimodal language is an emerging area of research in NLP. Intrinsically this language is multimodal (heterogeneous), sequential and asynchronous; it consists of the language (words), visual (expressions) and acoustic (paralinguistic) modalities all in the form of asynchronous coordinated sequences. From a resource perspective, there is a genuine need for large scale datasets that allow for in-depth studies of this form of language. In this paper we introduce CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI), the largest dataset of sentiment analysis and emotion recognition to date. Using data from CMU-MOSEI and a novel multimodal fusion technique called the Dynamic Fusion Graph (DFG), we conduct experimentation to exploit how modalities interact with each other in human multimodal language. Unlike previously proposed fusion techniques, DFG is highly interpretable and achieves competative performance when compared to the previous state of the art.

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Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)
Amir Zadeh | Paul Pu Liang | Louis-Philippe Morency | Soujanya Poria | Erik Cambria | Stefan Scherer
Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)

2017

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Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules
Xiaoshi Zhong | Aixin Sun | Erik Cambria
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Extracting time expressions from free text is a fundamental task for many applications. We analyze the time expressions from four datasets and find that only a small group of words are used to express time information, and the words in time expressions demonstrate similar syntactic behaviour. Based on the findings, we propose a type-based approach, named SynTime, to recognize time expressions. Specifically, we define three main syntactic token types, namely time token, modifier, and numeral, to group time-related regular expressions over tokens. On the types we design general heuristic rules to recognize time expressions. In recognition, SynTime first identifies the time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a light-weight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text of different types and of different domains. Experiment on benchmark datasets and tweets data shows that SynTime outperforms state-of-the-art methods.

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Context-Dependent Sentiment Analysis in User-Generated Videos
Soujanya Poria | Erik Cambria | Devamanyu Hazarika | Navonil Majumder | Amir Zadeh | Louis-Philippe Morency
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10% performance improvement over the state of the art and high robustness to generalizability.

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Tensor Fusion Network for Multimodal Sentiment Analysis
Amir Zadeh | Minghai Chen | Soujanya Poria | Erik Cambria | Louis-Philippe Morency
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Networks, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-of-the-art approaches for both multimodal and unimodal sentiment analysis.

2016

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Label Embedding for Zero-shot Fine-grained Named Entity Typing
Yukun Ma | Erik Cambria | Sa Gao
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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 a presenter and a person. Existing approaches to named entity typing cannot work with a growing type set and fails to recognize entity mentions of unseen types. In this paper, we present a label embedding method that incorporates prototypical and hierarchical information to learn pre-trained label embeddings. In addition, we adapt a zero-shot learning framework that can predict both seen and previously unseen entity types. We perform evaluation on three benchmark datasets with two settings: 1) few-shots recognition where all types are covered by the training set; and 2) zero-shot recognition where fine-grained types are assumed absent from training set. Results show that prior knowledge encoded using our label embedding methods can significantly boost the performance of classification for both cases.

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A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
Soujanya Poria | Erik Cambria | Devamanyu Hazarika | Prateek Vij
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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 date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network’s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.

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SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives
Erik Cambria | Soujanya Poria | Rajiv Bajpai | Bjoern Schuller
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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. This allows humans to adapt easily to novel situations where AI fails catastrophically due to a lack of situation-specific rules and generalization capabilities. Commonsense knowledge also provides background information that enables humans to successfully operate in social situations where such knowledge is typically assumed. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. Previous versions of SenticNet were focused on collecting this kind of knowledge for sentiment analysis but they were heavily limited by their inability to generalize. SenticNet 4 overcomes such limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.

2015

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Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis
Soujanya Poria | Erik Cambria | Alexander Gelbukh
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the third International Workshop on Natural Language Processing for Social Media
Shou-de Lin | Lun-Wei Ku | Cheng-Te Li | Erik Cambria
Proceedings of the third International Workshop on Natural Language Processing for Social Media

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SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning
Prerna Chikersal | Soujanya Poria | Erik Cambria
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)
Shou-de Lin | Lun-Wei Ku | Erik Cambria | Tsung-Ting Kuo
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

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A Rule-Based Approach to Aspect Extraction from Product Reviews
Soujanya Poria | Erik Cambria | Lun-Wei Ku | Chen Gui | Alexander Gelbukh
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

2012

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Affective Common Sense Knowledge Acquisition for Sentiment Analysis
Erik Cambria | Yunqing Xia | Amir Hussain
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Thanks to the advent of Web 2.0, the potential for opinion sharing today is unmatched in history. Making meaning out of the huge amount of unstructured information available online, however, is extremely difficult as web-contents, despite being perfectly suitable for human consumption, still remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, affective common sense knowledge is needed. In sentic computing, the general common sense knowledge contained in ConceptNet is usually exploited to spread affective information from selected affect seeds to other concepts. In this work, besides exploiting the emotional content of the Open Mind corpus, we also collect new affective common sense knowledge through label sequential rules, crowd sourcing, and games-with-a-purpose techniques. In particular, we develop Open Mind Common Sentics, an emotion-sensitive IUI that serves both as a platform for affective common sense acquisition and as a publicly available NLP tool for extracting the cognitive and affective information associated with short texts.

2011

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Taking Refuge in Your Personal Sentic Corner
Erik Cambria | Amir Hussain | Chris Eckl
Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011)

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Enriching Social Communication through Semantics and Sentics
Praphul Chandra | Erik Cambria | Alvin Pradeep
Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011)