Many NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label. In this paper we focus on a popular class of learning problems, sequence prediction applied to several sentiment analysis tasks, and suggest a modular learning approach in which different sub-tasks are learned using separate functional modules, combined to perform the final task while sharing information. Our experiments show this approach helps constrain the learning process and can alleviate some of the supervision efforts.
Grammar Detection, also referred to as Parts of Speech Tagging of raw text, is considered an underlying building block of the various Natural Language Processing pipelines like named entity recognition, question answering, and sentiment analysis. In short, forgiven a sentence, Parts of Speech tagging is the task of specifying and tagging each word of a sentence with nouns, verbs, adjectives, adverbs, and more. Sentiment Analysis may well be a procedure accustomed to determining if a given sentence's emotional tone is neutral, positive or negative. To assign polarity scores to the thesis or ent…
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL)（华中科技大学与平安财产保险研究院联合实验室）；Beijing Institute of Technology（北京理工大学）；Ping An Property & Casualty Insurance company of China, Ltd（中国平安财产保险股份有限公司）
Sentiment analysis has been an active area of research in the past two decades and recently, with the advent of social media, there has been an increasing demand for sentiment analysis on social media texts. Since the social media texts are not in one language and are largely code-mixed in nature, the traditional sentiment classification models fail to produce acceptable results. This paper tries to solve this very research problem and uses bi-directional LSTMs along with language tagging, to facilitate sentiment tagging of code-mixed Tamil texts that have been extracted from social media. The…
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）
Sentiment Analysis of code-mixed text has diversified applications in opinion mining ranging from tagging user reviews to identifying social or political sentiments of a sub-population. In this paper, we present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis of code-mixed tweets. While the CNN component helps in the classification of positive and negative tweets, the self-attention based LSTM, helps in the classification of neutral tweets, because of its ability to identify correct sentiment among multiple sentiment bearing units…
Information, Signal and Artificial Intelligence Lab（信息、信号与人工智能实验室）；University of Mons（蒙斯大学）
Understanding expressed sentiment and emotions are two crucial factors in human multimodal language. This paper describes a Transformer-based joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment Analysis. In addition to use the Transformer architecture, our approach relies on a modular co-attention and a glimpse layer to jointly encode one or more modalities. The proposed solution has also been submitted to the ACL20: Second Grand-Challenge on Multimodal Language to be evaluated on the CMU-MOSEI dataset. The code to replicate the presented experiments is open-source: https:/…
Sichuan University（四川大学）；The University of Adelaide（阿德莱德大学）；University of Electronic Science and Technology of China（电子科技大学）；Tencent（腾讯）
Aspect Sentiment Triplet Extraction (ASTE) aims to extract the spans of aspect, opinion, and their sentiment relations as sentiment triplets. Existing works usually formulate the span detection as a 1D token tagging problem, and model the sentiment recognition with a 2D tagging matrix of token pairs. Moreover, by leveraging the token representation of Pretrained Language Encoders (PLEs) like BERT, they can achieve better performance. However, they simply leverage PLEs as feature extractors to build their modules but never have a deep look at what specific knowledge does PLEs contain. In this p…
Singapore University of Technology and Design（新加坡科技设计大学）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；ByteDance（字节跳动）
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach. However, how to effectively design a tagging approach to extract the …
Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment. Existing research efforts are majorly tagging-based. Among the methods taking a sequence tagging approach, some fail to capture the strong interdependence between the three opinion factors, whereas others fall short of identifying triplets with overlapping aspect/opinion spans. A recent grid tagging approach on the other hand fails to capture the span-level s…
MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Academy of Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Shanghai Jiao Tong University（上海交通大学）；School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）
Food reviews and recommendations have always been important for online food service websites. However, reviewing and recommending food is not simple as it is likely to be overwhelmed by disparate contexts and meanings. In this paper, we use different deep learning approaches to address the problems of sentiment analysis, automatic review tag generation, and retrieval of food reviews. We propose to develop a web-based food review system at Nanyang Technological University (NTU) named NTU Food Hunter, which incorporates different deep learning approaches that help users with food selection. Firs…
Elhuyar Foundation（埃尔维亚尔基金会）；University of the Basque Country (UPV/EHU)（巴斯克大学）
本文提出了一个有监督的基于方面的情感分析（Aspect Based Sentiment Analysis，ABSA）系统。我们的目标是开发一个模块化平台，通过更换模块或添加新功能，可以轻松地进行实验。我们在意见目标提取（OTE）任务（插槽2）中使用现成的序列标签获得最佳结果。目标极性分类（槽3）通过多类SVM算法来解决，该算法包括基于词汇的特征，诸如从域和开放极性词汇获得的极性值。该系统获得的餐厅和笔记本电脑域分别为0.70和0.73的准确度，并执行第二个最好的域外酒店，达到0.80的准确度。
Data Science Approaches to Evaluating Honours Candidates
Ministry of Justice（司法部）；Joint Counter-Terrorism Prisons and Probation Hub（联合反恐监狱与缓刑中心）；Arndit Ltd.（阿恩迪特有限公司）；Indian Institute of Technology Delhi（印度德里理工学院）；Kainos（凯诺斯公司）
T-T: Table Transformer for Tagging-based Aspect Sentiment Triplet Extraction
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；School of Cyber Science and Technology, Beihang University（北航计算机科学与技术学院）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；College of Systems Engineering, National University of Defense Technology（国防科学技术大学系统工程学院）；Department of Computer Science, University of Illinois at Chicago（伊利诺伊大学香槟分校计算机科学系）
Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models
King Fahd University of Petroleum and Minerals（法赫德国王石油与矿业大学）；ARBML；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（沙特数据与人工智能局-法赫德国王石油与矿业大学人工智能联合研究中心）；Volvo Cars R&D Tech Center（沃尔沃汽车研发技术中心）
In the era of deep learning, modeling for most NLP tasks has converged to several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, NER, Chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. With the rapid progress of pre-trained language models, recent years have observed a rising trend of Paradigm Shift, which is solving one NLP task by reformulating it as another one. Paradigm shift has achieved great success on many tasks, becoming a promising way to improve model performance.…
