pwc-archive/papers-with-abstracts huggingface dataset:
Mapping and impact assessment of phenomenon-oriented research fields: The example of migration research
Authors: Liane Rothenberger, Muhammad Qasim Pasta, Daniel Mayerhoffer
Date: 2022-12-01 00:00:00
Proceeding: Quantitative Science Studies 2022 12
Tasks: Articles

Research that is not explicitly bound to a distinct discipline has not yet gained much acknowledgment with regard to research impact assessment and mapping of the respective research field. In this article, we provide a suggestion for new impact metrics taking the example of migration research as a phenomenon-oriented research field. Therewith, research merit is made comparable and is calculated irrespective of discipline. We show how the field of migration studies evolved, apply our new metrics and give insight into impact factors, numbers of citations of articles, and authors, as well as journals. Further, we present a field-related collaboration network that indicates a rather disconnected community. However, collaborations between researchers are on the rise. In our conclusion, we argue that there is a need for further assessment of research impact within other phenomenon-oriented research fields.
PanopTOP: a framework for generating viewpoint-invariant human pose estimation datasets
Authors: Nicola Garau, Giulia Martinelli, Piotr Bròdka, Niccolò Bisagno, Nicola Conci
Date: 2021-10-11 00:00:00
Proceeding: ICCV 2021 2021 10
Tasks: Pose Estimation, Pose Retrieval, Retrieval

Human pose estimation (HPE) from RGB and depth images has recently experienced a push for viewpoint-invariant and scale-invariant pose retrieval methods. Current methods fail to generalize to unconventional viewpoints due to the lack of viewpoint-invariant data at training time. Existing datasets do not provide multiple-viewpoint observations and mostly focus on frontal views. In this work, we introduce PanopTOP, a fully automatic framework for the generation of semi-synthetic RGB and depth samples with 2D and 3D ground truth of pedestrian poses from multiple arbitrary viewpoints. Starting from the Panoptic Dataset [15], we use the PanopTOP framework to generate the PanopTOP31K dataset, consisting of 31K images from 23 different subjects recorded from diverse and challenging viewpoints, also including the top-view. Finally, we provide baseline results and cross-validation tests for our dataset, demonstrating how it is possible to generalize from the semi-synthetic to the real-world domain. The dataset and the code will be made publicly available upon acceptance.
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms
Authors: Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao
Date: 2017-02-12 00:00:00
Proceeding: AAAI 2017 2
Tasks: Extract Aspect, Sentence

The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multi-layer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves state-of-the-art performances compared with several baselines.
Unsupervised Neural Aspect Extraction with Sememes
Authors: Ling Luo, Xiang Ao, Yan Song, Jinyao Li, Xiaopeng Yang, Qing He, Dong Yu
Date: 2019-08-01 00:00:00
Proceeding: IJCAI 2019 8
Tasks: Aspect Extraction, Aspect Term Extraction and Sentiment Classification, Sentence

Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts. To enhance the performance on aspect extraction, leveraging lexical semantic resources is a possible solution to such challenge. In this paper, we present an unsupervised neural framework that leverages sememes to enhance lexical semantics. The overall framework is analogous to an autoenoder which reconstructs sentence representations and learns aspects by latent variables. Two models that form sentence representations are proposed by exploiting sememes via (1) a hierarchical attention; (2) a context-enhanced attention. Experiments on two real-world datasets demonstrate the validity and the effectiveness of our models, which significantly outperforms existing baselines.
Representation Learning for Aspect Category Detection in Online Reviews
Authors: Xinjie Zhou, Xiaojun Wan, Jianguo Xiao
Date: 2015-02-09 00:00:00
Proceeding: AAAI 2015 2
Tasks: Aspect Category Detection, Decision Making, Feature Engineering, Opinion Mining, Representation Learning, Sentence, Sentiment Analysis

User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e.g., “food” and “service” in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches leverage hand-crafted features and a classification algorithm to accomplish the task. The crucial step to achieve better performance is feature engineering which consumes much human effort and may be unstable when the product domain changes. In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection. Specifically, a semi-supervised word embedding algorithm is first proposed to obtain continuous word representations on a large set of reviews with noisy labels. Afterwards, we propose to generate deeper and hybrid features through neural networks stacked on the word vectors. A logistic regression classifier is finally trained with the hybrid features to predict the aspect category. The experiments are carried out on a benchmark dataset released by SemEval-2014. Our approach achieves the state-of-the-art performance and outperforms the best participating team as well as a few strong baselines.
LICD: A Language-Independent Approach for Aspect Category Detection
Authors: Erfan Ghadery, Sajad Movahedi, Masoud Jalili Sabet, Heshaam Faili, Azadeh Shakery
Date: 2019-04-07 00:00:00
Proceeding: ECIR 2019 4
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Detection, Aspect Term Extraction and Sentiment Classification, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentiment Analysis, Text Matching

Aspect-based sentiment analysis (ABSA) deals with processing and summarizing customer reviews and has been a topic of interest in recent years. Given a set of predefined categories, Aspect Category Detection (ACD), as a subtask of ABSA, aims to assign a subset of these categories to a given review sentence. Thanks to the existence of websites such as Yelp and TripAdvisor, there exist a huge amount of reviews in several languages, and therefore the need for language-independent methods in this task seems necessary. In this paper, we propose Language-Independent Category Detector (LICD), a supervised method based on text matching without the need for any language-specific tools and hand-crafted features for identifying aspect categories. For a given sentence, our proposed method performs ACD based on two hypotheses: First, a category should be assigned to a sentence if there is a high semantic similarity between the sentence and a set of representative words of that category. Second, a category should be assigned to a sentence if sentences with high semantic and structural similarity to that sentence belong to that category. To apply the former hypothesis, we used soft cosine measure, and for the latter, word mover’s distance measure is utilized. Using these two measures, for a given sentence we calculate a set of similarity scores as features for a one-vs-all logistic regression classifier per category. Experimental results on the multilingual SemEval-2016 datasets in the restaurant domain demonstrate that our approach outperforms baseline methods in English, Russian, and Dutch languages, and obtains competitive results with the strong deep neural network-based baselines in French, Turkish, and Spanish languages.
Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks
Authors: Jianfei Yu, Jing Jiang, Rui Xia
Date: 2018-10-10 00:00:00
Proceeding: IEEE 2018 10
Tasks: Aspect Term Extraction and Sentiment Classification, Multi-Task Learning, Opinion Mining, Term Extraction

Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic constraints among aspect term extraction and opinion term extraction to uncover their intra-task and inter-task relationship, which seeks an optimal solution over the neural predictions for both tasks. Extensive evaluations on three benchmark datasets demonstrate that our global inference approach is able to bring consistent improvements over several base models in different scenarios.
Task Independent Capsule-Based Agents for Deep Q-Learning
Authors: Akash Singh, Tom De Schepper, Kevin Mets, Peter Hellinckx, Jose ́ Oramas, Steven  Latre ́
Date: 2022-01-11 00:00:00
Proceeding: Benelux Conference on Artificial Intelligence 2022 1
Tasks: Deep Reinforcement Learning, Object Recognition, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, Capsule Networks (CapsNets) have achieved promising results in tasks such as object recognition thanks to their invariance characteristics towards pose and lighting. They have been proposed as an alternative to relational insensitive and translation invariant Convolutional Neural Networks (CNN). It has been empirically proven that CapsNets are capable of achieving competitive performance while requiring significantly fewer parameters. This is a desirable characteristic for Deep reinforcement learning which is known to be sample-inefficient during training. In this paper, we propose DCapsQN, a task-independent CapsNets-based architecture in the deep reinforcement learning setting. We experiment in the model-free reinforcement learning setting, more specifically in Deep Q-Learning using the Atari suite as the testbed of our analysis. To the best of our knowledge, this work constitutes the first CapsNets-based deep reinforcement learning architecture to learn state-action value functions without the need for task-specific adaptation. Our results show that, in this setting, DCapsQN requires 92% fewer parameters than the baseline. Moreover, despite their smaller size, the DCapsQN provides significant boosts in performance (score), ranging between 10%–77% while further stabilizing the Deep Q-Learning. This is supported by our empirical results which shows that DCapsQN agents outperform the benchmark Double-DQN agent, with Prioritized experience replay, in eight out of the nine selected environments.
Target-dependent twitter sentiment classification with rich automatic features
Authors: Duy-Tin Vo, Yue Zhang
Date: 2015-07-25 00:00:00
Proceeding: IJCAI 2015 7
Tasks: Classification, Sentiment Analysis, Sentiment Classification

Target-dependent sentiment analysis on Twitter has attracted increasing research attention. Most previous work relies on syntax, such as automatic parse trees, which are subject to noise for informal text such as tweets. In this paper, we show that competitive results can be achieved without the use of syntax, by extracting a rich set of automatic features. In particular, we split a tweet into a left context and a right context according to a given target, using distributed word representations and neural pooling functions to extract features. Both sentiment-driven and standard embeddings are used, and a rich set of neural pooling functions are explored. Sentiment lexicons are used as an additional source of information for feature extraction. In standard evaluation, the conceptually simple method gives a 4.8% absolute improvement over the state-of-the-art on three-way targeted sentiment classification, achieving the best reported results for this task.
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network
Authors: Jiajun Cheng, Shenglin Zhao, Jiani Zhang, Irwin King, Xin Zhang, Hui Wang
Date: 2017-11-06 00:00:00
Proceeding: CIKM 2017 11
Tasks: Classification, Sentence, Sentiment Analysis, Sentiment Classification

Aspect-level sentiment classification is a fine-grained sentiment analysis task, which aims to predict the sentiment of a text in different aspects. One key point of this task is to allocate the appropriate sentiment words for the given aspect.Recent work exploits attention neural networks to allocate sentiment words and achieves the state-of-the-art performance. However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect. To solve this problem, we propose a HiErarchical ATtention (HEAT) network for aspect-level sentiment classification. The HEAT network contains a hierarchical attention module, consisting of aspect attention and sentiment attention. The aspect attention extracts the aspect-related information to guide the sentiment attention to better allocate aspect-specific sentiment words of the text. Moreover, the HEAT network supports to extract the aspect terms together with aspect-level sentiment classification by introducing the Bernoulli attention mechanism. To verify the proposed method, we conduct experiments on restaurant and laptop review data sets from SemEval at both the sentence level and the review level. The experimental results show that our model better allocates appropriate sentiment expressions for a given aspect benefiting from the guidance of aspect terms. Moreover, our method achieves better performance on aspect-level sentiment classification than state-of-the-art models.
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction
Authors: Lei Gao, Yulong Wang, Tongcun Liu, Jingyu Wang, Lei Zhang, Jianxin Liao
Date: 2021-05-18 00:00:00
Proceeding: AAAI 2021 5
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Decision Making, Machine Reading Comprehension, Reading Comprehension, Sentiment Analysis, Term Extraction

Aspect term extraction and opinion word extraction are two fundamental subtasks of aspect-based sentiment analysis. The internal relationship between aspect terms and opinion words is typically ignored, and information for the decision-making of buyers and sellers is insufficient. In this paper, we explore an aspect–opinion pair extraction (AOPE) task and propose a Question-Driven Span Labeling (QDSL) model to extract all the aspect–opinion pairs from user-generated reviews. Specifically, we divide the AOPE task into aspect term extraction (ATE) and aspect-specified opinion extraction (ASOE) subtasks; we first extract all the candidate aspect terms and then the corresponding opinion words given the aspect term. Unlike existing approaches that use the BIO-based tagging scheme for extraction, the QDSL model adopts a span-based tagging scheme and builds a question–answer-based machine-reading comprehension task for an effective aspect–opinion pair extraction. Extensive experiments conducted on three tasks (ATE, ASOE, and AOPE) on four benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches.
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning
Authors: Feixiang Wang, Man Lan, Wenting Wang
Date: 2018-10-14 00:00:00
Proceeding: IEEE 2018 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, Multi-Task Learning, Sentiment Analysis, Term Extraction

Previous studies usually divided aspect-based sentiment analysis into several subtasks in pipeline, i.e., first aspect term and/or opinion term extraction, then aspect-based sentiment prediction, resulting in error propagation and external resources dependency. To overcome the problems mentioned above, in this work we present a novel one-stop solution on aspect-based sentiment analysis. Specifically, we propose a novel multi-task neural learning framework to jointly tackle aspect extraction and sentiment prediction subtasks at the same time, and leverage attention mechanisms to learn the joint representation of aspect-sentiment relationship. We have conducted extensive comparative experiments on two benchmark datasets from SemEval-2014. The experiment results demonstrate the effectiveness of our proposed solution. Especially, our multi-task model outperforms the state-of-the-art systems on aspect extraction subtask.
Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction
Authors: Hao Fei, Yafeng Ren, Yue Zhang, Donghong Ji
Date: 2021-12-03 00:00:00
Proceeding: IEEE 2021 12
Tasks: Aspect Sentiment Triplet Extraction, Decoder, Triplet

Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications. Unfortunately, several major challenges, such as the overlapping issue and long-distance dependency, have not been addressed effectively by the existing ASTE methods, which limits the performance of the task. In this article, we present an innovative encoder-decoder framework for end-to-end ASTE. Specifically, the ASTE task is first modeled as an unordered triplet set prediction problem, which is satisfied with a nonautoregressive decoding paradigm with a pointer network. Second, a novel high-order aggregation mechanism is proposed for fully integrating the underlying interactions between the overlapping structure of aspect and opinion terms. Third, a bipartite matching loss is introduced for facilitating the training of our nonautoregressive system. Experimental results on benchmark datasets show that our proposed framework significantly outperforms the state-of-the-art methods. Further analysis demonstrates the advantages of the proposed framework in handling the overlapping issue, relieving long-distance dependency and decoding efficiency.
Aspect-based Sentiment Analysis with Opinion Tree Generation
Authors: Xiaoyi Bao, Wang Zhongqing, Xiaotong Jiang, Rong Xiao, Shoushan Li
Date: 2022-05-01 00:00:00
Proceeding: IJCAI 2022 5
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

Existing studies usually extract these sentiment elements by decomposing the complex structure prediction task into multiple subtasks. Despite their effectiveness, these methods ignore the semantic structure in ABSA problems and require extensive task-specific designs. In this study, we introduce an Opinion Tree Generation task, which aims to jointly detect all sentiment elements in a tree. We believe that the opinion tree can reveal a more comprehensive and complete aspect-level sentiment structure. Furthermore, we propose a pre-trained model to integrate both syntax and semantic features for opinion tree generation. On one hand, a pre-trained model with large-scale unlabeled data is important for the tree generation model. On the other hand, the syntax and semantic features are very effective for forming the opinion tree structure. Extensive experiments show the superiority of our proposed method. The results also validate the tree structure is effective to generate sentimental elements.
Multiple-element joint detection for Aspect-Based Sentiment Analysis
Authors: Chao Wu, Qingyu Xiong, Hualing Yi, Yang Yu, Qiwu Zhu, Min Gao, Jie Chen
Date: 2020-10-22 00:00:00
Proceeding: Knowledge Based Systems 2020 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Relation, Sentence, Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, which aims to detect target-aspect-sentiment elements in a sentence. Most of the existing research work distinguished the sentiment for aspects or targets independently, ignoring the corresponding relation between the targets and the aspects. However, such a corresponding relation is significant for the accurate prediction of fine-grained sentiment polarity. In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence. Our model utilizes BERT to obtain the initial embedding vector from the aspect–sentence joint input and applies bidirectional long short-term memory to model aspect and sentence representations. We then employ a graph convolutional network with attention mechanisms to capture the dependency relationship between aspect and sentence. We evaluate our approach on two restaurant datasets of SemEval 2015 Task 12 and SemEval 2016 Task 5. Experiment results show that our model achieves state-of-the-art performance in extracting (target, aspect, sentiment) triples. Moreover, the model also has good performance on multiple subtasks of target-aspect-sentiment detection.
Demystifying Disagreement-on-the-Line in High Dimensions
Authors: Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani
Date: 2023-01-31 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Evaluating the performance of machine learning models under distribution shift is challenging, especially when we only have unlabeled data from the shifted (target) domain, along with labeled data from the original (source) domain. Recent work suggests that the notion of disagreement, the degree to which two models trained with different randomness differ on the same input, is a key to tackle this problem. Experimentally, disagreement and prediction error have been shown to be strongly connected, which has been used to estimate model performance. Experiments have led to the discovery of the disagreement-on-the-line phenomenon, whereby the classification error under the target domain is often a linear function of the classification error under the source domain; and whenever this property holds, disagreement under the source and target domain follow the same linear relation. In this work, we develop a theoretical foundation for analyzing disagreement in high-dimensional random features regression; and study under what conditions the disagreement-on-the-line phenomenon occurs in our setting. Experiments on CIFAR-10-C, Tiny ImageNet-C, and Camelyon17 are consistent with our theory and support the universality of the theoretical findings.