Yunfei Long


Chinese Synesthesia Detection: New Dataset and Models
Xiaotong Jiang | Qingqing Zhao | Yunfei Long | Zhongqing Wang
Findings of the Association for Computational Linguistics: ACL 2022

In this paper, we introduce a new task called synesthesia detection, which aims to extract the sensory word of a sentence, and to predict the original and synesthetic sensory modalities of the corresponding sensory word. Synesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. It involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought and action, which makes it become a bridge between figurative linguistic phenomenon and abstract cognition, and thus be helpful to understand the deep semantics. To address this, we construct a large-scale human-annotated Chinese synesthesia dataset, which contains 7,217 annotated sentences accompanied by 187 sensory words. Based on this dataset, we propose a family of strong and representative baseline models. Upon these baselines, we further propose a radical-based neural network model to identify the boundary of the sensory word, and to jointly detect the original and synesthetic sensory modalities for the word. Through extensive experiments, we observe that the importance of the proposed task and dataset can be verified by the statistics and progressive performances. In addition, our proposed model achieves state-of-the-art results on the synesthesia dataset.

Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis
Zijie Lin | Bin Liang | Yunfei Long | Yixue Dang | Min Yang | Min Zhang | Ruifeng Xu
Proceedings of the 29th International Conference on Computational Linguistics

The existing research efforts in Multimodal Sentiment Analysis (MSA) have focused on developing the expressive ability of neural networks to fuse information from different modalities. However, these approaches lack a mechanism to understand the complex relations within and across different modalities, since some sentiments may be scattered in different modalities. To this end, in this paper, we propose a novel hierarchical graph contrastive learning (HGraph-CL) framework for MSA, aiming to explore the intricate relations of intra- and inter-modal representations for sentiment extraction. Specifically, regarding the intra-modal level, we build a unimodal graph for each modality representation to account for the modality-specific sentiment implications. Based on it, a graph contrastive learning strategy is adopted to explore the potential relations based on unimodal graph augmentations. Furthermore, we construct a multimodal graph of each instance based on the unimodal graphs to grasp the sentiment relations between different modalities. Then, in light of the multimodal augmentation graphs, a graph contrastive learning strategy over the inter-modal level is proposed to ulteriorly seek the possible graph structures for precisely learning sentiment relations. This essentially allows the framework to understand the appropriate graph structures for learning intricate relations among different modalities. Experimental results on two benchmark datasets show that the proposed framework outperforms the state-of-the-art baselines in MSA.


Affection Driven Neural Networks for Sentiment Analysis
Rong Xiang | Yunfei Long | Mingyu Wan | Jinghang Gu | Qin Lu | Chu-Ren Huang
Proceedings of the Twelfth Language Resources and Evaluation Conference

Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge can be effectively utilized. In this work, we propose a novel affection-driven approach to incorporating affective knowledge into neural network models. The affective knowledge is obtained in the form of a lexicon under the Affect Control Theory (ACT), which is represented by vectors of three-dimensional attributes in Evaluation, Potency, and Activity (EPA). The EPA vectors are mapped to an affective influence value and then integrated into Long Short-term Memory (LSTM) models to highlight affective terms. Experimental results show a consistent improvement of our approach over conventional LSTM models by 1.0% to 1.5% in accuracy on three large benchmark datasets. Evaluations across a variety of algorithms have also proven the effectiveness of leveraging affective terms for deep model enhancement.

Ciron: a New Benchmark Dataset for Chinese Irony Detection
Rong Xiang | Xuefeng Gao | Yunfei Long | Anran Li | Emmanuele Chersoni | Qin Lu | Chu-Ren Huang
Proceedings of the Twelfth Language Resources and Evaluation Conference

Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research. However, Chinese irony detection often lacks labeled benchmark datasets. In this paper, we introduce Ciron, the first Chinese benchmark dataset available for irony detection for machine learning models. Ciron includes more than 8.7K posts, collected from Weibo, a micro blogging platform. Most importantly, Ciron is collected with no pre-conditions to ensure a much wider coverage. Evaluation on seven different machine learning classifiers proves the usefulness of Ciron as an important resource for Chinese irony detection.

An Element-aware Multi-representation Model for Law Article Prediction
Huilin Zhong | Junsheng Zhou | Weiguang Qu | Yunfei Long | Yanhui Gu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Existing works have proved that using law articles as external knowledge can improve the performance of the Legal Judgment Prediction. However, they do not fully use law article information and most of the current work is only for single label samples. In this paper, we propose a Law Article Element-aware Multi-representation Model (LEMM), which can make full use of law article information and can be used for multi-label samples. The model uses the labeled elements of law articles to extract fact description features from multiple angles. It generates multiple representations of a fact for classification. Every label has a law-aware fact representation to encode more information. To capture the dependencies between law articles, the model also introduces a self-attention mechanism between multiple representations. Compared with baseline models like TopJudge, this model improves the accuracy of 5.84%, the macro F1 of 6.42%, and the micro F1 of 4.28%.

ExTRA: Explainable Therapy-Related Annotations
Mat Rawsthorne | Tahseen Jilani | Jacob Andrews | Yunfei Long | Jeremie Clos | Samuel Malins | Daniel Hunt
2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence

In this paper we report progress on a novel explainable artificial intelligence (XAI) initiative applying Natural Language Processing (NLP) with elements of codesign to develop a text classifier for application in psychotherapy training. The task is to produce a tool that will facilitate therapists to review their sessions by automatically labelling transcript text with levels of interaction for patient activation in known psychological processes, using XAI to increase their trust in the model’s suggestions and client trajectory predictions. After pre-processing of the language features extracted from professionally annotated therapy session transcripts, we apply a supervised machine learning approach (CHAID) to classify interaction labels (negative, neutral, positive). Weighted samples are used to overcome class imbalanced data. The results show this initial model can make useful distinctions among the three labels of patient activation with 74% accuracy and provide insight into its reasoning. This ongoing project will additionally evaluate which XAI approaches can be used to increase the transparency of the tool to end users, exploring whether direct involvement of stakeholders improves usability of the XAI interface and therefore trust in the solution.


Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis
Rong Xiang | Yunfei Long | Qin Lu | Dan Xiong | I-Hsuan Chen
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Social media text written in Chinese communities contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and some minor text using Latin letters, an alphabet-based writing system. This phenomenon is called writing systems changes (WSCs). Past studies have shown that WSCs can be used to express emotions, particularly where the social and political environment is more conservative. However, because WSCs can break the syntax of the major text, it poses more challenges in Natural Language Processing (NLP) tasks like emotion classification. In this work, we present a novel deep learning based method to include WSCs as an effective feature for emotion analysis. The method first identifies all WSCs points. Then representation of the major text is learned through an LSTM model whereas the minor text is learned by a separate CNN model. Emotions in the minor text are further highlighted through an attention mechanism before emotion classification. Performance evaluation shows that incorporating WSCs features using deep learning models can improve performance measured by F1-scores compared to the state-of-the-art model.

Dual Memory Network Model for Biased Product Review Classification
Yunfei Long | Mingyu Ma | Qin Lu | Rong Xiang | Chu-Ren Huang
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.

Food-Related Sentiment Analysis for Cantonese
Natalia Klyueva | Yunfei Long | Chu-Ren Huang | Qin Lu
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing


Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis
Minglei Li | Qin Lu | Yunfei Long
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases. We examine how phrases can be represented through manually prepared lexicons, extended lexicons using computational methods, or word embedding. Comparative studies clearly show that word embedding using unsupervised distributional method outperforms manually prepared lexicons no matter what affective models are used in the lexicons. Our conclusion is that although different affective lexicons are cognitively backed by theories, they do not show any advantage over the automatically obtained word embedding.

Fake News Detection Through Multi-Perspective Speaker Profiles
Yunfei Long | Qin Lu | Rong Xiang | Minglei Li | Chu-Ren Huang
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles.

A Cognition Based Attention Model for Sentiment Analysis
Yunfei Long | Qin Lu | Rong Xiang | Minglei Li | Chu-Ren Huang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.

Leveraging Eventive Information for Better Metaphor Detection and Classification
I-Hsuan Chen | Yunfei Long | Qin Lu | Chu-Ren Huang
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Metaphor detection has been both challenging and rewarding in natural language processing applications. This study offers a new approach based on eventive information in detecting metaphors by leveraging the Chinese writing system, which is a culturally bound ontological system organized according to the basic concepts represented by radicals. As such, the information represented is available in all Chinese text without pre-processing. Since metaphor detection is another culturally based conceptual representation, we hypothesize that sub-textual information can facilitate the identification and classification of the types of metaphoric events denoted in Chinese text. We propose a set of syntactic conditions crucial to event structures to improve the model based on the classification of radical groups. With the proposed syntactic conditions, the model achieves a performance of 0.8859 in terms of F-scores, making 1.7% of improvement than the same classifier with only Bag-of-word features. Results show that eventive information can improve the effectiveness of metaphor detection. Event information is rooted in every language, and thus this approach has a high potential to be applied to metaphor detection in other languages.


Event Based Emotion Classification for News Articles
Minglei Li | Da Wang | Qin Lu | Yunfei Long
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers

Emotion Corpus Construction Based on Selection from Hashtags
Minglei Li | Yunfei Long | Lu Qin | Wenjie Li
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The availability of labelled corpus is of great importance for supervised learning in emotion classification tasks. Because it is time-consuming to manually label text, hashtags have been used as naturally annotated labels to obtain a large amount of labelled training data from microblog. However, natural hashtags contain too much noise for it to be used directly in learning algorithms. In this paper, we design a three-stage semi-automatic method to construct an emotion corpus from microblogs. Firstly, a lexicon based voting approach is used to verify the hashtag automatically. Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels. Finally, the remaining data will be manually examined to filter out the noisy data. Out of about 48K filtered Chinese microblogs, 39k microblogs are selected to form the final corpus with the Kappa value reaching over 0.92 for the automatic parts and over 0.81 for the manual part. The proportion of automatic selection reaches 54.1%. Thus, the method can reduce about 44.5% of manual workload for acquiring quality data. Experiment on a classifier trained on this corpus shows that it achieves comparable results compared to the manually annotated NLP&CC2013 corpus.


现代汉语语义词典多义词词库的校正和再修订(New Editing and Checking Work of the Semantic Knowledge Base of Contemporary Chinese (SKCC))[In Chinese]
Yunfei Long | Yuefeng Bian | Weiguang Qu | Rubing Dai
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

Dependency parsing for Chinese long sentence: A second-stage main structure parsing method
Bo Li | Yunfei Long | Weiguang Qu
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters