Author name cluster

Jianfei Yu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

IJCAI Conference 2022 Conference Paper

Targeted Multimodal Sentiment Classification based on Coarse-to-Fine Grained Image-Target Matching

Jianfei Yu
Jieming Wang
Rui Xia
Junjie Li

Targeted Multimodal Sentiment Classification (TMSC) aims to identify the sentiment polarities over each target mentioned in a pair of sentence and image. Existing methods to TMSC failed to explicitly capture both coarse-grained and fine-grained image-target matching, including 1) the relevance between the image and the target and 2) the alignment between visual objects and the target. To tackle this issue, we propose a new multi-task learning architecture named coarse-to-fine grained Image-Target Matching network (ITM), which jointly performs image-target relevance classification, object-target alignment, and targeted sentiment classification. We further construct an Image-Target Matching dataset by manually annotating the image-target relevance and the visual object aligned with the input target. Experiments on two benchmark TMSC datasets show that our model consistently outperforms the baselines, achieves state-of-the-art results, and presents interpretable visualizations.

PDF Details DOI

IJCAI Conference 2019 Conference Paper

Adapting BERT for Target-Oriented Multimodal Sentiment Classification

Jianfei Yu
Jing Jiang

As an important task in Sentiment Analysis, Target-oriented Sentiment Classification (TSC) aims to identify sentiment polarities over each opinion target in a sentence. However, existing approaches to this task primarily rely on the textual content, but ignoring the other increasingly popular multimodal data sources (e. g. , images), which can enhance the robustness of these text-based models. Motivated by this observation and inspired by the recently proposed BERT architecture, we study Target-oriented Multimodal Sentiment Classification (TMSC) and propose a multimodal BERT architecture. To model intra-modality dynamics, we first apply BERT to obtain target-sensitive textual representations. We then borrow the idea from self-attention and design a target attention mechanism to perform target-image matching to derive target-sensitive visual representations. To model inter-modality dynamics, we further propose to stack a set of self-attention layers to capture multimodal interactions. Experimental results show that our model can outperform several highly competitive approaches for TSC and TMSC.

PDF Details

IS Journal 2018 Journal Article

Instance-based Domain Adaptation via Multiclustering Logistic Approximation

Feng Xu
Jianfei Yu
Rui Xia

With the explosive growth of the Internet online texts, we could nowadays easily collect a large amount of labeled training data from different source domains. However, a basic assumption in building statistical machine learning models for sentiment analysis is that the training and test data must be drawn from the same distribution. Directly training a statistical model usually results in poor performance, when the training and test data have different distributions. Faced with the massive labeled data from different domains, it is therefore important to identify the source-domain training instances that are closely relevant to the target domain, and make better use of them. In this work, we propose a new approach, called multiclustering logistic approximation (MLA), to address this problem. In MLA, we adapt the source-domain training data to the target domain via a framework of multiclustering logistic approximation. Experimental results demonstrate that MLA has significant advantages over the state-of-the-art instance adaptation methods, especially in the scenario of multidistributional training data.

Details DOI

AAAI Conference 2017 Conference Paper

Recurrent Neural Networks with Auxiliary Labels for Cross-Domain Opinion Target Extraction

Ying Ding
Jianfei Yu
Jing Jiang

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for different domains. When this hidden representation is used for opinion target extraction, we ﬁnd that it can outperform a number of strong baselines with a large margin.

PDF Details

AAAI Conference 2014 Conference Paper

Instance-Based Domain Adaptation in NLP via In-Target-Domain Logistic Approximation

Rui Xia
Jianfei Yu
Feng Xu
Shumei Wang

In the field of NLP, most of the existing domain adaptation studies belong to the feature-based adaptation, while the research of instance-based adaptation is very scarce. In this work, we propose a new instance-based adaptation model, called in-target-domain logistic approximation (ILA). In ILA, we adapt the source-domain data to the target domain by a logistic approximation. The normalized in-targetdomain probability is assigned as an instance weight to each of the source-domain training data. An instance-weighted classification model is trained finally for the cross-domain classification problem. Compared to the previous techniques, ILA conducts instance adaptation in a dimensionalityreduced linear feature space to ensure efficiency in highdimensional NLP tasks. The instance weights in ILA are learnt by leveraging the criteria of both maximum likelihood and minimum statistical distance. The empirical results on two NLP tasks including text categorization and sentiment classification show that our ILA model has advantages over the state-of-the-art instance adaptation methods, in crossdomain classification accuracy, parameter stability and computational efficiency.

PDF Details