Lung-Hao Lee, Liang-Chih Yu, Natalia V Loukachevitch, Ilseyar Alimova, Alexander Panchenko, Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou, Guangmin Zheng, Jin Wang, Sharanya Awasthi, Jonas Becker, Jan Philip Wahle, Terry Ruas, Shamsuddeen Hassan Muhammad, Saif M. Mohammad. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The main challenge of aspect-level sentiment classification (ASC) is associating target aspect terms with relevant contextual words. Existing methods improve ASC performance by incorporating syntactic dependencies through a graph convolution layer on top of BERT. However, these approaches often assign a fixed weight to edges with the same dependency type, overlooking the contextual nuances these dependencies can convey. To address this, we propose syntax-enhanced BERT (SE-BERT), which integrates syntactic distance embeddings, a syntax-enhanced transformer, aspect-specific masking, and a sentiment classification layer. SE-BERT advances previous methods in two main ways. First, SE-BERT enables dynamic weighting for edges with the same dependency type based on the source node's part-of-speech (POS) tags, which enhances graph propagation accuracy and enables more precise edge associations. Second, rather than adding additional graph convolutional layers on top of BERT, SE-BERT replaces BERT's final Transformer layers with the proposed SE-Transformer. This approach directly encodes syntactic information into the attention distribution and word representation within scaled dot-product attention. The model can be initialized from a pretrained checkpoint and fine-tuned for downstream tasks without requiring additional training parameters. Experimental results on five benchmark datasets demonstrate that SE-BERT outperforms existing ASC methods.

Entity linking is the task of assigning a unique identity to named entities mentioned in a text, a sort of word sense disambiguation that focuses on automatically determining a pre-defined sense for a target entity to be disambiguated. This study proposes the DGE (Dual Gloss Encoders) model for Chinese entity linking in the biomedical domain. We separately model a dual encoder architecture, comprising a context-aware gloss encoder and a lexical gloss encoder, for contextualized embedding representations. DGE are then jointly optimized to assign the nearest gloss with the highest score for target entity disambiguation. The experimental datasets consist of a total of 10,218 sentences that were manually annotated with glosses defined in the BabelNet 5.0 across 40 distinct biomedical entities. Experimental results show that the DGE model achieved an F1-score of 97.81, outperforming other existing methods. A series of model analyses indicate that the proposed approach is effective for Chinese biomedical entity linking.

An increasing amount of research has recently focused on dimensional sentiment analysis that represents affective states as continuous numerical values on multiple dimensions, such as valence-arousal (VA) space. Compared to the categorical approach that represents affective states as distinct classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. However, dimensional sentiment resources with valence-arousal ratings are very rare, especially for the Chinese language. Therefore, this study aims to: (1) Build a Chinese valence-arousal resource called Chinese EmoBank, the first Chinese dimensional sentiment resource featuring various levels of text granularity including 5,512 single words, 2,998 multi-word phrases, 2,582 single sentences, and 2,969 multi-sentence texts. The valence-arousal ratings are annotated by crowdsourcing based on the Self-Assessment Manikin (SAM) rating scale. A corpus cleanup procedure is then performed to improve annotation quality by removing outlier ratings and improper texts. (2) Evaluate the proposed resource using different categories of classifiers such as lexicon-based, regression-based, and neural-network-based methods, and comparing their performance to a similar evaluation of an English dimensional sentiment resource.

Named Entity Recognition (NER) is a natural language processing task for recognizing named entities in a given sentence. Chinese NER is difficult due to the lack of delimited spaces and conventional features for determining named entity boundaries and categories. This study proposes the ME-MGNN (Multiple Embeddings enhanced Multi-Graph Neural Networks) model for Chinese NER in the healthcare domain. We integrate multiple embeddings at different granularities from the radical, character to word levels for an extended character representation, and this is fed into multiple gated graph sequence neural networks to identify named entities and classify their types. The experimental datasets were collected from health-related news, digital health magazines and medical question/answer forums. Manual annotation was conducted for a total of 68,460 named entities across 10 entity types (body, symptom, instrument, examination, chemical, disease, drug, supplement, treatment and time) in 30,692 sentences. Experimental results indicated our ME-MGNN model achieved an F1-score result of 75.69, outperforming previous methods. In practice, a series of model analysis implied that our method is effective and efficient for Chinese healthcare NER.

In this paper, we describe the construction details of a confused character set for Chinese spell checking. The SIGHAN 2013-2015 bakeoff datasets are adopted to measure the performance of correct character suggestions. Our confusion set significantly outperforms the existing confusion set in candidate selection for automatic spelling checkers.

This article explores users' browsing intents to predict the category of a user's next access during web surfing and applies the results to filter objectionable content, such as pornography, gambling, violence, and drugs. Users' access trails in terms of category sequences in click‐through data are employed to mine users' web browsing behaviors. Contextual relationships of URL categories are learned by the hidden Markov model. The top‐level domains (TLDs) extracted from URLs themselves and the corresponding categories are caught by the TLD model. Given a URL to be predicted, its TLD and current context are empirically combined in an aggregation model. In addition to the uses of the current context, the predictions of the URL accessed previously in different contexts by various users are also considered by majority rule to improve the aggregation model. Large‐scale experiments show that the advanced aggregation approach achieves promising performance while maintaining an acceptably low false positive rate. Different strategies are introduced to integrate the model with the blacklist it generates for filtering objectionable web pages without analyzing their content. In practice, this is complementary to the existing content analysis from users' behavioral perspectives.

This special issue contains four articles based on and expanded from systems presented at the SIGHAN-7 Chinese Spelling Check Bakeoff. We provide an overview of the approaches and designs for Chinese spelling checkers presented in these articles. We conclude this introductory article with a summary of possible future directions.

This study explores the existing blacklists to discover suspected URLs that refer to on-the-fly phishing threats in real time. We propose a PhishTrack framework that includes redirection tracking and form tracking components to update the phishing blacklists. It actively finds phishing URLs as early as possible. Experimental results show that our proactive phishing update method is an effective and efficient approach for improving the coverage of the blacklists. In practice, our solution is complementary to the existing anti-phishing techniques for providing secured web surfing.

Yuen-Hsien Tseng, Lung-Hao Lee, Shu-Yen Lin, Bo-Shun Liao, Mei-Jun Liu, Hsin-Hsi Chen, Oren Etzioni, Anthony Fader. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers. 2014.

In this paper, we describe the development of the tagging editor for learner corpora annotation and computer-aided error analysis. We collect essays written by learners of Chinese as a foreign language for grammatical error annotation and correction. Our tagging editor is effective and enables the annotated corpus to be used in a shared task in ICCE 2014.

This paper explores users' browsing intents to predict the category of a user's next access during web surfing, and applies the results to objectionable content filtering. A user's access trail represented as a sequence of URLs reveals the contextual information of web browsing behaviors. We extract behavioral features of each clicked URL, i.e., hostname, bag-of-words, gTLD, IP, and port, to develop a linear chain CRF model for context-aware category prediction. Large-scale experiments show that our method achieves a promising accuracy of 0.9396 for objectionable access identification without requesting their corresponding page content. Error analysis indicates that our proposed model results in a low false positive rate of 0.0571. In real-life filtering simulations, our proposed model accomplishes macro-averaging blocking rate 0.9271, while maintaining a favorably low macro-averaging over-blocking rate 0.0575 for collaboratively filtering objectionable content with time change on the dynamic web.

This paper studies the feasibility of an early warning system that prevents users from the dangerous situations they may fall into during web surfing. Our approach adopts behavioral Hidden Markov Models to explore collective intelligence embedded in users' browsing behaviors for context-aware category prediction, and applies the results to web security threat prevention. Large-scale experiments show that our proposed method performs accuracy 0.463 for predicting the fine-grained categories of users' next accesses. In real-life filtering simulations, our method can achieve macro-averaging blocking rate 0.4293 to find web security threats that cannot be detected by the existing security protection solutions at the early stage, while accomplishes a low macro-averaging over-blocking rate 0.0005 with the passage of time. In addition, behavioral HMM is able to alert users for avoiding security threats by 8.4 hours earlier than the current URL filtering engine does. Our simulations show that the shortening of this lag time is critical to avoid severe diffusions of security threats.

Abstract This article presents a search‐intent‐based method to generate pornographic blacklists for collaborative cyberporn filtering. A novel porn‐detection framework that can find newly appearing pornographic web pages by mining search query logs is proposed. First, suspected queries are identified along with their clicked URLs by an automatically constructed lexicon. Then, a candidate URL is determined if the number of clicks satisfies majority voting rules. Finally, a candidate whose URL contains at least one categorical keyword will be included in a blacklist. Several experiments are conducted on an MSN search porn dataset to demonstrate the effectiveness of our method. The resulting blacklist generated by our search‐intent‐based method achieves high precision (0.701) while maintaining a favorably low false‐positive rate (0.086). The experiments of a real‐life filtering simulation reveal that our proposed method with its accumulative update strategy can achieve 44.15% of a macro‐averaging blocking rate, when the update frequency is set to 1 day. In addition, the overblocking rates are less than 9% with time change due to the strong advantages of our search‐intent‐based method. This user‐behavior‐oriented method can be easily applied to search engines for incorporating only implicit collective intelligence from query logs without other efforts. In practice, it is complementary to intelligent content analysis for keeping up with the changing trails of objectionable websites from users' perspectives.

This paper presents an intent conformity model to collaboratively generate blacklists for cyberporn filtering. A novel porn detection framework via searches-and-clicks is proposed to explore collective intelligence embedded in query logs. Firstly, the clicked pages are represented in terms of the weighted queries to reflect the degrees related to pornography. Consequently, these weighted queries are regarded as discriminative features to calculate the pornography indicator by an inverse chi-square method for candidate determination. Finally, a candidate whose URL contains at least one pornographic keyword is included in our collaborative blacklists. The experiments on a MSN porn data set indicate that the generated blacklist achieves a high precision, while maintaining a favorably low false positive rate. In addition, real-life filtering simulations reveal that our blacklist is more effective than some publicly released blacklists.

This paper presents a user intent method to generate blacklists for collaborative cyberporn filtering. A novel porn detection framework that finds new pornographic web pages by mining user search behaviors is proposed. It employs users' clicks in search query logs to select the suspected web pages without extra human efforts to label data for training, and determines their categories with the help of URL host name and path information, but without web page content. We adopt an MSN porn data set to explore the effectiveness of our method. This user intent approach achieves high precision, while maintaining favorably low false positive rate. In addition, real-life filtering simulation reveals that our user intent method with its accumulative update strategy achieves 43.36% of blocking rate, while maintaining a steadily less than 7% of over-blocking rate.

This study proposed early decision heuristics for objectionable content classification using an inverse chi-square classifier. The experimental results indicated that only examining the title plus 10% of a web page’s content can cost-effectively achieve an average precision of 93%. More importantly, the F1measure achieved its best when the title plus 60% of the body was examined. The proposed early decision making heuristics can serve as the trade-off baseline for real-time online filtering.