P-tuning has demonstrated that anchor tokens are beneficial for improving the performance of downstream tasks. However, manual selection of anchor tokens may result in subjective or suboptimal results. In this paper, we present aCat to automatically select anchor tokens. Following the framework of the soft-hard prompt paradigm, aCat achieves automatic prompt template construction. Experiments conducted on natural language understanding benchmarks demonstrate the effectiveness of our proposed method. On the seven SuperGLUE datasets, aCat achieves higher accuracy than P-tuning, with an average accuracy surpassing P-tuning V2.

The identification of drug-target interactions (DTIs) is a key step in drug discovery, but existing models lack sufficient generalization and robustness when processing data from diverse sources with inconsistent distributions. To address this problem, we propose MSCL-DTI: a novel dual-channel supervised contrastive learning framework for DTI prediction. The model integrates sequence-based and structure-based multi-source joint representations. These features are aggregated via a fusion module and jointly optimized under the guidance of a supervised contrastive learning objective, ultimately forming a unified and highly discriminative latent representation. Extensive experiments demonstrate that MSCL-DTI outperforms baseline models in both performance and generalization. Furthermore, the model was successfully applied to screen drugs for the SARS-CoV-2 Spike protein, proving it to be an effective tool for discovering potential targeted drugs. Data and code are available at https://github.com/HelloJie14492IMSCL-DTI.

Investigating drug-target interaction (DTI) is crucial for drug development and repurposing. Current computational methods leverage drug and protein features for interaction pre-diction, yet face two key challenges: effectively integrating multi-modal sequence and structural information, and overcoming lim-ited dataset size leading to poor generalization. To address these challenges, we propose a novel method called MICA-DTI. By employing intra-modal interactive module with self-supervised learning enhancement and cross-modal interactive module, this method can obtain bi-level and fine-grained representations of sequence-structure, significantly enhancing prediction of unseen drug-target pairs. Comparative experiments demonstrate that our method outperforms baseline models, with ablation studies and visualizations confirming its capability to capture latent drug-target interactions mechanism. Our code has been released in https://github.com/Ibf233/MICA-DTI.

Plagiarism source retrieval is the core task of plagiarism detection. It has become the standard for plagiarism detection to use the queries extracted from suspicious documents to retrieve the plagiarism sources. Generating queries from a suspicious document is one of the most important steps in plagiarism source retrieval. Heuristic-based query generation methods are widely used in the current research. Each heuristic-based method has its own advantages, and no one statistically outperforms the others on all suspicious document segments when generating queries for source retrieval. Further improvements on heuristic methods for source retrieval rely mainly on the experience of experts. This leads to difficulties in putting forward new heuristic methods that can overcome the shortcomings of the existing ones. This paper paves the way for a new statistical machine learning approach to select the best queries from the candidates. The statistical machine learning approach to query generation for source retrieval is formulated as a ranking framework. Specifically, it aims to achieve the optimal source retrieval performance for each suspicious document segment. The proposed method exploits learning to rank to generate queries from the candidates. To our knowledge, our work is the first research to apply machine learning methods to resolve the problem of query generation for source retrieval. To solve the essential problem of an absence of training data for learning to rank, the building of training samples for source retrieval is also conducted. We rigorously evaluate various aspects of the proposed method on the publicly available PAN source retrieval corpus. With respect to the established baselines, the experimental results show that applying our proposed query generation method based on machine learning yields statistically significant improvements over baselines in source retrieval effectiveness.

The goal of predicting query potential for personalization is to determine which queries can benefit from personalization. In this paper, we investigate which kind of strategy is better for this task: classification or regression. We quantify the potential benefits of personalizing search results using two implicit click-based measures: Click entropy and Potential@N. Meanwhile, queries are characterized by query features and history features. Then we build C-SVM classification model and epsilon-SVM regression model respectively according to these two measures. The experimental results show that the classification model is a better choice for predicting query potential for personalization.

Logistic average misclassification percentage (lam%) is a key measure for the spam filtering performance. This paper demonstrates that a spam filter can achieve a perfect 0.00% in lam%, the minimal value in theory, by simply setting a biased threshold during the classifier modeling. At the same time, the overall classification performance reaches only a low accuracy. The result suggests that the role of lam% for spam filtering evaluation should be re-examined.

This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate arbitrary features. LDM is different from most existing models in that it takes into account a variety of linguistic features that are derived from the component models of HMM that is widely used in language modeling approaches to IR. Therefore, LDM is a means of melding discriminative and generative models for IR. We present two algorithms of parameter learning for LDM. One is to optimize the average precision (AP) directly using an iterative procedure. The other is a perceptron-based algorithm that minimizes the number of discordant document-pairs in a rank list. The effectiveness of our approach has been evaluated on the task of ad hoc retrieval using six English and Chinese TREC test sets. Results show that (1) in most test sets, LDM significantly outperforms the state-of-the-art language modeling approaches and the classical probabilistic retrieval model; (2) it is more appropriate to train LDM using a measure of AP rather than likelihood if the IR system is graded on AP; and (3) linguistic features (e.g. phrases and dependences) are effective for IR if they are incorporated properly.