Language acquisition is vital to revealing the nature of human language intelligence and has recently emerged as a promising perspective for improving the interpretability of large language models (LLMs). However, it is ethically and practically infeasible to conduct experiments that require controlling human learners' language inputs. This poses challenges for the verifiability and scalability of language acquisition modeling, particularly in Chinese second language acquisition (SLA). While LLMs provide a controllable and reproducible alternative, a systematic benchmark to support phase-wise modeling and assessment is still lacking. To address these issues, we propose HSKBenchmark, the first benchmark for staged modeling and writing assessment of LLMs in Chinese SLA. The benchmark covers HSK levels 3 to 6, comprising authentic textbooks with 6.76M tokens, 16K synthetic instruction data, 30 test topics and a linguistically-grounded evaluation system. To simulate human acquisition trajectories, a curriculum-tuning framework is introduced, which trains LLMs in a progression from beginner to advanced proficiency levels. Since language production in writing is a key perspective for observing SLA development, an evaluation system is established to probe LLMs in writing, including the coverage of level-based grammar items, writing errors, lexical complexity, syntactic complexity, and holistic scoring. We also develop an HSKAgent fine-tuned on 10K compositions from Chinese second language learners to automate this evaluation system. Extensive experimental results demonstrate that HSKBenchmark not only models Chinese SLA effectively, but also serves as a reliable benchmark for dynamic writing assessment in LLMs. Our fine-tuned LLMs have writing performance on par with advanced human learners and exhibit human-like acquisition characteristics. The HSKBenchmark, HSKAgent, and checkpoints serve as foundational tools and resources, with the potential to pave the way for future research on language acquisition modeling and LLMs interpretability.

Clinical trials are essential for discovering new treatments and advancing medical knowledge. However, the high uncertainty of carrying out clinical trials often ends with ineffective results. Therefore, the accurate prediction of clinical trial outcomes has become a significant challenge. Numerous publicly accessible clinical trial reports have been discovered to be beneficial in alleviating this challenge but lack necessary annotations to be formal datasets for deep model training. To address the issue, this paper proposes to construct a new clinical trial dataset by extracting publicly available clinical trial reports from ClinicalTrials.gov and PubMed. In addition, a new two-stage method is proposed for the prediction of clinical trial outcomes across all trial phases. Specifically, our method first employs a prompt template combined with each clinical trial report to prompt a large language model to generate a concise summarization text containing essential information related to the clinical trial outcomes. Subsequently, this summarization text is utilized to train a classifier to predict the outcomes. Extensive experiments were conducted on the dataset, and our method was compared with several state-of-the-art classification models. The results showed that our method achieved the best performance in predicting clinical trial outcomes, especially using small amounts of training data under a data imbalance difficulty.

Spoken-only languages are languages without a writing system. They remain excluded from modern Natural Language Processing (NLP) advancements like Large Language Models (LLMs) due to their lack of textual data. Existing NLP research focuses primarily on high-resource or written low-resource languages, leaving spoken-only languages critically underexplored. As a popular NLP paradigm, LLMs have demonstrated strong few-shot and cross-lingual generalization abilities, making them a promising solution for understanding and translating spoken-only languages. In this paper, we investigate how LLMs can translate spoken-only languages into high-resource languages by leveraging international phonetic transcription as an intermediate representation. We propose UNILANG, a unified language understanding framework that learns to translate spoken-only languages via in-context learning. Through automatic dictionary construction and knowledge retrieval, UNILANG equips LLMs with more fine-grained knowledge for improving word-level semantic alignment. To support this study, we introduce the SOLAN dataset, which consists of Bai (a spoken-only language) and its corresponding translations in a high-resource language. A series of experiments demonstrates the effectiveness of UNILANG in translating spoken-only languages, potentially contributing to the preservation of linguistic and cultural diversity. Our dataset and code will be publicly released.

Task-oriented dialogue systems have recently gained increasing attention due to their capability of using natural language to fulfill specific user demands, such as restaurant reservation and hotel booking. Recent works directly model task-oriented dialogue response as a text generation task. However, these methods, utilizing generated response tokens as an attention query to obtain the vanilla attention distribution over an entire knowledge base, frequently lead to an entity inconsistency in final response generation. To tackle this problem, we propose a novel attention mechanism called span attention and a novel model named Span Attention GEnerator (SAGE). The span attention computes an attention score between a query vector and each knowledge record vector instead of computing a vanilla attention score among word vectors, which consisted of dialogue context and knowledge base. For effective training, we propose an attention constraint strategy that utilizes the entities appearing in response as pseudo-labels to supervise the training of the span attention. Experiments based on three publicly accessible datasets demonstrate that our model, utilizing the proposed mechanism, outperforms the state-of-the-art models with improvements of 8.94%, 2.29%, and 10.22% respectively in Entity F1.

Large language models (LLMs) fundamentally reshape learning and teaching models, shifting tutoring systems from supporting individual learning to facilitating collaborative learning (CL) like task-oriented group discussions. However, existing AI tutors struggle to guide CL, as they seldom model the interactions between AI tutors and students. Therefore, they cannot scaffold students to complete tasks collaboratively, which impairs learning outcomes and pedagogy adaptability. Additionally, existing AI tutors fail to make use of CL theories to generate instructive feedback, which leads to undesirable interactions such as over-instruction and limits students' autonomy. In this paper, we propose an LLM-based collaborative agent that innovatively leverages pedagogical strategies to sense discussion stages, detect learning issues, identify the timing of intervention, and generate instructive feedback. To develop the agent, we first design a prompting strategy based on a CL theory, that is, the Community of Inquiry, to cultivate the agent to understand the discussion status. Second, a multi-agent interaction framework is proposed to simulate the collaborative learning behavior between AI tutors and students. Meanwhile, a synthetic task-oriented group discussion dataset, namely CLTeach, is generated, which consists of 27k manually-verified multi-party dialogues with fine-grained annotations of instructive feedback and explanations. Lastly, we use CLTeach to fine-tune the LLM agent, ultimately enabling it to generate instructive feedback at the right time to support students in CL. Extensive experiments demonstrate that our agent achieves state-of-the-art performance in feedback generation and has the potential to mimic human teachers effectively.

Task-oriented dialogue (TOD) systems typically retrieve domain-specific knowledge from external knowledge bases to generate accurate responses. Most existing works focus on understanding unimodal textual context and generating text-only responses. This reliance on text-modality information overlooks the richness of real-world multi-modal data, thereby limiting the effectiveness of TOD systems. To address this problem, we propose a unified multi-modal context-knowledge model named MIND. MIND employs a text-image alignment strategy to achieve cross-modal understanding between textual and visual modalities. In addition, a multi-modal TOD dataset named MuCoK has been specifically designed for dialogue objectives in real-world multi-modal scenarios. To the best of our knowledge, MIND is the first TOD model capable of understanding multi-modal dialogue contexts, enabling multi-modal knowledge retrieval, and generating coherent multi-modal responses. Experiment results on two benchmarks demonstrate that our MIND achieves state-of-the-art performance across all evaluation metrics, surpassing even the multi-modal large language models.

Recently, measurable quantitative information extraction from unstructured texts has attracted increasing attention in various fields of industry. However, due to the issues of error propagation and insufficient deep interactions between entities and relations, the accurate extraction of measurable quantitative information remains as a challenging task. To address these issues, this paper proposes a joint model based on table filling (JMTF) for measurable quantitative information extraction task. The core of this model introduces a co-attention mechanism to achieve bidirectional interactions between recognition and association subtasks, thereby avoiding problems such as feature confusion or insufficient interaction. Additionally, the model employs an optimized BERT-based encoder (TEncoder) for text encoding. TEncoder improves ability of the model to capture long-range contextual information of text by incorporating direction-awareness, distance-awareness, and unscaled attention. To further improve performance of the model in Chinese text, TEncoder also integrates the inherent features of Chinese characters, such as pinyin and glyphs, which help handle the ambiguity of polysemous words and homophones. The experiments evaluate the JMTF model on a standardized quantitative information dataset of 3106 Chinese text sentences. The results show that our JMTF achieves F1 values of 87.01% and 85.95% for MQI recognition and association, respectively, outperforming the best baseline at 86.42% and 84.65%, demonstrating its advantages in measurable quantitative information extraction.

Knowledge graphs (KGs) typically have distinct entity and relation vocabularies, there is usually no overlap between the vocabularies of different KGs. Consequently, most existing studies develop independent reasoning models for different KGs. However, such models generally lack generalization capability in reasoning. This paper proposes a novel model Local-Global Collaborative Relational Representation for KG Reasoning(LGRR) to achieve universal reasoning by learning relational invariance in KGs. Specifically, we introduce a local-global relational graph embedding method, which employs a graph neural network with an attention mechanism to perform local message passing. Then, global attention is applied to information propagation to overcome the limitations of traditional local message passing and enhance capturing more comprehensive graph structural information. Finally, in the local message passing phase, we propose a relation-aware dynamic attention mechanism and a relation aggregation strategy. By integrating relation-type semantics with local subgraph structural features, our method dynamically generates attention coefficients among nodes, thereby enhancing the model reasoning capability. The results demonstrate that the zero-shot (0-shot) inference capability of a single pre-trained LGRR model is comparable or superior to models trained on specific KGs across most unseen KGs.

Word sense disambiguation (WSD) can be viewed as two subtasks: textual word sense disambiguation (Textual-WSD) and visual word sense disambiguation (Visual-WSD). They aim to identify the most semantically relevant senses or images to a given context containing ambiguous target words. However, existing WSD models seldom address these two subtasks jointly due to lack of images in Textual-WSD datasets or lack of senses in Visual-WSD datasets. To bridge this gap, we propose PolCLIP, a unified image-text WSD model. By employing an image-text complementarity strategy, it not only simulates stable diffusion models to generate implicit visual representations for word senses but also simulates image captioning models to provide implicit textual representations for images. Additionally, a disambiguation-oriented image-sense dataset is constructed for the training objective of learning multimodal polysemy representations. To the best of our knowledge, PolCLIP is the first model that can cope with both Textual-WSD and Visual-WSD. Extensive experimental results on benchmarks demonstrate the effectiveness of our method, achieving a 2.53% F1-score increase over the state-of-the-art models on Textual-WSD and a 2.22% HR@1 improvement on Visual-WSD.

Visual Word Sense Disambiguation (Visual-WSD), as a sub-task of fine-grained image-text retrieval, requires a high level of language-vision understanding to capture and exploit the nuanced relationships between text and visual features. However, the cross-linguistic background only with limited contextual information is considered the most significant challenges for this task. In this paper, we propose MTA, which employs a new approach for multilingual contrastive learning with self-distillation to align fine-grained textual features to fixed vision features and align non-English textual features to English textual momentum features. It is a lightweight and end-to-end model since it does not require updating the visual encoder or translation operations. Furthermore, a trilingual fine-grained image-text dataset is developed and a ChatGPT API module is integrated to enrich the word senses effectively during the testing phase. Extensive experiments show that MTA achieves state-of-the-art results on the benchmark English, Farsi, and Italian datasets in SemEval-2023 Task 1 and exhibits impressive generalization abilities when dealing with variations in text length and language.

Task-Oriented Dialogue commonly utilizes external knowledge bases to respond user utterances to complete specific tasks. Existing models use whole dialogue history record directly, which contain massive irrelevant noise entities, decreasing accuracy in knowledge retrieval. Thus, how to avoid noise entities and effectively identify relevant entities representing user intents within long dialogue history record remains as a crucial challenge. This paper proposes a new model named IEM consisted of a knowledge selector to retrieve relevant knowledge and a response generator to generate system responses. In addition, this paper introduces an intent retrieval mechanism that search for intent entities from dialogue history and generate intent prompts for task-oriented dialogue. Experiments on three publicly available datasets show that our IEM is superior to existing state-of-the-art baseline methods, verifying its effectiveness in task-oriented dialogue. Moreover, the results demonstrate the promising adaptability of intent retrieval mechanism in incorporating into large language models.

Language Models (LMs) have achieved impressive success in various question answering (QA) tasks but have shown limited performance on structured reasoning. Recent research suggests that Knowledge Graph (KG) can augment text data by providing a structured background to enhance reasoning capabilities of LMs. Therefore, how to integrate and reason over KG representations and language context remains an open question. In this work, we propose MJR, a novel model to integrate encoded representations of LMs and graph neural network through multiple layers of feature interaction operations. Subsequently, the fused feature representations in two modalities are fed into a multi-head representation fusion module to comprehensively capture semantic and graph structure information, thereby enhancing language understanding and reasoning capabilities. In addition, we investigate the performance and applicability of different types of large language models as text encoder in the question-answering task. We evaluate our model on three common dataset: CommonsenseQA, OpenBookQA, and MedQA-USMLE datasets. The results demonstrate the advancements of MJR over existing LMs, LM+KG and LLMs models in reasoning for question answering.

Discourse parsing in multi-party dialogue aims to extract the relationships between elementary discourse units (EDUs) such as arguments and utterances, and has numerous applications like chatbots or virtual assistants. Two significant challenges have been encountered in previous works: the obstacles in fusing various contexts in the modeling; and the lack of argumentative dialogue datasets in this field. To tackle context fusion challenges in the modeling, we introduce the Hierarchical Graph Fusion Network (HGFN). This method introduces sufficient contexts by hierarchically modeling the dialogue and minimizes context noise through a novel routing mechanism. It specifically: (1) Encodes multiple levels of contexts using hierarchical graph neural networks. During this stage, the router allows information exchange across different levels, expanding the model’s receptive field in dialogue. (2) Fuses matching signals from multiple levels of contexts with a fusion network. Here, the router restricts the information flows to the same context level, efficiently minimizing noise from irrelevant context. Furthermore, despite the significance of argumentative multi-party dialogue in real-world applications, this area remains largely unexplored due to dataset scarcity. To address this challenge, we develop two meticulously annotated datasets, MRDL and MRDR. Unlike the prevailing datasets that primarily focus on short and colloquial conversations, our datasets feature intricate argumentative dialogues and are publicly accessible at https://github.com/AI0Research/MRDL-and-MRDR. Our new datasets and the HGFN model could promote further advancements in this field. Extensive experiments are conducted and it is revealed that the HGFN model surpasses the state-of-the-art, particularly in complex, argumentative dialogues.

User intent classification is a vital task for analyzing users’ essential requirements from the users’ input query in information retrieval systems, question answering systems, and dialogue systems. Pre-trained language model Bidirectional Encoder Representation from Transformers (BERT) has been widely applied to the user intent classification task. However, BERT is compute intensive and time-consuming during inference and usually causes latency in real-time applications. To improve the inference efficiency of BERT for the user intent classification task, this article proposes a new network named one-stage deep-supervised early-exiting BERT as one-stage deep-supervised early-exiting BERT (OdeBERT). In addition, a deep supervision strategy is developed to incorporate the network with internal classifiers by one-stage joint training to improve the learning process of classifiers by extracting discriminative category features. Experiments are conducted on publicly available datasets, including ECDT, SNIPS, and FDQuestion. The results show that the OdeBERT can speed up original BERT 12 times faster at most with the same performance, outperforming state-of-the-art baseline methods.

This paper proposes a dense fusion transformer (DFT) framework to integrate textual, acoustic, and visual information for multimodal affective computing. DFT exploits a modality-shared transformer (MT) module to extract the modality-shared features by modelling unimodal, bimodal, and trimodal interactions jointly. MT constructs a series of dense fusion blocks to fuse utterance-level sequential features of the multiple modalities from the perspectives of low-level and high-level semantics. In particular, MT adopts local and global transformers to learn modality-shared representations by modelling inter- and intra-modality interactions. Furthermore, we devise a modality-specific representation (MR) module with a soft orthogonality constraint to penalize the distance between modality-specific and modality-shared representations, which are fused by a transformer to make affective predictions. Extensive experiments conducted on five public benchmark datasets show that DFT outperforms the state-of-the-art baselines.

In this paper, we propose a deep dense fusion network with multimodal residual (DFMR) to integrate multimodal information including language, acoustic speeches, and visual images for sentiment analysis. DFMR exploits a dense fusion (DF) block to fuse the multimodal features obtained by modality-specific sequence networks, which is achieved by modelling their unimodal, bimodal and trimodal interactions jointly. Instead of concatenating the multimodal features directly, DF block conducts fusion for any two paired modalities firstly, and the fused information will be integrated with the other modalities subsequently. Furthermore, DFMR stacks multiple DF blocks to capture high-level semantic information conveyed by the multimodal representations. In particular, DFMR adopts a multimodal residual (MR) block to integrate the modality-specific features and fused features in each DF blocks, to avoid forgetting the multi-aspect information and alleviate gradient vanishing during stacking. Extensive experiments conducted on four public benchmark datasets show that DFMR outperforms eleven state-of-the-art baselines.

Not identified as being exposed or infected, the group of asymptomatic and presymptomatic patients has become the key source of infectious hosts for the COVID-19 pandemic, triggering the re-emergence of outbreaks. Acknowledging the impacts of movement of unidentified patients and the limited testing capacity on understanding the spread of the virus, an augmented Susceptible-Exposed-Infectious-Confirmed-Recovered (SEICR) model integrating intercity migration data and testing capacity is developed to probe into the number of unidentified COVID-19 infected patients. This model allows evaluation of the effectiveness of active interventions, and more accurate prediction of the pandemic progression in a country, region or city. A pseudo-coevolutionary algorithm is adopted in the model fitting to provide an effective estimation of high-dimensional unknown parameter sets using a limited amount of historical data. The model is applied to 175 regions in Australia, Canada, Italy, Japan, Spain, the UK and USA to estimate the number of unconfirmed cases using limited historical data. Results showed that the actual number of infected cases could be 4.309 times as many as the official confirmed number. By implementing mass COVID-19 testing, the number of infected cases could be reduced by about 50%.

Imbalanced dataset is a common issue in many applications. The one-class Support Vector Machine (SVM) is found to be an effective algorithm to construct classification models over the underlying imbalanced dataset. In some cases, feature extraction is hard and one would prefer using pre-defined kernels to train the model. In traditional practice, a valid kernel has to satisfy the Mercer's condition, which may restrict the design of kernel functions or matrices. In this paper, an indefinite kernel extension is applied to the one-class SVM model in order to relieve such limitation. To illustrate its performance, the algorithm is applied to perform virtual screening of drugs.

Natural Language Processing (NLP) empowered mobile computing is the use of NLP techniques in the context of mobile environment. Research in this field has drawn much attention given the continually increasing number of publications in the last five years. This study presents the status and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from Web of Science. Analysis techniques including a descriptive statistics method, a geographic visualization method, a social network analysis method, a latent dirichlet allocation method, and an affinity propagation clustering method are used. We quantitatively analyze the publications in terms of statistical characteristics, geographical distribution, cooperation relationship, and topic discovery and distribution. This systematic analysis of the field illustrates the publications evolution over time and identifies current research interests and potential directions for future research. Our work can potentially assist researchers in keeping abreast of the research status. It can also help monitoring new scientific and technological development in the research field.

Automatic extraction of large‐scale and accurate drug–disease pairs from the medical literature plays an important role for drug repurposing. However, many existing extraction methods are mainly in a supervised manner. It is costly and time‐consuming to manually label drug–disease pairs datasets. There are many drug–disease pairs buried in free text. In this work, we first leverage a pattern‐based method to automatically extract drug–disease pairs with treatment and inducement relationships from free text. Then, to reflect a drug–disease relation, a network embedding algorithm is proposed to calculate the degree of correlation of a drug–disease pair. In the experiments, we use the method to extract treatment and inducement drug–disease pairs from 27 million medical abstracts and titles available on PubMed. We extract 138,318 unique treatment pairs and 75,396 unique inducement pairs. Our algorithm achieves a precision of 0.912 and a recall of 0.898 in extracting the frequent treatment drug–disease pairs, and a precision of 0.923 and a recall of 0.833 in extracting the frequent inducement drug–disease pairs. Besides, our proposed information network embedding algorithm can efficiently reflect the degree of correlation of drug–disease pairs. Our algorithm can achieve a precision of 0.802, a recall of 0.783 in the fine‐grained evaluation of extracting frequent pairs.

Transfer learning techniques have been broadly applied in applications where labeled data in a target domain are difficult to obtain while a lot of labeled data are available in related source domains. In practice, there can be multiple source domains that are related to the target domain, and how to combine them is still an open problem. In this paper, we seek to leverage labeled data from multiple source domains to enhance classification performance in a target domain where the target data are received in an online fashion. This problem is known as the online transfer learning problem. To achieve this, we propose novel online transfer learning paradigms in which the source and target domains are leveraged adaptively. We consider two different problem settings: homogeneous transfer learning and heterogeneous transfer learning. The proposed methods work in an online manner, where the weights of the source domains are adjusted dynamically. We provide the mistake bounds of the proposed methods and perform comprehensive experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms.

Protein function prediction is a challenging and essential research problem in the field of computational biology. Conventionally, a protein consists of a number of structural domains and performs multiple function. By representing proteins, domains and functions by bags as well as instances and classes respectively, we are able to model the protein function prediction task as the Multi-Instance Multi-Label (MIML) learning problem. Existing MIML algorithms mainly focus on batch setting where training examples are available before learning. Such offline paradigm works well in simulation, but it may be not feasible for real-world online applications where data comes one by one or chunk by chunk. In this paper, we investigate the protein function prediction problem under a new learning framework, called Online Multi-Instance Multi-Label (OMIML) learning, where MIML protein examples arrive sequentially in an online setting, and develop two OMIML algorithms (OMIML-I and OMIML-B) to make predictions for the incoming data. In the proposed OMIML algorithms, variable-length features are constructed to represent the MIML protein examples based on an incremental vocabulary mechanism. In particular, the incremental vocabularies that OMIML-I and OMIML-B are based on consist of instances and bags, respectively. Then we seek an online prediction for each new arrived protein example by incorporating the constructed features into an online multi-label learning algorithm which is constructed by introducing an artificial label into an online multi-label ranking model. We evaluate the algorithms on the protein dataset consisting of seven real-world organisms. Experimental results have demonstrated the effectiveness of the proposed OMIML algorithms for protein function prediction.

Detecting similar question is a fundamental and essential research problem for constructing similar question dataset for the research of question-answering, short text similarity calculating, and sentence paragraphing. This paper explores the previous assumption about similar question detection and analyzes its existing problem. Afterwards, we propose an automated approach to detecting similar questions based on the calculation of question topical diversity using different ways of topical feature generation methods. The experiment dataset are Yahoo! 4,482,757 questions with answers. The results present that our approach achieves a precision of 74% and a recall of 74% as the best performance compared with baseline methods, demonstrating its effectiveness in similar question group detection.

Increasing communication among Chinese-speaking regions using respectively traditional and simplified Chinese character systems has highlighted the subtle-yet-extensive differences between the two systems, which can lead to unexpected hindrance in converting characters from one to the other. This article proposes a new priority-based multi-data resources management model, with a new algorithm called Fused Conversion algorithm from Multi-Data resources (FCMD), to ensure more context-sensitive, human controllable, and thus more reliable conversions, by drawing on reverse maximum matching, n -gram-based statistical model and pattern-based learning and matching. After parameter training on the Tagged Chinese Gigaword corpus, its conversion precision reaches 91.5% in context-sensitive cases, the most difficult part in the conversion, with an overall precision rate at 99.8%, a significant improvement over the state-of-the-art models. The conversion platform based on the model has extra features such as data resource selection and n -grams self-learning ability, providing a more sophisticated tool good especially for high-end professional uses.

Abstract A method of learning adaptation rules for case‐based reasoning (CBR) is proposed in this paper. The resource space model and the semantic link network are applied in case‐base construction for efficient resource management and reuse. Adaptation rules are generated from the case‐base with the guidance of domain knowledge, which is also extracted from the case‐base. The adaptation rules are refined before they are applied in the revision process. General domain knowledge is brought in to help accurate similarity computing. After solving each new problem, the adaptation rule set is updated by an evolution module in the retention process. The results of our experiment show that the obtained adaptation rules can improve the performance of the CBR system compared with a retrieval‐only CBR system. The average solution difference error is decreased by 46.56%. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract A new type of semantic pattern is proposed in this paper, which can be used by users to post questions and answers in user‐interactive question answering (QA) systems. The necessary procedures of using semantic patterns in a QA system are also presented, which include question structure analysis, pattern matching, pattern generation, pattern classification and answer extraction. Both the manual creation method and the automatic generation method are proposed for patterns for different applications. A pattern instantiation level metrics is also presented for the predication of the quality of generated or learned patterns. We implemented a user interface for using the semantic pattern in our QA system, which allows users to effectively post and answer questions. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract In this paper, we propose a user reputation model and apply it to a user‐interactive question answering system. It combines the social network analysis approach and the user rating approach. Social network analysis is applied to analyze the impact of participant users' relations to their reputations. User rating is used to acquire direct judgment of a user's reputation based on other users' experiences with this user. Preliminary experiments show that the computed reputations based on our proposed reputation model can reflect the actual reputations of the simulated roles and therefore can fit in well with our user‐interactive question answering system. Copyright © 2006 John Wiley & Sons, Ltd.