共查询到20条相似文献，搜索用时 796 毫秒
1.
Bernard J. Jansen 《Information processing & management》2018,54(2):159-174
With the noted popularity of social networking sites, people increasingly rely on these social networks to address their information needs. Although social question and answering is potentially an important venue seeking information online, it, unfortunately, suffers from a problem of low response rate, with the majority of questions receiving no response. To understand why the response rate of social question and answering is low and hopefully to increase it in the future, this research analyzes extrinsic factors that may influence the response probability of questions posted on Sina Weibo. We propose 17 influential factors from 2 different perspectives: the content of the question, and the characteristics of the questioner. We also train a prediction model to forecast a question's likelihood of being responded based on the proposed features We test our predictive model on more than 60,000 real-world questions posted on Weibo, which generate more than 600,000 responses. Findings show that a Weibo's question answerability is primarily contingent on the questioner versus the question. Our findings indicate that using appreciation emojis can increase a question's response probability, whereas the use of hashtags negatively influences the chances of receiving answers. Our contribution is in providing insights for the design and development of future social question and answering tools, as well as for enhancing social network users’ collaboration in supporting social information seeking activities. 相似文献
2.
Community question answering (CQA) services that enable users to ask and answer questions have become popular on the internet. However, lots of new questions usually cannot be resolved by appropriate answerers effectively. To address this question routing task, in this paper, we treat it as a ranking problem and rank the potential answerers by the probability that they are able to solve the given new question. We utilize tensor model and topic model simultaneously to extract latent semantic relations among asker, question and answerer. Then, we propose a learning procedure based on the above models to get optimal ranking of answerers for new questions by optimizing the multi-class AUC (Area Under the ROC Curve). Experimental results on two real-world CQA datasets show that the proposed method is able to predict appropriate answerers for new questions and outperforms other state-of-the-art approaches. 相似文献
3.
Lanting Fang Luu Anh Tuan Siu Cheung Hui Lenan Wu 《Information processing & management》2018,54(2):184-202
With the popularity of online educational platforms, English learners can learn and practice no matter where they are and what they do. English grammar is one of the important components in learning English. To learn English grammar effectively, it requires students to practice questions containing focused grammar knowledge. In this paper, we study a novel problem of retrieving English grammar questions with similar grammatical focus. Since the grammatical focus similarity is different from textual similarity or sentence syntactic similarity, existing approaches cannot be applied directly to our problem. To address this problem, we propose a syntactic based approach for English grammar question retrieval which can retrieve related grammar questions with similar grammatical focus effectively. In the proposed syntactic based approach, we first propose a new syntactic tree, namely parse-key tree, to capture English grammar questions’ grammatical focus. Next, we propose two kernel functions, namely relaxed tree kernel and part-of-speech order kernel, to compute the similarity between two parse-key trees of the query and grammar questions in the collection. Then, the retrieved grammar questions are ranked according to the similarity between the parse-key trees. In addition, if a query is submitted together with answer choices, conceptual similarity and textual similarity are also incorporated to further improve the retrieval accuracy. The performance results have shown that our proposed approach outperforms the state-of-the-art methods based on statistical analysis and syntactic analysis. 相似文献
4.
《Information processing & management》2019,56(2):274-290
In this paper we focus on the problem of question ranking in community question answering (cQA) forums in Arabic. We address the task with machine learning algorithms using advanced Arabic text representations. The latter are obtained by applying tree kernels to constituency parse trees combined with textual similarities, including word embeddings. Our two main contributions are: (i) an Arabic language processing pipeline based on UIMA—from segmentation to constituency parsing—built on top of Farasa, a state-of-the-art Arabic language processing toolkit; and (ii) the application of long short-term memory neural networks to identify the best text fragments in questions to be used in our tree-kernel-based ranker. Our thorough experimentation on a recently released cQA dataset shows that the Arabic linguistic processing provided by Farasa produces strong results and that neural networks combined with tree kernels further boost the performance in terms of both efficiency and accuracy. Our approach also enables an implicit comparison between different processing pipelines as our tests on Farasa and Stanford parsers demonstrate. 相似文献
5.
《Information processing & management》2023,60(5):103421
Semantic information in judgement documents has been an important source in Artificial Intelligence and Law. Sequential representation is the traditional structure for analyzing judgement documents and supporting the legal charge prediction task. The main problem is that it is not effective to represent the criminal semantic information. In this paper, to represent and verify the criminal semantic information such as multi-linked legal features, we propose a novel criminal semantic representation model, which constructs the Criminal Action Graph (CAG) by extracting criminal actions linked in two temporal relationships. Based on the CAG, a Graph Convolutional Network is also adopted as the predictor for legal charge prediction. We evaluate the validity of CAG on the confusing charges which composed of 32,000 judgement documents on five confusing charge sets. The CAG reaches about 88% accuracy averagely, more than 3% over the compared model. The experimental standard deviation also show the stability of our model, which is about 0.0032 on average, nearly 0. The results show the effectiveness of our model for representing and using the semantic information in judgement documents. 相似文献
6.
Danushka Bollegala Tomokazu Goto Nguyen Tuan Duc Mitsuru Ishizuka 《Information processing & management》2013
Measuring the similarity between the semantic relations that exist between words is an important step in numerous tasks in natural language processing such as answering word analogy questions, classifying compound nouns, and word sense disambiguation. Given two word pairs (A, B) and (C, D), we propose a method to measure the relational similarity between the semantic relations that exist between the two words in each word pair. Typically, a high degree of relational similarity can be observed between proportional analogies (i.e. analogies that exist among the four words, A is to B such as C is to D). We describe eight different types of relational symmetries that are frequently observed in proportional analogies and use those symmetries to robustly and accurately estimate the relational similarity between two given word pairs. We use automatically extracted lexical-syntactic patterns to represent the semantic relations that exist between two words and then match those patterns in Web search engine snippets to find candidate words that form proportional analogies with the original word pair. We define eight types of relational symmetries for proportional analogies and use those as features in a supervised learning approach. We evaluate the proposed method using the Scholastic Aptitude Test (SAT) word analogy benchmark dataset. Our experimental results show that the proposed method can accurately measure relational similarity between word pairs by exploiting the symmetries that exist in proportional analogies. The proposed method achieves an SAT score of 49.2% on the benchmark dataset, which is comparable to the best results reported on this dataset. 相似文献
7.
In this paper, we propose a generative model, the Topic-based User Interest (TUI) model, to capture the user interest in the User-Interactive Question Answering (UIQA) systems. Specifically, our method aims to model the user interest in the UIQA systems with latent topic method, and extract interests for users by mining the questions they asked, the categories they participated in and relevant answer providers. We apply the TUI model to the application of question recommendation, which automatically recommends to certain user appropriate questions he might be interested in. Data collection from Yahoo! Answers is used to evaluate the performance of the proposed model in question recommendation, and the experimental results show the effectiveness of our proposed model. 相似文献
8.
《Information processing & management》2023,60(5):103451
Document-level relation extraction (RE) aims to extract the relation of entities that may be across sentences. Existing methods mainly rely on two types of techniques: Pre-trained language models (PLMs) and reasoning skills. Although various reasoning methods have been proposed, how to elicit learnt factual knowledge from PLMs for better reasoning ability has not yet been explored. In this paper, we propose a novel Collective Prompt Tuning with Relation Inference (CPT-RI) for Document-level RE, that improves upon existing models from two aspects. First, considering the long input and various templates, we adopt a collective prompt tuning method, which is an update-and-reuse strategy. A generic prompt is first encoded and then updated with exact entity pairs for relation-specific prompts. Second, we introduce a relation inference module to conduct global reasoning overall relation prompts via constrained semantic segmentation. Extensive experiments on two publicly available benchmark datasets demonstrate the effectiveness of our proposed CPT-RI as compared to the baseline model (ATLOP (Zhou et al., 2021)), which improve the 0.57% on the DocRED dataset, 2.20% on the CDR dataset, and 2.30 on the GDA dataset in the F1 score. In addition, further ablation studies also verify the effects of the collective prompt tuning and relation inference. 相似文献
9.
《Information processing & management》2022,59(4):102961
Stress and depression detection on social media aim at the analysis of stress and identification of depression tendency from social media posts, which provide assistance for the early detection of mental health conditions. Existing methods mainly model the mental states of the post speaker implicitly. They also lack the ability to mentalise for complex mental state reasoning. Besides, they are not designed to explicitly capture class-specific features. To resolve the above issues, we propose a mental state Knowledge–aware and Contrastive Network (KC-Net). In detail, we first extract mental state knowledge from a commonsense knowledge base COMET, and infuse the knowledge using Gated Recurrent Units (GRUs) to explicitly model the mental states of the speaker. Then we propose a knowledge–aware mentalisation module based on dot-product attention to accordingly attend to the most relevant knowledge aspects. A supervised contrastive learning module is also utilised to fully leverage label information for capturing class-specific features. We test the proposed methods on a depression detection dataset Depression_Mixed with 3165 Reddit and blog posts, a stress detection dataset Dreaddit with 3553 Reddit posts, and a stress factors recognition dataset SAD with 6850 SMS-like messages. The experimental results show that our method achieves new state-of-the-art results on all datasets: 95.4% of F1 scores on Depression_Mixed, 83.5% on Dreaddit and 77.8% on SAD, with 2.07% average improvement. Factor-specific analysis and ablation study prove the effectiveness of all proposed modules, while UMAP analysis and case study visualise their mechanisms. We believe our work facilitates detection and analysis of depression and stress on social media data, and shows potential for applications on other mental health conditions. 相似文献
10.
《Information processing & management》2023,60(5):103411
Knowledge representation learning(KRL) transforms knowledge graph(KG) from symbol space to vector space. However, KRL under open world assumption(OWA) is deeply trapped in the dilemma of lack of labels due to difficulty or high cost in labeling. To address this problem, we propose KRL_MLCCL:Multi-Label Classification based on Contrastive Learning(CL) Knowledge Representation Learning method. Specifically, (1)we formalize a problem of solving true knowledge graph objects(KGOs) matchings(KGOMs) under the OWA in the original KGOM sample space(KGOMSS)(multi-label classification with one known true matching(positive-example)). (2)we solve the problem in the new KGOMSS, generated through augmenting the true matching according to CL’s idea(multi-label classification with multiple known true matching). (3)we score the true matchings based on hermitian inner product and softmax and minimize a negative logarithm likelihood loss to establish KRL_MLCCL model preliminarily. (4)we migrate the learned model back to the original KGOMSS to solve the true matching problem. We creatively design and apply a positive-example augmentation way of CL enabling KRL_MLCCL with back migration ability: “pulling KGOs in true matching close and pushing KGOs in false matching away”, which helps KRL out of the labels shortage dilemma faced in modeling. We also propose a negative-example noise filtering algorithm to enhance this ability. The open world entity prediction(OWEP) experiment on dataset FB15K-237-OWE shows that the performance of KRL_MLCCL is increased by 3% in Hits@10 and 1.32% in MRR compared with the state-of-the-art in the baselines. The experiments of OWEP in KG also show that KRL_MLCCL has a better back migration ability. 相似文献
11.
Wanpeng Song Liu Wenyin Naijie Gu Xiaojun Quan Tianyong Hao 《Information processing & management》2011
Question categorization, which suggests one of a set of predefined categories to a user’s question according to the question’s topic or content, is a useful technique in user-interactive question answering systems. In this paper, we propose an automatic method for question categorization in a user-interactive question answering system. This method includes four steps: feature space construction, topic-wise words identification and weighting, semantic mapping, and similarity calculation. We firstly construct the feature space based on all accumulated questions and calculate the feature vector of each predefined category which contains certain accumulated questions. When a new question is posted, the semantic pattern of the question is used to identify and weigh the important words of the question. After that, the question is semantically mapped into the constructed feature space to enrich its representation. Finally, the similarity between the question and each category is calculated based on their feature vectors. The category with the highest similarity is assigned to the question. The experimental results show that our proposed method achieves good categorization precision and outperforms the traditional categorization methods on the selected test questions. 相似文献
12.
《Information processing & management》2023,60(3):103296
Recent studies point out that VQA models tend to rely on the language prior in the training data to answer the questions, which prevents the VQA model from generalization on the out-of-distribution test data. To address this problem, approaches are designed to reduce the language distribution prior effect by constructing negative image–question pairs, while they cannot provide the proper visual reason for answering the question. In this paper, we present a new debiasing framework for VQA by Learning to Sample paired image–question and Prompt for given question (LSP). Specifically, we construct the negative image–question pairs with certain sampling rate to prevent the model from overly relying on the visual shortcut content. Notably, question types provide a strong hint for answering the questions. We utilize question type to constrain the sampling process for negative question–image pairs, and further learn the question type-guided prompt for better question comprehension. Extensive experiments on two public benchmarks, VQA-CP v2 and VQA v2, demonstrate that our model achieves new state-of-the-art results in overall accuracy, i.e., 61.95% and 65.26%. 相似文献
13.
This paper describes how questions can be characterized for question answering (QA) along different facets and focuses on questions that cannot be answered directly but can be divided into simpler ones so that they can be answered directly using existing QA capabilities. Since individual answers are composed to generate the final answer, we call this process as compositional QA. The goal of the proposed QA method is to answer a composite question by dividing it into atomic ones, instead of developing an entirely new method tailored for the new question type. A question is analyzed automatically to determine its class, and its sub-questions are sent to the relevant QA modules. Answers returned from the individual QA modules are composed based on the predetermined plan corresponding to the question type. The experimental results based on 615 questions show that the compositional QA approach outperforms the simple routing method by about 17%. Considering 115 composite questions only, the F-score was almost tripled from the baseline. 相似文献
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15.
Yanxiang Ling Fei Cai Xuejun Hu Jun Liu Wanyu Chen Honghui Chen 《Information processing & management》2021,58(1):102392
Incorporating topic information can help response generation models to produce informative responses for chat-bots. Previous work only considers the individual semantic of each topic, ignoring its specific dialog context, which may result in inaccurate topic representation and hurt response coherence. Besides, as an important feature of multi-turn conversation, dynamic topic transitions have not been well-studied. We propose a Context-Controlled Topic-Aware neural response generation model, i.e., CCTA, which makes dialog context interact with the process of topic representing and transiting to achieve balanced improvements on response informativeness and contextual coherence. CCTA focuses on capturing the semantical relations within topics as well as their corresponding contextual information in conversation, to produce context-dependent topic representations at the word-level and turn-level. Besides, CCTA introduces a context-controlled topic transition strategy, utilizing contextual topics to yield relevant transition words. Extensive experimental results on two benchmark multi-turn conversation datasets validate the superiority of our proposal on generating coherent and informative responses against the state-of-the-art baselines. We also find that topic transition modeling can work as an auxiliary learning task to boost the response generation. 相似文献
16.
《Information processing & management》2022,59(3):102863
Few-shot intent recognition aims to identify user’s intent from the utterance with limited training data. A considerable number of existing methods mainly rely on the generic knowledge acquired on the base classes to identify the novel classes. Such methods typically ignore the characteristics of each meta task itself, resulting in the inability to make full use of limited given samples when classifying unseen classes. To deal with such issues, we propose a Contrastive learning-based Task Adaptation model (CTA) for few-shot intent recognition. In detail, we leverage contrastive learning to help achieve task adaptation and make full use of the limited samples of novel classes. First, a self-attention layer is employed in the task adaptation module, which aims to establish interactions between samples of different categories so that new representations are task-specific rather than relying entirely on the base classes. Then, the contrastive-based loss functions and the semantics of the label name are respectively used for reducing the similarity between sample representations in different categories while increasing it in the same categories. Experimental results on a public dataset OOS verify the effectiveness of our proposal by beating the competitive baselines in terms of accuracy. Besides, we conduct the cross-domain experiments on three datasets, i.e., OOS, SNIPS as well as ATIS. We find that CTA gains obvious improvements in terms of accuracy in all cross-domain experiments, indicating that it has a better generalization ability than other competitive baselines in both cross-domain and single-domain settings. 相似文献
17.
《Information processing & management》2023,60(3):103331
Sequential recommendation models a user’s historical sequence to predict future items. Existing studies utilize deep learning methods and contrastive learning for data augmentation to alleviate data sparsity. However, these existing methods cannot learn accurate high-quality item representations while augmenting data. In addition, they usually ignore data noise and user cold-start issues. To solve the above issues, we investigate the possibility of Generative Adversarial Network (GAN) with contrastive learning for sequential recommendation to balance data sparsity and noise. Specifically, we propose a new framework, Enhanced Contrastive Learning with Generative Adversarial Network for Sequential Recommendation (ECGAN-Rec), which models the training process as a GAN and recommendation task as the main task of the discriminator. We design a sequence augmentation module and a contrastive GAN module to implement both data-level and model-level augmentations. In addition, the contrastive GAN learns more accurate high-quality item representations to alleviate data noise after data augmentation. Furthermore, we propose an enhanced Transformer recommender based on GAN to optimize the performance of the model. Experimental results on three open datasets validate the efficiency and effectiveness of the proposed model and the ability of the model to balance data noise and data sparsity. Specifically, the improvement of ECGAN-Rec in two evaluation metrics (HR@N and NDCG@N) compared to the state-of-the-art model performance on the Beauty, Sports and Yelp datasets are 34.95%, 36.68%, and 13.66%, respectively. Our implemented model is available via https://github.com/nishawn/ECGANRec-master. 相似文献
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19.
【目的/意义】解决自动问答系统构建过程中数据集构建成本高的问题,以及自动问答过程中仅考虑问题或
答案本身相关性的局限。【方法/过程】提出了一种融合标注问答库和社区问答数据的数据集构建方法,构建问题关
键词-问题-答案-答案簇多层异构网络模型,并给出了基于该模型的自动问答算法。获取图书馆语料进行处理作
为实验数据,将BERT-Cos、AINN、BiMPM模型作为对比对象进行了实验与分析。【结果/结论】通过实验得到了各
模型在图书馆自动问答任务上的效果,本文所提模型在各评价指标上均优于其他模型,模型准确率达87.85%。【创
新/局限】本文提出的多数据源融合数据集构建方法和自动问答模型在问答任务中相对于已有方法具有更好的表
现,同时根据模型效果分析给出用户提问词长建议。 相似文献
20.
《Information processing & management》2023,60(5):103467
Extractive summarization for academic articles in natural sciences and medicine has attracted attention for a long time. However, most existing extractive summarization models often process academic articles with sentence classification models, which are hard to produce comprehensive summaries. To address this issue, we explore a new view to solve the extractive summarization of academic articles in natural sciences and medicine by taking it as a question-answering process. We propose a novel framework, MRC-Sum, where the extractive summarization for academic articles in natural sciences and medicine is cast as an MRC (Machine Reading Comprehension) task. To instantiate MRC-Sum, article-summary pairs in the summarization datasets are firstly reconstructed into (Question, Answer, Context) triples in the MRC task. Several questions are designed to cover the main aspects (e.g. Background, Method, Result, Conclusion) of the articles in natural sciences and medicine. A novel strategy is proposed to solve the problem of the non-existence of the ground truth answer spans. Then MRC-Sum is trained on the reconstructed datasets and large-scale pre-trained models. During the inference stage, four answer spans of the predefined questions are given by MRC-Sum and concatenated to form the final summary for each article. Experiments on three publicly available benchmarks, i.e., the Covid, PubMed, and arXiv datasets, demonstrate the effectiveness of MRC-Sum. Specifically, MRC-Sum outperforms advanced extractive summarization baselines on the Covid dataset and achieves competitive results on the PubMed and arXiv datasets. We also propose a novel metric, COMPREHS, to automatically evaluate the comprehensiveness of the system summaries for academic articles in natural sciences and medicine. Abundant experiments are conducted and verified the reliability of the proposed metric. And the results of the COMPREHS metric show that MRC-Sum is able to generate more comprehensive summaries than the baseline models. 相似文献