共查询到20条相似文献，搜索用时 31 毫秒
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《Information processing & management》2023,60(3):103314
Many Chinese NER models only focus on lexical and radical information, ignoring the fact that there are also certain rules for the pronunciation of Chinese entities. In this paper, we propose VisPhone, which incorporates Chinese characters’ Phonetic features into Transformer Encoder along with the Lattice and Visual features. We present the common rules for the pronunciation of Chinese entities and explore the most appropriate method to encode it. VisPhone uses two identical cross transformer encoders to fuse the visual and phonetic features of the input characters with the text embedding. A selective fusion module is used to get the final features. We conducted experiments on four well-known Chinese NER benchmark datasets: OntoNotes4.0, MSRA, Resume, and Weibo, with F1 scores of 82.63%, 96.07%, 96.26%, 70.79% respectively, improving the performance by 0.79%, 0.32%, 0.39%, and 3.47%. Our ablation experiments have also demonstrated the effectiveness of VisPhone. 相似文献
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Xu Sun Yaozhong Zhang Takuya Matsuzaki Yoshimasa Tsuruoka Jun’ichi Tsujii 《Information processing & management》2013
In this article, we focus on Chinese word segmentation by systematically incorporating non-local information based on latent variables and word-level features. Differing from previous work which captures non-local information by using semi-Markov models, we propose an alternative method for modeling non-local information: a latent variable word segmenter employing word-level features. In order to reduce computational complexity of learning non-local information, we further present an improved online training method, which can arrive the same objective optimum with a significantly accelerated training speed. We find that the proposed method can help the learning of long range dependencies and improve the segmentation quality of long words (for example, complicated named entities). Experimental results demonstrate that the proposed method is effective. With this improvement, evaluations on the data of the second SIGHAN CWS bakeoff show that our system is competitive with the state-of-the-art systems. 相似文献
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Named entity recognition (NER) is mostly formalized as a sequence labeling problem in which segments of named entities are represented by label sequences. Although a considerable effort has been made to investigate sophisticated features that encode textual characteristics of named entities (e.g. PEOPLE, LOCATION, etc.), little attention has been paid to segment representations (SRs) for multi-token named entities (e.g. the IOB2 notation). In this paper, we investigate the effects of different SRs on NER tasks, and propose a feature generation method using multiple SRs. The proposed method allows a model to exploit not only highly discriminative features of complex SRs but also robust features of simple SRs against the data sparseness problem. Since it incorporates different SRs as feature functions of Conditional Random Fields (CRFs), we can use the well-established procedure for training. In addition, the tagging speed of a model integrating multiple SRs can be accelerated equivalent to that of a model using only the most complex SR of the integrated model. Experimental results demonstrate that incorporating multiple SRs into a single model improves the performance and the stability of NER. We also provide the detailed analysis of the results. 相似文献
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《Information processing & management》2023,60(3):103290
Due to the particularity of Chinese word formation, the Chinese Named Entity Recognition (NER) task has attracted extensive attention over recent years. Recently, some researchers have tried to solve this problem by using a multimodal method combining acoustic features and text features. However, the text-speech data pairs required by the above methods are lacking in real-world scenarios, making it difficult to apply widely. To address this, we proposed a multimodal Chinese NER method called USAF, which uses synthesized acoustic features instead of actual human speech. USAF aligns text and acoustic features through unique position embeddings and uses a multi-head attention mechanism to fuse the features of the two modalities, which stably improves the performance of Chinese named entity recognition. To evaluate USAF, we implemented USAF on three Chinese NER datasets. Experimental results show that USAF witnesses a stable improvement compare to text-only methods on each dataset, and outperforms SOTA external-vocabulary-based method on two datasets. Specifically, compared to the SOTA external-vocabulary-based method, the F1 score of USAF is improved by 1.84 and 1.24 on CNERTA and Aishell3-NER, respectively. 相似文献
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近几年来,越来越多的语言学家开始关注母语对第二语言习得的迁移影响,以期帮助外语学习者更好地掌握外语。本文将在充分比较英语和汉语音节结构和重音差异的基础上探索学生英语发音错误产生的原因,并希望能对学生的英语学习和教师的教学工作产生积极的影响。 相似文献
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介绍命名实体识别在文本信息处理领域的重要地位,分析了中文命名实体识别存在的困难,介绍中文命名实体识别的一般过程、评价标准及方法。提出了一种在构造内部规则和外部规则的同时采用概率统计的中文命名实体的识别方法,并利用这种基于规则和统计相结合的方法。实验证明该方法获得了较高的准确率和召回率,具有可行性和合理性,同时也指出了它的局限性。 相似文献
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[目的/意义] 从跨语言视角探究如何更好地解决低资源语言的实体抽取问题。[方法/过程] 以英语为源语言,西班牙语和荷兰语为目标语言,借助迁移学习和深度学习的思想,提出一种结合自学习和GRU-LSTM-CRF网络的无监督跨语言实体抽取方法。[结果/结论] 与有监督的跨语言实体抽取方法相比,本文提出的无监督跨语言实体抽取方法可以取得更好的效果,在西班牙语上,F1值为0.6419,在荷兰语上,F1值为0.6557。利用跨语言知识在源语言和目标语言间建立桥梁,提升低资源语言实体抽取的效果。 相似文献
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[目的/意义]基于数据科学与情报学领域的密切联系,对数据科学任职要求知识进行深入挖掘,有利于掌握社会对于情报学相关领域人才的需求,从而完善情报学教育的培养方案,帮助实现社会需求与高校教育的良好对接。[方法/过程]文章采集了国内主流招聘网站中数据科学相关工作岗位的招聘信息,并对数据进行解析、去重等清洗工作,对招聘信息中的任职要求实体进行人工标注,比较了LSTM,BiLSTM-CRF和BERT三种深度学习模型应用于实体识别的效果。[结果/结论]结果表明,BiLSTM-CRF模型对任职要求实体的识别效果最好,相较于其他两种深度学习模型具有一定的优势。文章根据抽取出的任职要求实体从实践能力、学历要求、脚本语言、数据处理、综合素质等方面总结了目前情报学人才应当具备的技能和素质,并由此提出了针对情报学教育的人才培养方案。 相似文献
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《Information processing & management》2023,60(3):103322
Using AI technology to automatically match Q&A pairs on online health platforms (OHP) can improve the efficiency of doctor-patient interaction. However, previous methods often neglected to fully exploit rich information contained in OHP, especially the medical expertise that could be leveraged through medical text modeling. Therefore, this paper proposes a model named MKGA-DM-NN, which first uses the named entities of the medical knowledge graph (KG) to identify the intention of the problem, and then uses graph embedding technology to learn the representation of entities and entity relationships in the KG. The proposed model also employs the relationship between entities in KG to optimize the hybrid attention mechanism. In addition, doctors' historical Q&A records on OHP are used to learn modeling doctors’ expertise to improve the accuracy of Q&A matching. This method is helpful to bridge the semantic gap of text and improve the accuracy and interpretability of medical Q&A matching. Through experiments on a real dataset from a Chinese well-known OHP, our model has been verified to be superior to the baseline models. The accuracy of our model is 4.4% higher than the best baseline model. The cost-sensitive error of our model is 13.53% lower than that of the best baseline model. The ablation experiment shows that the accuracy rate can be significantly improved by 8.72% by adding the doctor modeling module, and the cost-sensitive error can be significantly reduced by 17.27% by adding the medical KG module. 相似文献
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Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities. 相似文献
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《Information processing & management》2020,57(6):102311
Overlapping entity relation extraction has received extensive research attention in recent years. However, existing methods suffer from the limitation of long-distance dependencies between entities, and fail to extract the relations when the overlapping situation is relatively complex. This issue limits the performance of the task. In this paper, we propose an end-to-end neural model for overlapping relation extraction by treating the task as a quintuple prediction problem. The proposed method first constructs the entity graphs by enumerating possible candidate spans, then models the relational graphs between entities via a graph attention model. Experimental results on five benchmark datasets show that the proposed model achieves the current best performance, outperforming previous methods and baseline systems by a large margin. Further analysis shows that our model can effectively capture the long-distance dependencies between entities in a long sentence. 相似文献
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《Information processing & management》2022,59(5):103004
Although the Knowledge Graph (KG) has been successfully applied to various applications, there is still a large amount of incomplete knowledge in the KG. This study proposes a Knowledge Graph Completion (KGC) method based on the Graph Attention Faded Mechanism (GAFM) to solve the problem of incomplete knowledge in KG. GAFM introduces a graph attention network that incorporates the information in multi-hop neighborhood nodes to embed the target entities into low dimensional space. To generate a more expressive entity representation, GAFM gives different weights to the neighborhood nodes of the target entity by adjusting the attention value of neighborhood nodes according to the variation of the path length. The attention value is adjusted by the attention faded coefficient, which decreases with the increase of the distance between the neighborhood node and the target entity. Then, considering that the capsule network has the ability to fit features, GAFM introduces the capsule network as the decoder to extract feature information from triple representations. To verify the effectiveness of the proposed method, we conduct a series of comparative experiments on public datasets (WN18RR and FB15k-237). Experimental results show that the proposed method outperforms baseline methods. The Hits@10 metric is improved by 8% compared with the second-place KBGAT method. 相似文献
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《Information processing & management》2022,59(1):102790
Entity alignment is an important task for the Knowledge Graph (KG) completion, which aims to identify the same entities in different KGs. Most of previous works only utilize the relation structures of KGs, but ignore the heterogeneity of relations and attributes of KGs. However, these information can provide more feature information and improve the accuracy of entity alignment. In this paper, we propose a novel Multi-Heterogeneous Neighborhood-Aware model (MHNA) for KGs alignment. MHNA aggregates multi-heterogeneous information of aligned entities, including the entity name, relations, attributes and attribute values. An important contribution is to design a variant attention mechanism, which adds the feature information of relations and attributes to the calculation of attention coefficients. Extensive experiments on three well-known benchmark datasets show that MHNA significantly outperforms 12 state-of-the-art approaches, demonstrating that our approach has good scalability and superiority in both cross-language and monolingual KGs. An ablation study further supports the effectiveness of our variant attention mechanism. 相似文献
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We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities. 相似文献
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《Information processing & management》2022,59(1):102735
Despite the known financial, economical, and humanitarian impacts of hurricanes and the floods that follow, datasets consisting of flood and flood risk reduction projects are either small in scope, lack in details, or held privately by commercial holders. However, with the amount of online data growing exponentially, we have seen a rise of information extraction techniques on unstructured text to drive insights. On one hand, social media in particular has seen a tremendous increase in popularity. On the other hand, despite this popularity, social media has proven to be unreliable and difficult to extract full information from. In contrast, online newspapers are often vetted by a journalist, and consist of more fine details. As a result, in this paper we leverage Natural Language Processing (NLP) to create a hybrid Named-Entity Recognition (NER) model that employs a domain-specific machine learning model, linguistic features, and rule-based matching to extract information from newspapers. To the knowledge of the authors, this model is the first of its kind to extract detailed flooding information and risk reduction projects over the entire contiguous United States. The approach used in this paper expands upon previous similar works by widening the geographical location and applying techniques to extract information over large documents, with minimal accuracy loss from the previous methods. Specifically, our model is able to extract information such as street closures, project costs, and metrics. Our validation indicates an F1 score of 72.13% for the NER model entity extraction, a binary classification location filter with a score of 73%, and an overall performance only 8.4% lower than a human validator against a gold-standard. Through this process, we find the location of 27,444 streets, 181,076 flood risk reduction projects, and 435,353 storm locations throughout the United States in the past two decades. 相似文献
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《Information processing & management》2023,60(2):103174
The majority of currently available entity alignment (EA) solutions primarily rely on structural information to align entities, which is biased and disregards additional multi-source information. To compensate for inadequate structural details, this article suggests the SKEA framework, which is a simple but flexible framework for Entity Alignment with cross-modal supervision of Supporting Knowledge. We employ a relational aggregate network to specifically utilize the details about the entity and its neighbors. To overcome the limitations of relational features, two multi-modal encode modules are being used to extract visual and textural information. A new set of potential aligned entity pairs are generated by SKEA in each iteration using the knowledge of two reference modalities, which can enhance the model’s supervision. It is important to note that the supporting information used in our framework does not participate in the network’s backpropagation, which considerably improves efficiency and differs dramatically from earlier work. In comparison to existing baselines, experiments demonstrate that our proposed framework can incorporate multi-aspect information efficiently and enable supervisory signals from other modalities to transmit to entities. The maximum performance improvement of 5.24% indicates our suggested framework’s superiority, especially for sparse KGs. 相似文献
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《Information processing & management》2022,59(4):102957
The identification of knowledge graph entity mentions in textual content has already attracted much attention. The major assumption of existing work is that entities are explicitly mentioned in text and would only need to be disambiguated and linked. However, this assumption does not necessarily hold for social content where a significant portion of information is implied. The focus of our work in this paper is to identify whether textual social content include implicit mentions of knowledge graph entities or not, hence forming a two-class classification problem. To this end, we adopt the systemic functional linguistic framework that allows for capturing meaning expressed through language. Based on this theoretical framework we systematically introduce two classes of features, namely syntagmatic and paradigmatic features, for implicit entity recognition. In our experiments, we show the utility of these features for the task, report on ablation studies, measure the impact of each feature subset on each other and also provide a detailed error analysis of our technique. 相似文献
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In this paper, we propose a new learning method for extracting bilingual word pairs from parallel corpora in various languages. In cross-language information retrieval, the system must deal with various languages. Therefore, automatic extraction of bilingual word pairs from parallel corpora with various languages is important. However, previous works based on statistical methods are insufficient because of the sparse data problem. Our learning method automatically acquires rules, which are effective to solve the sparse data problem, only from parallel corpora without any prior preparation of a bilingual resource (e.g., a bilingual dictionary, a machine translation system). We call this learning method Inductive Chain Learning (ICL). Moreover, the system using ICL can extract bilingual word pairs even from bilingual sentence pairs for which the grammatical structures of the source language differ from the grammatical structures of the target language because the acquired rules have the information to cope with the different word orders of source language and target language in local parts of bilingual sentence pairs. Evaluation experiments demonstrated that the recalls of systems based on several statistical approaches were improved through the use of ICL. 相似文献