检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

759 篇 会议
22 篇 期刊文献
10 册 图书

馆藏范围

791 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

641 篇 工学
- 623 篇 计算机科学与技术...
- 500 篇 软件工程
- 88 篇 信息与通信工程
- 34 篇 化学工程与技术
- 32 篇 生物工程
- 22 篇 控制科学与工程
- 18 篇 机械工程
- 13 篇 电气工程
- 11 篇 农业工程
- 11 篇 生物医学工程（可授...
- 7 篇 材料科学与工程（可...
186 篇 管理学
- 154 篇 图书情报与档案管...
- 46 篇 管理科学与工程(可...
- 24 篇 工商管理
184 篇 理学
- 99 篇 数学
- 38 篇 生物学
- 35 篇 统计学（可授理学、...
- 34 篇 化学
- 27 篇 物理学
- 14 篇 系统科学
29 篇 文学
- 27 篇 外国语言文学
- 5 篇 中国语言文学
27 篇 医学
- 24 篇 基础医学(可授医学...
- 19 篇 临床医学
- 9 篇 公共卫生与预防医...
- 8 篇 药学(可授医学、理...
23 篇 法学
- 20 篇 社会学
14 篇 教育学
- 12 篇 教育学
11 篇 农学
- 11 篇 作物学
6 篇 经济学
- 6 篇 应用经济学
1 篇 军事学
1 篇 艺术学

主题

182 篇 natural language...
57 篇 graphic methods
53 篇 natural language...
36 篇 computational li...
24 篇 semantics
14 篇 deep learning
13 篇 iterative method...
12 篇 syntactics
11 篇 natural language...
11 篇 classification (...
10 篇 sentiment analys...
9 篇 machine translat...
9 篇 knowledge graph
9 篇 data mining
9 篇 text processing
9 篇 extraction
8 篇 heuristic method...
7 篇 speech recogniti...
7 篇 machine learning
6 篇 artificial intel...

机构

5 篇 natural language...
5 篇 institute for na...
4 篇 language technol...
3 篇 lviv polytechnic...
3 篇 sichuan tianfu b...
3 篇 key laboratory o...
3 篇 department of co...
3 篇 handan institute...
3 篇 national enginee...
3 篇 natural language...
3 篇 baidu inc.
3 篇 carnegie mellon ...
3 篇 department of co...
3 篇 center for langu...
3 篇 idiap research i...
3 篇 language technol...
3 篇 china academy of...
2 篇 department of in...
2 篇 human language t...
2 篇 singapore univer...

作者

6 篇 ney hermann
5 篇 biemann chris
5 篇 dredze mark
5 篇 waibel alex
4 篇 liu qun
4 篇 krallinger marti...
4 篇 llopis fernando
4 篇 daelemans walter
4 篇 muñoz rafael
4 篇 vysotska victori...
4 篇 zhang qi
3 篇 pei jian
3 篇 zhou guodong
3 篇 malmasi shervin
3 篇 guo xiaojie
3 篇 thayaparan mokan...
3 篇 aras hidir
3 篇 gascó luis
3 篇 bao-liang lu
3 篇 xia yinglong

语言

768 篇 英文
16 篇 其他
2 篇 西班牙文
2 篇 中文
1 篇 法文
1 篇 俄文
1 篇 乌克兰文

检索条件"任意字段=Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing"

共 791 条记录，以下是181-190 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Online News Event Extraction for Crime Analysis 30

Online News Event Extraction for Crime Analysis

引用

30th Italian Symposium on Advanced Database Systems, SEBD 2022

作者： Rollo, Federica Po, Laura Bonisoli, Giovanni Enzo Ferrari Engineering Department University of Modena and Reggio Emilia Via Vivarelli 10 Modena Italy

Event Extraction is a complex and interesting topic in Information Extraction that includes methods for the identification of event's type, participants, location, and date from free text or web data. The result of event extraction systems can be used in several fields, such as online monitoring systems or decision support tools. In this paper, we introduce a framework that combines several techniques (lexical, semantic, machine learning, neural networks) to extract events from Italian news articles for crime analysis purposes. Furthermore, we concentrate to represent the extracted events in a Knowledge graph. An evaluation on crimes in the province of Modena is reported. © 2022 CEUR-WS. All rights reserved.

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Towards Procedural Fairness: Uncovering Biases in How a Toxic language Classifier Uses Sentiment Information 5

Towards Procedural Fairness: Uncovering Biases in How a Toxi...

引用

5th workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2022 hosted by the 2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Nejadgholi, Isar Balkır, Esma Fraser, Kathleen C. Kiritchenko, Svetlana National Research Council Canada Ottawa Canada

ISBN: (纸本)9781959429050

Previous works on the fairness of toxic language classifiers compare the output of models with different identity terms as input features but do not consider the impact of other important concepts present in the context. Here, besides identity terms, we take into account high-level latent features learned by the classifier and investigate the interaction between these features and identity terms. For a multi-class toxic language classifier, we leverage a concept-based explanation framework to calculate the sensitivity of the model to the concept of sentiment, which has been used before as a salient feature for toxic language detection. Our results show that although for some classes the classifier has learned the sentiment information as expected, this information is outweighed by the influence of identity terms as input features. This work is a step towards evaluating procedural fairness, where unfair processes lead to unfair outcomes. The produced knowledge can guide debiasing techniques to ensure that important concepts besides identity terms are well-represented in training datasets. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Automated bot detection based on coherence metric 8

Automated bot detection based on coherence metric

引用

8th International Scientific Conference "Information Technology and Implementation", IT and I 2021

作者： Marchenko, Oleksandr Isoieva, Mariam Taras Shevchenko National University of Kyiv 60 Volodymyrska Str Kyiv01033 Ukraine

This paper describes a model of bot detection based on a coherence metric. Much attention is being paid to natural language generation technologies since the middle of the previous century, many companies invest in research in this direction. The quality of automatically generated texts becomes better. Nowadays, it is hard to distinguish machine-generated texts from those written by humans. While this can be used for helping people in automating even creative tasks, there is also a downside of such technologies being widely available, e. g. easier spread of fake news and propaganda. That is why it is also important to develop efficient bot detection methods for having misinformation protection tools. Here an attempt to create such a model is made. One of the main distinguishing features of high-quality texts is coherence. This is also what automatically generated texts, especially long ones, often lack in. A classifier with a set of features based on a coherence metric and syntactic characteristics has been built. The method can be extended and used for different languages. © 2022 CEUR-WS. All rights reserved.

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Using Part-of-Speech Tagging for Building Networks of Terms in Legal Sphere 5

Using Part-of-Speech Tagging for Building Networks of Terms ...

引用

5th International Conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Lande, Dmytro Dmytrenko, Oleh NAS Ukraine IRR 2 Mykoly Shpaka St UA-03113 Kiev Ukraine NTU Igor Sikorsky KPI 37 Prosp Peremohy UA-03056 Kiev Ukraine NALS Ukraine SRIIL 110-v Saksaganskogo St UA-01032 Kiev Ukraine

This paper considers an important formalization problem and building the terminological ontology of problem subject domains based on content-related text data. As an ontological model, we propose to use a linguistic network model of text representation, the so-called network of key terms. In this network, the nodes are keywords and phrases that appear in the text corpus, and the links between them are semantic-syntactic links between these terms in the text. Using systems of aggregation of thematic information flows from freely available information resources distributed in global computer networks, input sets of text data were prepared. In particular, this paper solves the important and urgent problem of computerized processing of legal information. The task of computerized processing of natural language texts lies at the intersection between linguistic theory and mathematical sciences. Therefore, a wider natural language processing based on Part-of-Speech tagging was used for extraction of the key terms. After the extraction, a statistical weighing of the formed words and phrases was performed. The horizontal visibility graph algorithm was used to build undirected links between key terms. This paper also considers a new method that allows determining the direction of links between terms and weighting these links in the undirected network of words and phrases. This method takes into account the parts of speech tagging and also obeys the principle of inclusion of a word or phrase in their corresponding extended phrases with more words. The approbation of the proposed method was carried out on the example of a freely available legal document << Universal Declaration of Human Rights >>. After extracting the key terms from this legal document and determining the direction and weight of links between words or phrases using the proposed methods the directed weighted network of terms was built. The considered in this work method for building the terminological networks ca

关键词： Information space unstructured data ontological model problem subject domain legal information text corpus computerized text processing Part-of-Speech tagging network of terms automatic summarization

来源：评论

学校读者我要写书评

暂无评论

When to Use Which Neural Network? Finding the Right Neural Network Architecture for a Research Problem

When to Use Which Neural Network? Finding the Right Neural N...

引用

2022 workshop on Scientific Document Understanding, SDU 2022

作者： Färber, Michael Weber, Nicolas Web Science Group Kaiserstr. 89 Karlsruhe76133 Germany Heidelberg University Natural Language Processing Group Im Neuenheimer Feld 325 Heidelberg69120 Germany

Considering the increasing rate of scientific papers published in recent years, for researchers throughout all disciplines it has become a challenge to keep track of which latest scientific methods are suitable for which applications. In particular, an unmanageable amount of neural network architectures has been published. In this paper, we propose the task of recommending neural network architectures based on textual problem descriptions. We frame the recommendation as a text classification task and develop appropriate text classification models for this task. In experiments based on three data sets, we find that an SVM classifier outperforms a more complex model based on BERT. Overall, we give evidence that neural network architecture recommendation is a nontrivial but gainful research topic. © 2021 Copyright for this paper by its authors.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Metagrammar of Causatives in Morphologically Rich languages

A Metagrammar of Causatives in Morphologically Rich Language...

引用

2022 International Conference and workshop on Agglutanative language Technologies as a Challenge of natural language processing, ALTNLP 2022

作者： Generalova, Valeria Petitjean, Simon Heinrich Heine University of Dusseldorf Institute for Language and Information Department of Computational Linguistics TreeGraSP project Univesitaetstr. 1 Dusseldorf40225 Germany

Agglutinative languages are known to encode (almost) each grammatical category with a separate morpheme. It results in longer words that might be challenging for some NLP methods. However, the one-to-one correspondence between word segments and their meanings makes agglutinative languages especially suitable for descriptions with help of rule-based precision grammar formalisms. The present paper demonstrates a solution for describing agglutinative languages with help of eXtensible MetaGrammar (XMG). The main innovation of the present paper consists in a careful representation of agglutinative morphology, offering a clear distinction between derivational and inflectional affixes. In the metagrammar, morphology and syntax are represented in a single tree structure, where each morpheme is regarded as a leaf. This paper presents data from three genetically non-related languages: Finnish, Bashkir and Kannada. More languages can be easily added since linguistic generalizations are described separately from individual language properties. The paper focuses on a single type of linguistic constructions, namely, morphological causatives derived from transitive verbs. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Morphology

来源：评论

学校读者我要写书评

暂无评论

SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-based Approaches 19

SIGMORPHON 2022 Task 0 Submission Description: Modelling Mor...

引用

19th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology, SIGMORPHON 2022

作者： Merzhevich, Tatiana Gbadegoye, Nkonye Girrbach, Leander Li, Jingwen Shim, Ryan Soh-Eun Department of Linguistics University of Tübingen Germany Institute for Natural Language Processing University of Stuttgart Germany

ISBN: (纸本)9781955917827

This paper describes our participation in the 2022 SIGMORPHON-UniMorph Shared Task on Typologically Diverse and Acquisition-Inspired Morphological Inflection Generation. We present two approaches: one being a modification of the neural baseline encoder-decoder model, the other being hand-coded morphological analyzers using finite-state tools (FST) and outside linguistic knowledge. While our proposed modification of the baseline encoder-decoder model underperforms the baseline for almost all languages, the FST methods outperform other systems in the respective languages by a large margin. This confirms that purely data-driven approaches have not yet reached the maturity to replace trained linguists for documentation and analysis especially considering low-resource and endangered languages. © 2022 Association for Computational Linguistics.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Deeper Clinical Document Understanding Using Relation Extraction

Deeper Clinical Document Understanding Using Relation Extrac...

引用

2022 workshop on Scientific Document Understanding, SDU 2022

作者： Haq, Hasham Ul Kocaman, Veysel Talby, David John Snow Labs inc. 16192 Coastal Highway LewesDE19958 United States

The surging amount of biomedical literature & digital clinical records presents a growing need for text mining techniques that can not only identify but also semantically relate entities in unstructured data. In this paper we propose a text mining framework comprising of Named Entity Recognition (NER) and Relation Extraction (RE) models, which expands on previous work in three main ways. First, we introduce two new RE model architectures - an accuracy-optimized one based on BioBERT and a speed-optimized one utilizing crafted features over a Fully Connected Neural Network (FCNN). Second, we evaluate both models on public benchmark datasets and obtain new state-of-the-art F1 scores on the 2012 i2b2 Clinical Temporal Relations challenge (F1 of 73.6, +1.2% over the previous SOTA), the 2010 i2b2 Clinical Relations challenge (F1 of 69.1, +1.2%), the 2019 Phenotype-Gene Relations dataset (F1 of 87.9, +8.5%), the 2012 Adverse Drug Events Drug-Reaction dataset (F1 of 90.0, +6.3%), and the 2018 n2c2 Posology Relations dataset (F1 of 96.7, +0.6%). Third, we show two practical applications of this framework - for building a biomedical knowledge graph and for improving the accuracy of mapping entities to clinical codes. The system is built using the Spark NLP library which provides a production-grade, natively scalable, hardware-optimized, trainable & tunable NLP framework. © 2021 Copyright for this paper by its authors.

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Document-level Event Extraction via Heterogeneous graph-based Interaction Model with a Tracker 59

Document-level Event Extraction via Heterogeneous Graph-base...

引用

Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint Conference on natural language processing (IJCNLP) / 6th workshop on Representation Learning for NLP (RepL4NLP)

作者： Xu, Runxin Liu, Tianyu Li, Lei Chang, Baobao Peking Univ Key Lab Computat Linguist MOE Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781954085527

Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences;b) the correlation among events in a document is non-trivial to model. In this paper, we propose Heterogeneous graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges. For the first challenge, GIT constructs a heterogeneous graph interaction network to capture global interactions among different sentences and entity mentions. For the second, GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events. Experiments on a large-scale dataset (Zheng et al., 2019) show GIT outperforms the existing best methods by 2.8 F1. Further analysis reveals GIT is effective in extracting multiple correlated events and event arguments that scatter across the document.

关键词： graphic methods

来源：评论

学校读者我要写书评

暂无评论

N-ary Constituent Tree Parsing with Recursive Semi-Markov Model 59

<i>N</i>-ary Constituent Tree Parsing with Recursive Semi-Ma...

引用

作者： Xin, Xin Li, Jinlong Tan, Zeqi Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China BIT BJ ER Ctr HVLIPCCA Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781954085527

In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. Previous graph-based methods on this setting typically generate hidden nodes with the dummy label inside the n-ary nodes, in order to transform the tree into a binary tree for prediction. The limitation is that the hidden nodes break the sibling relations of the n-ary node's children. Consequently, the dependencies of such sibling constituents might not be accurately modeled and is being ignored. To solve this limitation, we propose a novel graph-based framework, which is called "recursive semi-Markov model". The main idea is to utilize 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate, which then recursively serves as a child candidate of its parent. In this manner, the dependencies of sibling constituents can be described by 1-order transition features, which solves the above limitation. Through experiments, the proposed framework obtains the F1 of 95:92% and 92:50% on the datasets of PTB and CTB 5.1 respectively. Specially, the recursive semi-Markov model shows advantages in modeling nodes with more than two children, whose average F1 can be improved by 0:3-1:1 points in PTB and 2:3-6:8 points in CTB 5.1.

关键词： graphic methods

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共80页 << < 15 16 17 18 19 20 21 22 23 24 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：