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

105 册 图书

馆藏范围

105 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

41 篇 工学
- 29 篇 计算机科学与技术...
- 18 篇 控制科学与工程
- 9 篇 软件工程
16 篇 理学
- 16 篇 系统科学
6 篇 管理学
- 5 篇 管理科学与工程(可...
- 1 篇 图书情报与档案管...
4 篇 文学
- 2 篇 中国语言文学
- 2 篇 外国语言文学
2 篇 法学
- 2 篇 社会学

主题

61 篇 artificial intel...
58 篇 natural language...
56 篇 computational li...
8 篇 machine learning
6 篇 language
5 篇 computer science
5 篇 computer science...
5 篇 synthesis
5 篇 lectures
4 篇 human
4 篇 computers
3 篇 natural language...
3 篇 natural language...
3 篇 computer \u0026 ...
3 篇 natural
3 篇 processing
3 篇 human language t...
2 篇 knowledge based ...
2 篇 information scie...
2 篇 computer applica...

机构

5 篇 university of ot...
4 篇 university of co...
4 篇 university of he...
4 篇 university of po...
4 篇 microsoft resear...
4 篇 university of wa...
3 篇 university of ed...
3 篇 educational test...
2 篇 university of ed...
2 篇 tilburg universi...
2 篇 dataminr inc.
2 篇 pearson knowledg...
2 篇 university of mo...
2 篇 university of il...
2 篇 heidelberg unive...
2 篇 university of wa...
2 篇 nantes universit...
2 篇 montclair state ...
2 篇 university of so...
2 篇 university of ca...

作者

4 篇 diana inkpen
4 篇 shay cohen
3 篇 emily m. bender
3 篇 michael mctear
3 篇 preslav nakov
3 篇 anders søgaard
3 篇 jimmy lin
3 篇 hang li
3 篇 stan szpakowicz
3 篇 vivi nastase
3 篇 michael hagmann
3 篇 stefan riezler
2 篇 martin chodorow
2 篇 diarmuid ó séagd...
2 篇 lotem peled-cohe...
2 篇 chodorow martin
2 篇 rodrigo nogueira
2 篇 shashi narayan
2 篇 stede manfred
2 篇 atefeh farzindar

语言

92 篇 英文
13 篇 中文

检索条件"丛书名=Synthesis Lectures on Human Language Technologies"

共 105 条记录，以下是11-20 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：

Semantic Relations Between Nominals

引用

丛书名： synthesis lectures on human language technologies

2021年

作者： Vivi Nastase Stan Szpakowicz Preslav Nakov Diarmuid Ó Séagdha

ISBN: (数字)9781636390871

ISBN: (纸本)9781636390864;9781636390888

Opportunity and Curiosity find similar rocks on Mars. One can generally understand this statement if one knows that Opportunity and Curiosity are instances of the class of Mars rovers, and recognizes that, as signalled by the word on, ROCKS are located on Mars. Two mental operations contribute to understanding: recognize how entities/concepts mentioned in a text interact and recall already known facts (which often themselves consist of relations between entities/concepts). Concept interactions one identifies in the text can be added to the repository of known facts, and aid the processing of future texts. The amassed knowledge can assist many advanced language-processing tasks, including summarization, question answering and machine translation. Semantic relations are the connections we perceive between things which interact. The book explores two, now intertwined, threads in semantic relations: how they are expressed in texts and what role they play in knowledge repositories. A historical perspective takes us back more than 2000 years to their beginnings, and then to developments much closer to our time: various attempts at producing lists of semantic relations, necessary and sufficient to express the interaction between entities/concepts. A look at relations outside context, then in general texts, and then in texts in specialized domains, has gradually brought new insights, and led to essential adjustments in how the relations are seen. At the same time, datasets which encompass these phenomena have become available. They started small, then grew somewhat, then became truly large. The large resources are inevitably noisy because they are constructed automatically. The available corpora—to

关键词： bisac category: computers \/ artificial intelligence \/ natural language processing \/ computers \/ data science \/ machine learning \/ language arts \u0026 disciplines \/ linguistics \/ general

来源：评论

学校读者我要写书评

暂无评论

Cross-Lingual Word Embeddings

引用

丛书名： synthesis lectures on human language technologies

2019年

作者： Sogaard, Anders Vulic, Ivan Ruder, Sebastian

ISBN: (数字)9781681730646

ISBN: (纸本)9781681730639

The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano nd most other languages remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic.

关键词： information science \u0026 technology\/computer science\/\/information science \u0026 technology\/computer science\/\/language\/linguistics

来源：评论

学校读者我要写书评

暂无评论

Linguistic Fundamentals for Natural language Processing II: 100 Essentials from Semantics and Pragmatics

引用

丛书名： synthesis lectures on human language technologies

2019年

作者： Emily M. Bender Alex Lascarides

ISBN: (数字)9781681730745

ISBN: (纸本)9781681736211;9781681730738

Meaning is a fundamental concept in Natural language Processing (NLP), in the tasks of both Natural language Understanding (NLU) and Natural language Generation (NLG). This is because the aims of these fields are to build systems that understand what people mean when they speak or write, and that can produce linguistic strings that successfully express to people the intended content. In order for NLP to scale beyond partial, task-specific solutions, researchers in these fields must be informed by what is known about how humans use language to express and understand communicative intents. The purpose of this book is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that"s accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics.

关键词： information science \u0026 technology\/computer science\/\/information science \u0026 technology\/computer science\/\/language\/linguistics

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approaches to Text Production 1

引用

丛书名： synthesis lectures on human language technologies

2020年

作者： Shashi Narayan Claire Gardent

ISBN: (数字)9781681737591

ISBN: (纸本)9781681737607;9781681737584

Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives. Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.

关键词： Text processing (Computer science).Neural networks (Computer science).Machine learning

来源：评论

学校读者我要写书评

暂无评论

Bayesian Analysis in Natural language Processing Second edition.

引用

丛书名： synthesis lectures on human language technologies,

2019年

作者： Shay Cohen

ISBN: (数字)9781681735276

ISBN: (纸本)9781681735283

Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze *** then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction *** this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quality Estimation for Machine Translation

引用

丛书名： synthesis lectures on human language technologies

2018年

作者： Specia, Lucia Scarton, Carolina Paetzold, Gustavo Henrique

ISBN: (数字)9781681733746

ISBN: (纸本)9781681733739

Many applications within natural language processing involve performing text-to-text transformations, i.e., given a text in natural language as input, systems are required to produce a version of this text (e.g., a translation), also in natural language, as output. Automatically evaluating the output of such systems is an important component in developing text-to-text applications. Two approaches have been proposed for this problem: (i) to compare the system outputs against one or more reference outputs using string matching-based evaluation metrics and (ii) to build models based on human feedback to predict the quality of system outputs without reference texts. Despite their popularity, reference-based evaluation metrics are faced with the challenge that multiple good (and bad) quality outputs can be produced by text-to-text approaches for the same input. This variation is very hard to capture, even with multiple reference texts. In addition, reference-based metrics cannot be used in production (e.g., online machine translation systems), when systems are expected to produce outputs for any unseen input. In this book, we focus on the second set of metrics, so-called Quality Estimation (QE) metrics, where the goal is to provide an estimate on how good or reliable the texts produced by an application are without access to gold-standard outputs. QE enables different types of evaluation that can target different types of users and applications. Machine learning techniques are used to build QE models with various types of quality labels and explicit features or learnt representations, which can then predict the quality of unseen system outputs. This book describes the topic of QE for text-to-text applications, covering quality labels, features, algorithms, evaluation, uses, and state-of-the-art approaches. It focuses on machine translation as application, since this represents most of the QE work done to date. It also briefly describes QE for several other applications,

关键词： artificial intelligence\/natural language \u0026 machine translation\/computer science

来源：评论

学校读者我要写书评

暂无评论

Argumentation Mining

引用

丛书名： synthesis lectures on human language technologies

2018年

作者： Stede, Manfred Schneider, Jodi

ISBN: (数字)9781681734606

ISBN: (纸本)9781681734590

Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation m

关键词： bisac category: computers \/ natural language processing \/ computers \/ databases \/ data mining \/ computers \/ intelligence (ai) \u0026 semantics

来源：评论

学校读者我要写书评

暂无评论

Metaphor: A Computational Perspective

引用

丛书名： synthesis lectures on human language technologies

2016年

作者： Veale, Tony Shutova, Ekaterina Klebanov, Beata Beigman

ISBN: (数字)9781627058513

ISBN: (纸本)9781627058506

The literary imagination may take flight on the wings of metaphor, but hard-headed scientists are just as likely as doe-eyed poets to reach for a metaphor when the descriptive need arises. Metaphor is a pervasive aspect of every genre of text and every register of speech, and is as useful for describing the inner workings of a "black hole" (itself a metaphor) as it is the affairs of the human heart. The ubiquity of metaphor in natural language thus poses a significant challenge for Natural language Processing (NLP) systems and their builders, who cannot afford to wait until the problems of literal language have been solved before turning their attention to figurative phenomena. This book offers a comprehensive approach to the computational treatment of metaphor and its figurative brethren including simile, analogy, and conceptual blending that does not shy away from their important cognitive and philosophical dimensions. Veale, Shutova, and Beigman Klebanov approach metaphor from multiple computational perspectives, providing coverage of both symbolic and statistical approaches to interpretation and paraphrase generation, while also considering key contributions from philosophy on what constitutes the "meaning" of a metaphor. This book also surveys available metaphor corpora and discusses protocols for metaphor annotation. Any reader with an interest in metaphor, from beginning researchers to seasoned scholars, will find this book to be an invaluable guide to what is a fascinating linguistic phenomenon.

关键词： Computational Linguistics

来源：评论

学校读者我要写书评

暂无评论

Automatic Text Simplification

引用

丛书名： synthesis lectures on human language technologies

2017年

作者： Saggion, Horacio

ISBN: (数字)9781627058698

ISBN: (纸本)9781627058681

Thanks to the availability of texts on the Web in recent years, increased knowledge and information have been made available to broader audiences. However, the way in which a text is written its vocabulary, its syntax an be difficult to read and understand for many people, especially those with poor literacy, cognitive or linguistic impairment, or those with limited knowledge of the language of the text. Texts containing uncommon words or longand complicated sentences can be difficult to read and understand by people as well as difficult to analyze by machines. Automatic text simplification is the process of transforming a text into another text which, ideally conveying the same message, will be easier to read and understand by a broader audience. The process usually involves the replacement of difficult or unknown phrases with simpler equivalents and the transformation of long and syntactically complex sentences into shorter and less complex ones. Automatic text simplification, a research topic which started 20 years ago, now has taken on a central role in natural language processing research not only because of the interesting challenges it posesses but also because of its social implications. This book presents past and current research in text simplification, exploring key issues including automatic readability assessment, lexical simplification, and syntactic simplification. It also provides a detailed account of machine learning techniques currently used in simplification, describes full systems designed for specific languages and target audiences, and offers available resources for research and development together with text simplification evaluation techniques.

关键词： human language technologies

来源：评论

学校读者我要写书评

暂无评论

Neural Network Methods in Natural language Processing

引用

丛书名： synthesis lectures on human language technologies,

2017年

作者： Yoav Goldberg

ISBN: (数字)9781627052955

ISBN: (纸本)9781627052986

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

关键词： Natural language processing (Computer science) Neural networks (Computer science)

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11页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：