nlp怎么发表论文

nlp怎么发表论文

我可能会倾向于人工智能，因为未来的市场人工智能占比还是较大的，这样你写论文的范围很广，比较容易

两篇都好发。opencv主要以实践和应用为主，同时需要研究成果可以指导应用。NLP是算法中最有挑战性的，因为在CV中，视频可以分割为一帧一帧的图像，像素点是有限的，这很适合计算机去解析。

很抱歉，我是小学毕业的老糟头子。视频、图像处理，涉及领域非常广阔，任何一个应用，都可以写出无数篇有价值的论文。比如CT图像的电脑判读，比如润滑油的色度检测，比如违章人脸识别，比如人脸图像的历史年轮，视频特效，图像特效等等。至于自然语言，不知道你想说啥。计算机领域没有自然语言，只有程序语言。程序语言不外乎是C、Delphi，外加VB。如果你更专，那就必须会汇编语言。不管什么语言，必须能控制硬件、数据库、媒体文件、HTML5等等。但无论如何不要碰python，那是庞氏局。搞程序，随便完成一个课题，都可以用代码来实现课题中的程序控制部分，写论文也很容易。其实不管选图像、视频处理，还是程序语言，关键是你得选择一个适合自己的课题，用你的计算机技术来完成这个课题，那就是论文了。

推荐下NLP领域内最重要的8篇论文吧（依据学术范标准评价体系得出的8篇名单）： 一、Deep contextualized word representations 摘要：We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals. 全文链接： Deep contextualized word representations——学术范 二、Glove: Global Vectors for Word Representation 摘要：Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition. 全文链接： Glove: Global Vectors for Word Representation——学术范 三、SQuAD: 100,000+ Questions for Machine Comprehension of Text 摘要：We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at this https URL 全文链接： SQuAD: 100,000+ Questions for Machine Comprehension of Text——学术范 四、GloVe: Global Vectors for Word Representation 摘要：Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition. 全文链接： GloVe: Global Vectors for Word Representation——学术范 五、Sequence to Sequence Learning with Neural Networks 摘要：Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.  全文链接： Sequence to Sequence Learning with Neural Networks——学术范 六、The Stanford CoreNLP Natural Language Processing Toolkit 摘要：We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage. 全文链接： The Stanford CoreNLP Natural Language Processing Toolkit——学术范 七、Distributed Representations of Words and Phrases and their Compositionality 摘要：The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. 全文链接： Distributed Representations of Words and Phrases and their Compositionality——学术范 八、Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank 摘要：Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.  全文链接： Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank——学术范 希望可以对大家有帮助， 学术范 是一个新上线的一站式学术讨论社区，在这里，有海量的计算机外文文献资源与研究领域最新信息、好用的文献阅读及管理工具，更有无数志同道合的同学以及学术科研工作者与你一起，展开热烈且高质量的学术讨论！快来加入我们吧！

nlp论文怎么发表

如果对您来说这篇讠仑文重要的话觉得还是不要直接投网上冒那个险，除非真的完美没得缺陷！要想有保障点不如去找专业点的信用高的机构发表，这样对自己也有利些！发表人飘过。。。。

根据学术堂的了解,论文发表一般流程主要为六个步骤:第一步:投稿.这是论文发表人员选择好投稿期刊之后,将自己的论文稿件通过邮箱、在线投稿窗口、QQ或者微信即时通讯软件这三大方式发送给编辑.第二步:审核即审稿.投稿之后,编辑会按照投稿顺序对论文进行审稿,有的期刊杂志收取审稿费,如果您的论文需要加急发表,请在投稿时标注清楚,可能会产生加急费用.审稿环节是整个论文发表过程中耗时最长的,影响了论文发表周期的长短,关于论文发表时间影响因素可以阅读《是什么影响论文发表时间长短》了解.这里需要注意的是论文审稿可能会反复进行.第三步:审稿结果.主要介绍通过审稿被录用的论文.通过杂志社论文三审的论文,杂志社会下发录用通知书,并注明预安排在某年某期发表,之所以是预安排,是因为还没交纳版面费.关于论文三审可以阅读《什么时候论文需要三审》,了解一些审稿知识.第四部:交费.这里的交费主要是版面费,交纳之后,论文才会正式进入安排刊期出版流程.第五步:安排发表.版面费到位之后,即可安排刊期,并按照日期出版见刊.少部分论文发表可能会延期,原因很多,例如:有人安排加急.第六步:寄送样刊.论文见刊之后,会给作者寄送一本样刊,作为用途上交的材料.到此整个的论文发表流程结束.

发表论文的步骤：

第一步论文查重。

之所以放在第一步，是因为期刊天空一直都建议作者投稿前查重，这样既能提前发现自己论文重复率多少，又不会给杂志社编辑造成不良印象，更减少了投稿后再查重导致退修，进而论文发表时间周期增加。

第二步：筛选期刊。

针对自己的专业方向，论文内容领域，到相应分类的期刊当中挑选，有作者因为发表论文不符合期刊发表方向而退稿的。

第三步：选定期刊。

需要根据自己评职称、毕业论文发表要求，期刊天空编辑指出，这些内容一般从职称文件当中可以了解到，例如：期刊级别，选定后要了解期刊发表论文要求。

第四步：论文发表。

选定期刊之后，可以通过邮箱、在线投递、微信QQ等发送文件，之所以有这么多方式，是因为各投稿方式相应的处理效率呈提高的趋势。

第五步：等待审稿。

论文审稿是整个论文发表过程当中时间周期最长的，没有退修的稿件属于正常时间周期，如果存在论文审稿有退修，那么发表周期就会相应的增加，发表论文期刊的级别越高，发表周期就越长。

第六步：录用通知函。

对于顺利被期刊录用的论文来说，杂志社会发送录用通知函，缴纳版面费用之后，即可安排发表刊期。

第七步：发表见刊。

在到了论文发表安排刊期时，论文就算是正式见刊发表，作者需等待杂志社寄送样刊就可以当做评职称材料上交。

第一步，确定自己发表论文的需求。 第二步，选择合适的期刊，核实期刊论文真伪，目前国内所有学术期刊，均可通过国家新闻出版总署期刊查询中心进行查询核实，如果查询不到CN刊号，那就说明期刊是假刊 第三步，了解期刊征稿需求，阅读其刊登发表过的论文，看自己的论文是否适合在这些期刊上发表，从中挑出2-3个期刊作为备选，进一步了解这些刊物的审稿周期、投稿费用、投稿要求等 第四步，等待样刊和论文上网，可以通过国内的四大权威数据库如知网、万方、维普、龙源查询核实。 如果觉得自己寻找期刊麻烦，还可以通过代理/杂志社服务，以下为流程： 1、论文写好并发给代理或者杂志社。（文章质量要有保证） 2、根据论文字数内容和作者的发表意向确定所发表的期刊及费用。 3、支付定金。 4、杂志社进行审稿，审稿通过后邮寄给您稿件录用通知单。 5、在你收到用稿通知后，三天内请付清余款，以确保你的论文能及时发表。 6、杂志出刊后杂志社会给每个作者邮寄两本样刊，以供您使用。

nlp发论文

推荐下NLP领域内最重要的8篇论文吧（依据学术范标准评价体系得出的8篇名单）： 一、Deep contextualized word representations 摘要：We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals. 全文链接： Deep contextualized word representations——学术范 二、Glove: Global Vectors for Word Representation 摘要：Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition. 全文链接： Glove: Global Vectors for Word Representation——学术范 三、SQuAD: 100,000+ Questions for Machine Comprehension of Text 摘要：We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at this https URL 全文链接： SQuAD: 100,000+ Questions for Machine Comprehension of Text——学术范 四、GloVe: Global Vectors for Word Representation 摘要：Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition. 全文链接： GloVe: Global Vectors for Word Representation——学术范 五、Sequence to Sequence Learning with Neural Networks 摘要：Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.  全文链接： Sequence to Sequence Learning with Neural Networks——学术范 六、The Stanford CoreNLP Natural Language Processing Toolkit 摘要：We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage. 全文链接： The Stanford CoreNLP Natural Language Processing Toolkit——学术范 七、Distributed Representations of Words and Phrases and their Compositionality 摘要：The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. 全文链接： Distributed Representations of Words and Phrases and their Compositionality——学术范 八、Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank 摘要：Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.  全文链接： Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank——学术范 希望可以对大家有帮助， 学术范 是一个新上线的一站式学术讨论社区，在这里，有海量的计算机外文文献资源与研究领域最新信息、好用的文献阅读及管理工具，更有无数志同道合的同学以及学术科研工作者与你一起，展开热烈且高质量的学术讨论！快来加入我们吧！

两篇都好发。opencv主要以实践和应用为主，同时需要研究成果可以指导应用。NLP是算法中最有挑战性的，因为在CV中，视频可以分割为一帧一帧的图像，像素点是有限的，这很适合计算机去解析。

发表nlp论文

你的论文准备往什么方向写，选题老师审核通过了没，有没有列个大纲让老师看一下写作方向？ 老师有没有和你说论文往哪个方向写比较好？写论文之前，一定要写个大纲，这样老师，好确定了框架，避免以后论文修改过程中出现大改的情况！！学校的格式要求、写作规范要注意，否则很可能发回来重新改，你要还有什么不明白或不懂可以问我，希望你能够顺利毕业，迈向新的人生。 一、毕业论文选题的重要意义 第一、选题是撰写毕业论文的第一步，它实际上是确定“写什么”的问题，也就是确定论文论述的方向。如果“写什么”都不明确，“怎么写”根本无从谈起，因此毕业论文的顺利完成离不开合适的论文选题。 第二、毕业论文的写作一方面是对这几年所学知识的一次全面检验，同时也是对同学们思考问题的广度和深度的全面考察。因此，毕业论文的选题非常重要，既要考虑论文涉及的层面，又要考虑它的社会价值。 二、毕业论文选题的原则 （一）专业性原则 毕业论文选题必须紧密结合自己所学的专业，从那些学过的课程内容中选择值得研究或探讨的学术问题，不能超出这个范围，否则达不到运用所学理论知识来解决实际问题的教学目的。我们学的是工商管理专业，选题当然不能脱离这个大范畴，而且在限定的小范围内，也不能脱离工商管理、企业经营去谈公共事业管理或金融问题。学术研究是无止境的，任何现成的学说，都有需要完善改进的地方，这就是选题的突破口，由此入手，是不难发现问题、提出问题的。 （二）价值性原则 论文要有科学价值。那些改头换面的文章抄袭、东拼西凑的材料汇集以及脱离实际的高谈阔论，当然谈不上有什么价值。既然是论文，选题就要具有一定的学术意义，也就是要具有先进性、实践性和一定的理论意义。对于工商管理专业的学生而言，我们可以选择企业管理中有理论意义和实践指导意义的论题，或是对提高我国企业的管理水平有普遍意义的议题，还可以是新管理方法的使用。毕业论文的价值关键取决于是否有自己的恶创见。也就是说，不是简单地整理和归纳书本上或前人的见解，而是在一定程度上用新的事实或新的理论来丰富专业学科的某些氦姬份肯莓厩逢询抚墨内容，或者运用所学专业知识解决现实中需要解决的问题。 （三）可能性原则 选题要充分考虑到论题的宽度和广度以及你所能占有的论文资料。既要有“知难而进”的勇气和信心，又要做到“量力而行”。”选题太大、太难，自己短时间内无力完成，不行；选题太小、太易，又不能充分发挥自己的才能，也不行。一切应从实际出发，主要应考虑选题是否切合自己的特长和兴趣，是否可以收集到足够的材料和信息，是否和自己从事的工作相接近。一定要考虑主客观条件和时限，选择那些适合自己情况，可以预期成功的课题。一般来说，题目的大小要由作者实际情况而定，很难作硬性规定要求。有的同学如确有水平和能力，写篇大文章，在理论上有所突破和创新，当然是很好的。但从成人高校学生的总体来看，选题还是小点为宜。小题目论述一两个观点，口子虽小，却能小题大做，能从多层次多角度进行分析论证.这样，自己的理论水平可以发挥，文章本身也会写得丰满而充实。选择一个比较恰当的小论题，特别是与自己的工作或者生活密切相关的问题，不仅容易搜集资料，同时对问题也看得准，论述也会更透彻，结论也就可能下得更准确。 三、毕业论文选题的方法 第一、 浏览捕捉法。这种方法是通过对占有的论文资料快速、大量地阅读，在比较中来确定题目的方法。浏览，一般是在资料占有达到一定数量时集中一段时间进行，这样便于对资料作集中的比较和鉴别。浏览的目的是在咀嚼消化已有资料的过程中，提出问题，寻找自己的论题。这就需要我们对收集到的材料进行全面阅读研究，主要的、次要的、不同角度的、不同观点的都应了解，不能“先入为主”，不能以自己头脑中原有的观点决定取舍。而应冷静地、客观地对所有资料作认真的分析思考，从内容丰富的资料中吸取营养，反复思考琢磨之后，就会有所发现，然后再根据自己的实际确定自己的论题。 第二、 追溯验证法。这种方法要求同学们先有一种拟想，然后再通过阅读资料加以验证来确定选题的方法。同学们应该先有自己的主观论点，即根据自己平时的积累，初步确定准备研究的方向、题目或选题范围。这种选题方法应注意：看自己的“拟想”是否与别人重复，是否对别人的观点有补充作用；如果自己的“拟想”虽然别人还没有谈到，但自己尚缺乏足够的理由来加以论证，那就应该中止，再作重新构思。要善于捕捉一闪之念，抓住不放，深入研究。在阅读文献资料或调查研究中，有时会突然产生一些思想火花，尽管这种想法很简单、很朦胧，也未成型，但千万不可轻易放弃。 第三、 知识迁移法。通过四年的学习，对某一方面的理论知识（经济或者法律或者其它）有一个系统的新的理解和掌握。这是对旧知识的一种延伸和拓展，是一种有效的更新。在此基础之上，同学们在认识问题和解决问题的时候就会用所学到的新知识来感应世界，从而形成一些新的观点。理论知识和现实的有机结合往往会激发同学们思维的创造力和开拓性，为毕业论文的选题提供了一个良好的实践基础和理论基础。 第四、 关注热点法。热点问题就是在现代社会中出现的能够引起公众广泛注意的问题。这些问题或关系国计民生，或涉及时代潮流，而且总能吸引人们注意，引发人们思考和争论。同学们在平时的学习和工作中大部分也都会关注国际形势、时事新闻、经济变革。选择社会热点问题作为论文论题是一件十分有意义的事情，不仅可以引起指导老师的关注，激发阅读者的兴趣和思考，而且对于现实问题的认识和解决也具有重要的意义。将社会热点问题作为论文的论题对于同学们搜集材料、整理材料、完成论文也提供了许多便利。 第五，调研选题法。调研选题法类同于关注社会热点这样的选题方法，但所涉及的有一部分是社会热点问题，也有一部分并不是社会热点问题。社会调研可以帮助我们更多地了解调研所涉问题的历史、现状以及发展趋势，对问题的现实认识将更为清晰，并可就现实问题提出一些有针对性的意见和建议。同学们将社会调研课题作为毕业论文的论题，有着十分重要的现实意义，不仅可为地方经济建设和社会发展提供有价值的资料和数据，而且可为解决一些社会现实问题提供一个很好的路径。

两篇都好发。opencv主要以实践和应用为主，同时需要研究成果可以指导应用。NLP是算法中最有挑战性的，因为在CV中，视频可以分割为一帧一帧的图像，像素点是有限的，这很适合计算机去解析。

nlp论文发表

让AI技术与基础医学理论结合，成为AI用于临床 探索 的新思路。目前这一新思路已被证实确有更大潜力——

最近，由广州市妇女儿童医疗中心教授夏慧敏和加州大学圣地亚哥分校教授张康领衔、人工智能公司依图 科技 等共同参与的科研团队设计出一套基于AI的疾病诊断系统，就将医学知识图谱加入其中，使AI可以像人类医生一样根据读取的电子病历来“诊病”。

结果也颇为乐观：用纳入系统的55种常见儿科疾病和部分危急重症作测试，AI的诊断水平可达到儿科主治医生的专业水准。

目前，这一研究成果《使用人工智能评估和准确诊断儿科疾病》已于2月中旬在线发表于《自然—医学》杂志。

将深度学习技术与专业医学知识图谱进行结合，是该人工智能辅诊平台的最大特色。依图医疗总裁倪浩在接受笔者采访时说，未来对临床数据进行学习、为医生提供更多的辅助诊断能力（病种），采用深度学习+知识图谱的方式“很可行”。

为了使AI辅诊平台拥有专业的儿科医学知识，科研团队让它学习了56.7万名儿童136万份电子文本病历中的诊断逻辑。这些来自广州市妇女儿童医疗中心2016年1月至2017年7月间的电子病历，覆盖了初始诊断包括儿科55种病例学中常见疾病的1.016亿个数据点。

除了将医疗知识进行整合，科研团队还利用依图 科技 的自然语言处理（NLP）技术构建了一个自然语言处理模型，以对这些电子病历进行注释——通过将病历变得标准化，该模型在未经过“培训”的情况下可以粗略地将临床信息进行分类。

“粗略分类是指，将整个电子病历当作输入，将专家诊断结果作为输出，以达到粗略的分类。但这样并没有真正理解疾病本身，也很难解释为何做出了这个诊断。”倪浩告诉笔者， NLP模型虽然突破了病历文本语言和计算机语言之间的障碍，但知识图谱才是让AI诊断平台获取专家能力的关键 。

这也是他们接下来的一项重要工作：由30余位高级儿科医师和10余位信息学研究人员组成的专家团队，手动给电子病历上的6183张图表进行注释、持续检验和迭代，以保证诊断的准确性。

通过资深医疗专家注释的图表对AI诊断平台进行“培训优化验证”后，研究人员发现，经过深度学习的NLP模型可以对电子病历进行很好的注释，在体检和主诉项目的注释上分别达到最高灵敏度和精确度。也就是说， 深度学习的NLP模型能够准确地读取电子病历中记录的信息，并可以准确作出符合临床标准的批注。而这也是整个研究中最为关键的部分。

“通过引入知识图谱将每种疾病的电子病历深入解构，使得NLP模型具备了理解电子病历的能力。例如手足口病与哪些特征密切相关，川崎病最相关的特征是什么，让模型在给出准确诊断的基础上，能够具备更好的医学可解释性。”倪浩解释说，“有了知识图谱，再用深度学习技术来解构电子病历，就能够真正理解临床数据。基于此，机器学习分类等算法就有用武之地，否则把电子病历当成‘黑盒子’，是无法构建高精度可解释的模型的。”

综合利用深度学习技术与医学知识图谱对电子病历数据进行解构，研究人员据此构建了高质量的智能病种库，这使得后续可以较容易地利用智能病种库建立各种诊断模型。

构建一个多层级的诊断模型，是研究人员把AI诊断平台打造成为儿科医生的第二步。倪浩介绍说，这一基于逻辑回归分类器创建的诊断模型，首先会按呼吸系统疾病、胃肠道疾病、全身性疾病等几大系统分，然后在每一类下面做细分—— 这是让AI模拟人类医生的诊疗路径，对目标患儿的数据进行逐级判定 。

结果显示，基于NLP模型准确读取的数据，AI诊断模型能够对儿科疾病作出精确诊断： 平均准确率达90%，对神经精神失调疾病的诊断准确率更是高达98%。

在对相应儿科疾病的划分和诊断上，该诊断模型同样表现不俗。系统对上呼吸道疾病和下呼吸道疾病的诊断准确率分别为89%和87%。同时，该系统对普通系统性疾病以及高危病症也有很高的诊断准确率，例如传染性单核细胞增多症准确率为90%，水痘为93%，玫瑰疹93%，流感94%，手足口病为97%和细菌性脑膜炎为93%。

这揭示出，该诊断系统可以根据NLP系统注释的临床数据信息对常见儿科疾病作出较高准确度的判断。

研究人员随后运用11926个临床病例比较了AI诊断系统和5个临床治疗组诊断儿科疾病的水平，其中参与研究的治疗组从事临床工作时间和资历逐渐增加。结果显示， AI诊断系统反映模型综合性能的F1评分均值高于2个年轻医生组成的治疗组，但稍逊于3个高年资医生组成的治疗组。

论文认为，这说明该AI诊断系统可以协助年轻治疗团队进行疾病诊断，提升团队诊疗水平。

今年1月1日，该系统在广州市妇女儿童医疗中心投入临床应用。 仅1月1日至1月21日短短20天，该院医生实际调用它开展辅助诊断30276次，诊断与临床符合率达到87.4%。广州市妇儿中心医务部主任孙新在体验该系统后表示，这套系统在对疾病进行分组分类方面“比较科学”。

上述论文发表后，《纽约时报》点评这项研究称，“前后访问了儿科医院18个月中数十万名中国就医儿童的数据，能有这么庞大的数据量用于研究，也是中国在全球人工智能和竞赛中的优势。”

“数据确实是我们此次研究成果的核心关键之一。”倪浩说，“不过，高质量标准数据来源于强大的联合团队，我们专门开发了数据标准系统，进行了大量的数据标注。”

论文通讯作者之一、广州市妇女儿童医疗中心教授夏慧敏表示，这篇文章的启示意义在于“通过系统学习文本病历，AI或将诊断更多疾病”。不过他提醒道， 当下还须清醒认识到，仍有很多基础性工作要做扎实，比如高质量数据的集成便是一个长期的过程。

笔者了解到，该医院在近3年里注重将数据标准化、结构化处理，实现了50多个诊断数据子系统的相互交流和互联互通，为该系统应用打下了基础。

“此外，A I学习了海量数据后，其诊断结果的准确性仍然需要更大范围的数据对其进行验证和比对。 ”夏慧敏说。

AI技术落地的4元素之中，场景也非常重要。论文的另一位通讯作者张康认为，该研究以儿科疾病为对象意义重大。

“对儿科疾病的诊断是医疗中的一大痛点。一些儿科疾病威胁程度较大需要尽快得到治疗，而儿童恰恰不善于表达病情，因此快速、准确地对儿科疾病进行诊断非常必要。”张康表示，当前儿科医生供不应求，论文中构建的AI诊断系统对于严重不足的医疗资源会有很大的辅助作用。

相关论文信息：DOI:10.1038/s41591-018-0335-9

推荐下NLP领域内最重要的8篇论文吧（依据学术范标准评价体系得出的8篇名单）： 一、Deep contextualized word representations 摘要：We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals. 全文链接： Deep contextualized word representations——学术范 二、Glove: Global Vectors for Word Representation 摘要：Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition. 全文链接： Glove: Global Vectors for Word Representation——学术范 三、SQuAD: 100,000+ Questions for Machine Comprehension of Text 摘要：We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at this https URL 全文链接： SQuAD: 100,000+ Questions for Machine Comprehension of Text——学术范 四、GloVe: Global Vectors for Word Representation 摘要：Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition. 全文链接： GloVe: Global Vectors for Word Representation——学术范 五、Sequence to Sequence Learning with Neural Networks 摘要：Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.  全文链接： Sequence to Sequence Learning with Neural Networks——学术范 六、The Stanford CoreNLP Natural Language Processing Toolkit 摘要：We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage. 全文链接： The Stanford CoreNLP Natural Language Processing Toolkit——学术范 七、Distributed Representations of Words and Phrases and their Compositionality 摘要：The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. 全文链接： Distributed Representations of Words and Phrases and their Compositionality——学术范 八、Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank 摘要：Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.  全文链接： Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank——学术范 希望可以对大家有帮助， 学术范 是一个新上线的一站式学术讨论社区，在这里，有海量的计算机外文文献资源与研究领域最新信息、好用的文献阅读及管理工具，更有无数志同道合的同学以及学术科研工作者与你一起，展开热烈且高质量的学术讨论！快来加入我们吧！