nlp研究生论文

nlp研究生论文

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 (., syntax and semantics), and (2) how these uses vary across linguistic contexts (., 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 , a significant improvement over a simple baseline (20%). However, human performance () 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 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 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 , 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 . The accuracy of predicting fine-grained sentiment labels for all phrases reaches , an improvement of 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论文期刊

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 (., syntax and semantics), and (2) how these uses vary across linguistic contexts (., 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 , a significant improvement over a simple baseline (20%). However, human performance () 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 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 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 , 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 . The accuracy of predicting fine-grained sentiment labels for all phrases reaches , an improvement of 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《导航与控制》中文核心期刊审稿快。根据查询相关资料信息，《导航与控制》是国家新闻出版署批准公开出版发行，由中国航天科技集团公司主管，所以nlp《导航与控制》中文核心期刊审稿快。

《现代语言学》可以投你说的。而且周期很短。 是一本关注语言学领域最新进展的国际中文期刊，主要刊登国内外语言学领域最新动态，研究进展及前沿报道、学者讨论和专业评论等多方面的论文。

刚刚! 西北农林科技大学又双叒叕发《Science》，台籍学者领衔!高分子科学前沿2022-09-23 08:02 ·浙江17氮元素是生物体构成的主要元素之一。氮是植物生长的主要限制因素，是农业生产力，动物和人类营养以及可持续生态系统的基础。光合植物通过将无机氮同化为维持植物和依赖它们的食物网的生物分子（DNA，RNA，蛋白质，叶绿素和维生素）来驱动陆地氮循环。为了与土壤中更喜欢有机氮或铵的微生物竞争，大多数植物已经进化出对硝酸盐可用性波动做出反应的调节途径。感知可用硝酸盐的植物将在几分钟内协调转录组，新陈代谢，激素，全系统芽和根的生长以及繁殖反应。长久以来科学家们只是能够在基因水平确定硝态氮是一种信号分子，但并不清楚植物感受它的机制。其次，氮肥是能源密集型生产并造成污染;此外，它在农业中的过度使用以提高作物产量，导致全球环境灾难性的富营养化。全球和区域研究表明，地球上的氮供应量正在下降。提高植物氮利用效率有助于可持续农业和生态系统保护。这些年，研究者们关于硝态氮的研究，一直热情不减，很多研究者们认为蛋白（也称为CHL1或）不仅仅是质膜细胞外硝酸盐转运体传感器（转感受器），同时也是硝酸盐的感受器，具有感受硝态氮的功能。但刘坤祥教授根据多年研究认为，CHL1/蛋白不是一个主要的硝酸盐感受器，他带领团队夜以继日地研究,尝试解释清楚这一机制。打开网易新闻 查看精彩图片 2022年9月23日，《Science》期刊刊登了西北农林大学、台湾籍教授刘坤祥团队和其合作团队在植物硝酸盐信号“开关”——NLP7蛋白的最新研究成果，相关论文题为 “NIN-like protein 7 transcription factor is a plant nitrate sensor” 在这项研究里，研究者们发现所有七个拟南芥 NIN 样蛋白 (NLP) 转录因子的突变消除了植物的初级硝酸盐反应和发展计划。 NIN-NLP7 嵌合体和硝酸盐结合的分析显示NLP7 通过其氨基末端对硝酸盐的感知被解除抑制。基因编码的荧光拆分生物传感器，mCitrine-NLP7，实现了植物中单细胞硝酸盐动力学的可视化。硝酸盐传感器NLP7 的结构域类似于细菌硝酸盐传感器 NreA。 保守残基的替换配体结合口袋损害了硝酸盐触发的 NLP7 控制转录、转运、新陈代谢、发育和生物量。这项结果表明转录因子NIN样蛋白7（NLP7）为主要的硝酸盐传感器。其中，刘坤祥教授和Jen Sheen为共同通讯作者，西北农林大学生命学院博士生刘孟红、林子炜，师资博士后陈斌卿以及Zi-Fu Wang为共同一作为共同第一作者，西北农林科技大学旱区作物逆境生物学国家重点实验室、生命学院、未来农业研究院为第一署名单位。组合 NLP 控制初级硝酸盐反应打开网易新闻 查看精彩图片 图 1.组合 NLP 转录因子是原发性硝酸盐反应和发育计划的核心。由于所有九个NLP基因都在拟南芥芽中表达。对硝酸盐介导的芽生长中单个nlp1-9单个突变体的分析显示，仅在nlp2和nlp7中存在统计学上显着的缺陷。为了规避NLP冗余并更好地定义NLP1-9的重叠或独特功能，我们进行了全基因组靶基因调查。每个NLP在土壤生长植物的转染叶细胞中瞬时表达小时，用于RNA测序（RNA-seq）分析。推定的NLP靶基因的分层聚类分析（日志2≥ 1 或 ≤ −1;P ≤）揭示了所有NLP激活先前通过微阵列、RNA-seq、片上染色质免疫沉淀（ChIP-chip）、ChIP-seq和启动子分析鉴定的通用原代硝酸盐响应性标记基因的能力。NLP2、NLP4、NLP7、NLP8 或 NLP9特异性激活了一些在调节生长素和细胞分裂素激素功能、细胞周期、代谢、肽信号传导以及芽和根分生组织活性方面具有已知功能的靶基因。NLP2和NLP7调节具有多种功能的更广泛的非冗余靶基因，这可能表现为在种子萌发后在nlp2或nlp7中观察到的生长缺陷。NLP6/7主要作为转录激活剂，而NLP2，4，5，8，9可以激活或抑制靶基因。NLP1，3，6比其他NLP调节的靶基因更少。例如，生长素生物合成基因TAR2仅被NLP2，4，5，7，8，9激活。这些结果与NLP变体在控制硝酸盐响应网络和导致土壤中生长的nlp2，4，5，6，7，8，9隔突变植物中发育迟缓的芽和根系发育的NLP变体一致。基因编码的荧光生物传感器可视化植物中的硝酸盐打开网易新闻 查看精彩图片 图 4.遗传编码的生物传感器检测转基因芽和根部的细胞内硝酸盐。配体-传感器相互作用可能触发传感器蛋白的构象变化。我们产生了一个遗传编码的荧光生物传感器mCitrine-NLP7，类似于基于单个荧光蛋白的葡萄糖生物传感器Green Glifon。我们假设硝酸盐结合的分裂米西特林-NLP7硝酸盐生物传感器（sCiNiS）将重建麦西特罗以发出荧光信号。预测的核定位信号（630断续器638）的NLP7突变为AAAAA，以避免与内源性NLP7的竞争，内源性NLP7在硝酸盐诱导后保留在细胞核中以进行转录活化.在转基因植物子叶的叶肉细胞和根尖的腔内膜细胞中，通过sCiNiS对细胞质硝酸盐进行定量共聚焦成像。在硝酸盐（10mM）后5分钟内检测到重组的mCitine荧光信号，但不是KCl，在正常发育的完整sCiNiS转基因幼苗中以单细胞分辨率诱导叶肉细胞和原代根尖细胞。土壤硝酸盐浓度可以从微摩尔到毫摩尔范围变化。我们使用不含硝酸盐的转基因幼苗测试了不同的硝酸盐浓度，并表明sCiNiS生物传感器在完整植物的单个叶肉细胞中检测到100μM至10mM的硝酸盐浓度范围，与敏感和特异性的硝酸盐结合K一致。植物中硝酸盐传感器的进化保护打开网易新闻 查看精彩图片 图 传感器域类似于NreA，具有用于硝酸盐感知和信号传导的保守残留物。为了在功能上定义NLP7中硝酸盐结合的必需残基，我们对硝酸盐-NreA晶型中定义的八种推定的硝酸盐结合残基进行了丙氨酸扫描诱变，并检查了无硝酸盐叶细胞中突变NLP7的硝酸盐反应。NLP7突变的四个残基，Trp395→阿拉（W395A），H404A，L406A或Y436A，在低硝酸盐（）下显着降低硝酸盐诱导的4xNRE-min-LUC活性2小时。因为H404，L406和Y436在NLP2，4，5，6，8，9中是保守的，在硝酸盐结合域中具有相似的结构。我们接下来生成并分析了NLP7的双重（HL / AA）和三重（HLY / AAA）突变体，其消除了硝酸盐诱导的4xNRE-min-LUC活性。HLY硝酸盐结合残留物在作物植物结构相似的硝酸盐传感器域内的NLP7同源物中也是保守的，包括油菜籽BnaNLP7，大豆转基因NLP6，玉米ZmNLP6，小麦TaNLP7和水稻OsNLP3。我们建议NLP7及其同源物可以作为硝酸盐传感器，保存在从炭藻植物到被子植物的光合植物中，包括真双子叶植物和单子叶植物，但不是叶绿素。总结：作者揭示了光合植物用来感知无机氮的调节机制，然后激活植物信号网络和生长反应。我们的见解可能会建议提高作物氮利用效率，减少肥料和能源投入以及减轻温室气体排放引起的气候变化的途径，以支持更可持续的农业。来源：高分子科学前沿声明：仅代表作者个人观点，作者水平有限，如有不科学之处，请在下方留言指正！特别声明：本文为网易自媒体平台“网易号”作者上传并发布，仅代表该作者观点。网易仅提供信息发布平台。

zz研究生论文与研究生科研

研究生论文和硕士论文，约定俗成的说，两者一样不过，严格的说，研究生包括博士和硕士，在我国，大家都把硕士等同于研究生所以！特别严格特别严格(其实完全没必要)，硕士论文就是硕士的毕业论文，研究生论文可能也指博士毕业论文

如果要发表的话请参考这个

研究生分硕士研究生和博士研究生 这个论文我不太清楚 一般就说硕士论文博士论文

研究生高中生的研究论文

不同学校的处罚方式不同，现在，学校对于学术不端行为正在进行严厉打击，还是认认真真把自己的论文写好比较好噢！不过今年因为yq原因，可以会容易通过点吧！

选择论文研究课题、研究论文课题、制定论文大纲、写作毕业论文初稿、修改、编辑、校对论文

一、选择论文研究课题

硕士毕业论文要选择一个自己专业的研究课题，论文写作步骤的最基础事件，在选择论文研究可以时可以参考平时自己的研究重点或者感兴趣的方向，还要和自己的导师商议哪个课题适合研究。

二、研究论文课题

对于论文课题的研究需要参考大量的文献资料，因此阅读大量课题相关论文内容，找到相关资料和论据以支持自己论文课题的论点，在搜索到相关论据和材料时一定要详细记录，以便得到更好的运用。

三、制定论文大纲

论文提纲对于整理论文写作思路有重要意义，我们需要在写毕业论文前先列出论文大纲，这对于论文而言是个简易且结构全面的论文草稿，可以逐步整理混乱的论文想法。

四、写作毕业论文初稿

按照论文提纲逐步丰满论文全文，在写作完成后即为论文初稿，论文初稿并非最终成稿，但是基本算是完了研究生毕业论文。

五、修改、编辑、校对论文

论文初稿完成后对论文进行细致的检查和反复阅读，理顺逻辑和语言表达。

以中学的教育角度出发，有如下题目：1、中学XX探究学习方法运用的研究 2、将研究学科性学习引入中学XX教学初探

本科生谁查，要查，就得同时把那些他妈不指导人的老师也处罚。我今年就是，老师屁都没指导，工况都不给，毕业设计我一个人捣鼓出来的不过吧，我觉得做毕业设计的时候，思路最好不要告诉别人。我感觉自己这次的想法就被抄了。那人知道了我的想法后，找一个做这方面的外校同学，拿他做完的内容，最后答辩老师说做的很具体。我，妈的……老师啥都不指导，也不想抄外校同学的，结果最后只是勉强交上，跟设想差很多。哎

专业研究生论文研究生论文

写作思路：根据导师的要求定好题目，再根据题目拟大纲，但是要先给导师看一下是否可行。

论文选题推荐：

1、在素质教育中如何加强未成年人的思想道德教育。

2、思想政治课如何理论联系实际。

3、分层递进教学在政治课教学中的运用。

4、初中思想政治教法浅谈。

5、如何加强中小学心理健康教育。

6、浅谈初中思想品德教育对青少年成长的影响。

7、浅谈思想政治活动课教学中学生创新能力的培养。

8、对山区未成年人思想道德建设的思考。

9、中学思想政治教学改革的探索。

10、谈谈小学思想政治教学中的创新。

11、浅谈农村如何加强思想道德教育。

12、校园文化建设与学校政治思想工作。

13、强化德育功能，推进素质教育。

14、浅谈小学生良好思想道德的培养。

15、德育工作在班级管理中的作用。

16、论对中学生进行思想品德教育的途径与方法。

17、改进政治思想教育，塑造学生优良人格。

18、初中思想政治课堂教学探究性学习研究。

19、中学生思想及思想政治教育的调查与研究。

20、小学品德与生活课应注意培养学生的道德情感。

论文主要是学位论文和期刊论文。学位论文主要是毕业答辩时的论文，也就是我们所说的大论文。期刊论文主要是让你达到毕业要求用的，是发表在期刊上的，也就是我们所说的小论文。期刊论文有主要有两种，一种是综述类的，内容主要是总结之前相关领域所做的研究。还有一种是实验型的，内容主要是通过实验得出的一些结果或者是结论。

按不同的标准对论文有不同分类。对论文进行分类，是为了便于进行科学研究，这是因为不同领域不同学科的研究论文撰写的要求是不相同的，其功用也存在着差异。一、按照研究领域来划分从研究领域来划分，可分为社会科学论文和自然科学论文。社会科学论文，主要是描述社会复杂现象，阐述社会发展变化规律，分析和解决社会问题而积极开展的科学研究而撰写的论文。自然科学论文，主要是描述自然现象，阐述自然发展变化，分析和解决自然科学发展存在问题而进行积极研究，而发表自己的观点和主张的文章。二、按照研究方式来划分从研究方式来划分，可以分为描述性论文、综述型论文和应用型论文。描述型论文，主要是指通过概念、判断和推理等逻辑形式，结合议论、说明等表达方式，来分析事物、阐明事理，以达到作者阐述自己的新观点和新见解之目的的一种论文。可分为两类：一是立论型论文和驳论型论文。立论型论文，主要是通过摆事实，讲道理等方式，正面阐述作者的观点和见解的文章；驳论型论文，主要是在摆事实讲道理的论证过程中，辨析和驳斥他人的观点，树立自己观点和见解的文章。从其定义的描述中可以看出，描述型论文具有很强的理论性、严密的逻辑性和以议论为主的表达方式。综述型论文，主要是一种就某领域中的某一问题为研究对象，以纵向、横向和纵横向等描述向度，通过归纳、总结等方式对前人已取得的研究成果进行介绍或评论，并发表作者自己的见解的论文。“它的目的是使读者看到某一眼镜成果的性质、规模、进程、状态和趋势。其特点是以叙述为主，夹叙夹议，有时议论多于叙述”（刘巨钦 等.经济管理类学生专业论文导写[M].长沙：中南大学出版社，2000年，第3版）。应用型论文，主要是指以某一社会现象或问题作为研究对象，运用一些理论对通过实证调研已经收集的数据资料进行判断和分析，作者并提出应对的政策或措施的一种文章。它具有实效性、实用性和针对性等特点。三、按照论文形式来划分从论文形式来看，可划分为学期或学年论文、学科论文、学位论文、调查报告、实习报告和研究论文等。学期论文、学位论文和实习报告是高校学生学习过程中必须要完成的论文。调查报告和研究论文，高校学生也可以进行撰写或不撰写，但科研人员必须熟练掌握的。学期或学年论文，主要是指大学本科的三年级（包括三年级）以上的学生初次运用已学的基础知识和研究方法，在老师的指导下独立撰写的论文。这种论文是相当于学生的独立完成的作业，往往是为毕业论文的撰写打下基础，其学术性要求不高。其目的是要求学生取得撰写论文的一些经验，并理解和掌握论文的基本写作的步骤和方法而已。学科论文，主要是指学完一门课程（学科）之后，要求学生运用该学科理论和方法来探讨和研究该学科所涉及到领域的问题和现象，独立撰写的文章。撰写学科论文的学生，主要是硕士、博士研究生。这类论文要求有一定的学术性，即它要具有较强的理论分析和阐述问题的深度。这其实也是为撰写硕博士学位论文作的准备工作和强化训练工作。学位论文，主要是指学生为了能拿到学位而撰写的论文，也称毕业论文。学位论文，主要包括学士学位论文、硕士学位论文和博士学位论文。不同学历教育层次的学生要想拿到相应的学位，就必须撰写学位论文。学位论文是检查学生的所掌握某领域的基础知识，运用该研究领域的理论和方法来研究社会现象或问题，在指导教师的指导下独立完成的文章。学位论文要求具有一定的学术性，尤其是硕博士学位论文要求更高，不仅具有学术性，更要求具有前沿性、开拓性和创新性。不同层次的学位论文有不同的字数要求，学士学位论文要求5000字至1万字；硕士论文的字数要求2万字至5万字之间；博士论文则要求在8万字以上。毕业论文属于学术论文范畴。四、按照专业领域来划分从学科专业领域来看，主要有哲学、经济学、文学、政治学、行政学、数学和物理学等学科专业论文。哲学论文，主要是对哲学基本问题、哲学思维方法、哲学思想发展等开展讨论、研究，并发表自己的见解和主张的文章。经济学论文，主要是指研究经济领域方面思想以及其存在的问题进行讨论、研究，并发表自己的见解和主张的文章。文学论文，主要是指探讨文学领域理论及其存在的问题，并阐述自己的主张和见解的文章。政治学、行政学、数学和物理学等学科专业论文也是如此。五、按照研究范围来划分依据研究范围的大小，将论文分为宏观型论文和微观型论文。宏观型论文，相对于微观型论文而言的，主要是指研究带有普遍性的、全局性和整体性的问题，并对其局部具有重要的指导性意义的文章。一般来说，多半是理论性很强的文章，具有共性和指导性的特征；微观型论文，主要是指研究具体问题的文章，具有很强的针对性和具体的指导性等特征。