On the three-layer passage reading method in English extensive reading

This paper raises, for the first time, with a model reading passage, a three-layer passage reading method （3LPRM in brief ） in English reading： reading beyond paragraphs; reading within paragraphs and sentences, and reading with coherences of words and expressions. The paper illustrates the definition, contents, characteristics and application of the method which is easy to master and apply. The paper aims at supplying readers especially beginners of E/S/FL （English as second/foreign language） with a practical passage reading method so as to improve their reading efficiency. The paper has some practical significance and directing value.

一种基于用户轨迹的跨社交网络用户身份识别算法

针对现有的基于用户轨迹的跨社交网络用户身份识别算法未考虑用户轨迹中的位置访问顺序特征的缺点,该文提出一种基于Paragraph2vec的跨社交网络用户轨迹匹配算法(CDTraj2vec)。首先将用户轨迹转化为易于处理的网格化表示,并按照一定的时间粒度、距离尺度对原始的用户轨迹进行划分,使用户轨迹中的位置访问顺序特征易于抽取;然后利用Paragraph2vec算法中PV-DM模型抽取轨迹序列中位置访问顺序特征,得到用户轨迹的向量表示。最后通过用户轨迹向量判定轨迹是否匹配。在社交网络BrightKite上的实验结果表明,与基于位置访问频率或者基于轨迹间距离的方法相比,F值提高了2%～4%个百分点,所提算法能够有效地抽取出用户轨迹中的位置访问顺序特征,更加准确地实现了基于用户轨迹的跨社交网络用户身份识别。

Word文档中的图片向数据库转换方法的实现

从一个Word文档实例入手,分析Word文档的组成、VBA中对象的应用区间以及Delphi对Word的支持,并设计一种Word文档中的图片信息向SQL Server数据库进行转换的实现方法,通过这种方法可以借助于程序实现自动阅读Word中的信息,将Word文档中的图片信息提取出来存于相应的数据库文件中。验证表明,在办公自动化过程中,用自动转换方法来处理一些包含图片信息的Word文档向数据库的转换是行之有效的方法,可以实现快速、高效的信息转换,极大地提高工作效率。

网络异源数据社会风险预估及有效性分析

针对互联网中异源数据的风险预估问题,借鉴社会心理学中社会风险研究成果,提出基于深度学习模型Paragraph Vector的风险分类和风险预估方法.选择"天涯社区"的"天涯杂谈"和"百姓声音"子板块为例,通过话题分析和相似度比较说明了不同子版块之间社会风险分类的可行性;利用基于Paragraph Vector的K-Nearest Neighbors分类方法(KNN-PV),以"天涯杂谈"标注数据为训练集,实现"百姓声音"相同时间段新发帖的风险分类和风险预估,并通过人工标注结果验证了KNN-PV方法的有效性.研究结果表明,基于机器学习方法能够实现互联网中异源数据的风险分类和风险预估.

Paragraph Vector Representation Based on Word to Vector and CNN Learning

Document processing in natural language includes retrieval,sentiment analysis,theme extraction,etc.Classical methods for handling these tasks are based on models of probability,semantics and networks for machine learning.The probability model is loss of semantic information in essential,and it influences the processing accuracy.Machine learning approaches include supervised,unsupervised,and semi-supervised approaches,labeled corpora is necessary for semantics model and supervised learning.The method for achieving a reliably labeled corpus is done manually,it is costly and time-consuming because people have to read each document and annotate the label of each document.Recently,the continuous CBOW model is efficient for learning high-quality distributed vector representations,and it can capture a large number of precise syntactic and semantic word relationships,this model can be easily extended to learn paragraph vector,but it is not precise.Towards these problems,this paper is devoted to developing a new model for learning paragraph vector,we combine the CBOW model and CNNs to establish a new deep learning model.Experimental results show that paragraph vector generated by the new model is better than the paragraph vector generated by CBOW model in semantic relativeness and accuracy.

基于Paragraph Vector模型的科研热点发现方法

科研热点对科学研究具有指导意义,目前的科研热点发现主要依赖文本聚类技术,针对文本表示存在的特征语义表达能力不强、无法揭示词语之间潜在联系的问题,提出了一种基于Paragraph Vector模型的科研热点发现方法。该方法对文本集使用Paragraph Vector模型构建文本语义向量表示,并根据语义向量的相似度计算结果进行聚类分析得到主题集,最后基于主题文本引用特征选择前N个主题作为科研热点。实验结果表明,科研热点发现的聚类评价ARI值为0.452、H值为0.532、C值为0.538、V值为0.535,说明Paragraph Vector的低维空间表示法可通过挖掘词之间的关联属性优化向量语义表示,并能缓解维数灾难问题进而提高了话题发现的准确度。

Contrastive Rhetoric: Chinese Students and Academic Writin

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English Paragraph Construction Rules and Implementation Techniques

A complete English Paragraph consists of five parts:opening sentence,topic sentence,supporting sentences,concluding sentence(and sometimes transitional sentence).The paper explores the construction rules of paragraph and explains its implementation techniques.The significance lies in that it is helpful to the teaching and study in some fields like reading comprehension,speaking and writing.

Showcasing modeling strategies in the ESOL writing class： Blending rhetorical fluency with grammatical accuracy

This paper argues for an overt innovational shift in praxis, as well as classroom configuration in the ESOL writing class by calling for a move away from the current foci on process-based pedagogies for newcomer populations, to an explicit teaching of modeling strategies with concomitant practice opportunities provided in the ESOL writing class. It is argued that explicit, sequenced instruction in the domains of rhetorical structure as well as grammatical accuracy provide ESOL （English for Speakers of Other Languages） learners in the emerging stages of language learning with a more concrete grasp of meaning, structure and grammar in rhetorical construction. The modeling strategies proposed in the paper focus on a simultaneous building of rhetorical fluency and grammatical accuracy via spotlighted and sequenced strategies which afford learners practice in smaller chunks of composition including but not limited to thesis statement writing, varied paragraph organization, multiple modes of exposition, and grammatical complexity all in a bid to generate rhetorical depth and grammatical detail in writing. In short, both form and function need to be explicitly taught in the ESOL writing class with adequate opportunities provided for rhetorical practice. Using a meticulous blend of meaningful, authentic and purposeful tasks combined with one-on-one instruction which incorporates a variety of visual and rhetorical modeling strategies, emerging writers, it is argued a move from controlled to automatic writing fluency within a short time span. The pedagogy proposed in the current paper spotlights the specific learner, rather than the writing process and entails a move away from traditional, teacher-fronted classrooms to targeted, workshop-centered configurations which permit for one-on-one conferencing in the ESOL writing class. The visually rendered modeling strategies proposed in this paper argue for writing instruction for ESOL students which is learner responsive, relevant and practical.

Using Distant Supervision and Paragraph Vector for Large Scale Relation Extraction

Distant supervision has the ability to generate a huge amount training data.Recently,the multi-instance multi-label learning is imported to distant supervision to combat noisy data and improve the performance of relation extraction.But multi-instance multi-label learning only uses hidden variables when inference relation between entities,which could not make full use of training data.Besides,traditional lexical and syntactic features are defective reflecting domain knowledge and global information of sentence,which limits the system’s performance.This paper presents a novel approach for multi-instance multilabel learning,which takes the idea of fuzzy classification.We use cluster center as train-data and in this way we can adequately utilize sentencelevel features.Meanwhile,we extend feature set by paragraph vector,which carries semantic information of sentences.We conduct an extensive empirical study to verify our contributions.The result shows our method is superior to the state-of-the-art distant supervised baseline.

An AntConc-based Analysis of the First Paragraph of In Another Country

This essay aims at analyzing the stylistic features of Hemingway’s In Another Country from linguistic point of view.Different from qualitative analysis,the first paragraph,which is one of the rare depictions of the environment in Hemingway’s works,is chosen as a sample to perform quantitative analysis based on AntConc,whose data shows that Hemingway prefers words of Anglo-Saxon origin and using repetition and polysemy,revealing his writing“principle of iceberg”.

Comprehensive Relation Modelling for Image Paragraph Generation

Image paragraph generation aims to generate a long description composed of multiple sentences,which is different from traditional image captioning containing only one sentence.Most of previous methods are dedicated to extracting rich features from image regions,and ignore modelling the visual relationships.In this paper,we propose a novel method to generate a paragraph by modelling visual relationships comprehensively.First,we parse an image into a scene graph,where each node represents a specific object and each edge denotes the relationship between two objects.Second,we enrich the object features by implicitly encoding visual relationships through a graph convolutional network(GCN).We further explore high-order relations between different relation features using another graph convolutional network.In addition,we obtain the linguistic features by projecting the predicted object labels and their relationships into a semantic embedding space.With these features,we present an attention-based topic generation network to select relevant features and produce a set of topic vectors,which are then utilized to generate multiple sentences.We evaluate the proposed method on the Stanford image-paragraph dataset which is currently the only available dataset for image paragraph generation,and our method achieves competitive performance in comparison with other state-of-the-art(SOTA)methods.

The Distributed Representation for Societal Risk Classification toward BBS Posts

The risk classification of BBS posts is important to the evaluation of societal risk level within a period. Using the posts collected from Tianya forum as the data source, the authors adopted the societal risk indicators from socio psychology, and conduct document-level multiple societal risk classification of BBS posts. To effectively capture the semantics and word order of documents, a shallow neural network as Paragraph Vector is applied to realize the distributed vector representations of the posts in the vector space. Based on the document vectors, the authors apply one classification method KNN to identify the societal risk category of the posts. The experimental results reveal that paragraph vector in document-level societal risk classification achieves much faster training speed and at least 10% improvements of F-measures than Bag-of-Words. Furthermore, the performance of paragraph vector is also superior to edit distance and Lucene-based search method. The present work is the first attempt of combining document embedding method with socio psychology research results to public opinions area.

美国Paragraph Ⅳ专利挑战成功首仿对中国制药工业的启示录

首仿药的专利挑战Paragraph Ⅳ制度及180天市场独占期的规定,在一定程度上保护了首仿药企业的市场优势。本文梳理了2018年上旬美国专利挑战成功首仿的品种,通过总结和个案分析,为中国制药工业的发展提出了通过建立首仿药目录和待仿品种清单完善仿制药数据系统、建立REMS专业性网站、充实中国上市药品目录集数据库和优选首仿药品种4项策略。

ENSEMBLE OF MULTIPLE kNN CLASSIFIERS FOR SOCIETAL RISK CLASSIFICATION

Societal risk classification is a fundamental and complex issue for societal risk perception. To conduct societal risk classification, Tianya Forum posts are selected as the data source, and four kinds of representations： string representation, term-frequency representation, TF-IDF representation and the distributed representation of BBS posts are applied. Using edit distance or cosine similarity as distance metric, four k-Nearest Neighbor （kNN） classifiers based on different representations are developed and compared. Owing to the priority of word order and semantic extraction of the neural network model Paragraph Vector, kNN based on the distributed representation generated by Paragraph Vector （kNN-PV） shows effectiveness for societal risk classification. Furthermore, to improve the performance of societal risk classification, through different weights, kNN-PV is combined with other three kNN classifiers as an ensemble model. Through brute force grid search method, the optimal weights are assigned to different kNN classifiers. Compared with kNN-PV, the experimental results reveal that Macro-F of the ensemble method is significantly improved for societal risk classification.

What Characterizes Chinese Students’ Exposition Besides Deduction and Induction?: A Comparative Rhetoric Perspective

Rhetorical features of Chinese writers’essays have been studied for decades but inconsistent interpretations of deduction and induction lead to controversial results.Taking a comparative rhetoric perspective,this paper clarifies the notions of deduction and induction and investigates what rhetorical features characterize Chinese expository paragraphs besides deduction and induction and whether Chinese EFL learners’English paragraphs have similar features.Two kinds of data sources were used—29 full-score Chinese expositions in College Entrance Examinations and 29 English expositions written by Chinese EFL learners.The results show that deduction is preferred in both Chinese and EFL writing,and that rhetorical paragraphs and coordinate paragraphs are particular to Chinese writing while the EFL learners’paragraphs display hybrid rhetoric such as semi-coordination.It is concluded that neither Chinese paragraphs nor EFL ones are similar to the modern English rhetorical paradigm,and English rhetoric instruction will facilitate the introspection of the two kinds of rhetoric.