注意字形相似的字

在报刊上出现的错字中，字形相似的字出错，占的比例较大。其原因，也可能是作者笔误造成的，也可能是没有校对出来，后一种可能性大一些，因为即使作者笔误，校对有责任纠正其错误。所以，无论是作者或是编辑、校对，都有责任共同消灭这种错误。这里举一些常见的错误例句，以引起注意。有的是字形相近，而字音、字义不同，例如：“有的还能用英语、奥语进行交流”——“奥语”的“奥”字，同广东的简称“粤”字，字型很相近。误将“粤语”错成“奥语”。“收到爱人寄来的一朵枯萎的攻瑰”——“攻瑰”中的“攻”字，同“玫瑰”中的“玫”字…     

形似字与文稿纠错

从事编校实务的人,说起消除文稿中的差错,大都会有这样的体认:在书刊文稿出现的所有差错类型中,最频繁遇到且最诡谲难防的当数形似字.形似字是指字形相似的一组字.     

常用字辨（下）

以下这几组字，也是容易混淆的，在此加以辨析。屈曲“屈”、“曲”读音相同，都读qū。二字都能表示弯曲、使其弯曲（“屈指”、“屈膝”，“曲线”、“曲径”、“弯腰曲背”、“曲突徙薪”）。但是“曲”指弯曲的地方（“河曲”、“乡曲”），又指违背（“曲意逢迎”）、婉转（“曲折”、“委曲”）；“屈”不能这样用。“屈”指被迫顺从（“屈服”、“屈从”）、穷尽（“理屈词穷”）、冤枉（“冤屈”、“委屈”）；“曲”不能这样用。“曲”读qǔ，指歌曲（“散曲”、“南曲”、“北曲”）、杂剧（“元曲”）、曲谱（“乐曲”），是另一…     

试论张土俊泽存堂刻本《群经音辨》

清代的《群经音辨》刺本中，康熙张士俊《泽存堂五种》本是一个代表，在谊书的流传过程中起到了枢纽作用，为学术的传播和发展做出了重要贡献。然而。由于种种原因，从陆心源到张元济皆表扬宋本。批评泽存堂本改易原本、脱误甚多。通过考察张氏刻书的缘起和经过，并以此本与《四部丛刊续编》影印南宋汀州本相比勘，发现该本具有较高的校勘价值．在某些方面确能补汀州本之不足。     

辨是非:读校活动的第一要义

书稿的电子化使传统意义上的原稿与校样合而为一,校对方式变为以读校为主,校是非的功能凸现,这是校对工作者必须面对的现实.要想解决书稿中的是非问题,首先必须辨明是非.做不到这一点,校是非就无从谈起,读校活动也就失去了意义.本文结合实例分析、探讨了这方面的问题,得出了"辨是非是读校活动的第一要义"这一结论.     

“杂”字开路 突出综合素质和运用能力培养——编辑出版学教学改革初试

做一个“杂家”．对做好编辑工作至为重要,这已是一个不争的事实。“杂家”虽然不是靠眼前的学位、学历教育培养出来的．但在编辑出版学的教学过程中树立“杂家”意识。灌输“杂家”思想,有目的地提高学生的综合素养．增强其分析解决问题的能力,为其适应工作并做出成绩奠定扎实的基础是十分必要的。     

A data quality approach to the identification of discrimination risk in automated decision making systems

Automated decision-making (ADM) systems may affect multiple aspects of our lives. In particular, they can result in systematic discrimination of specific population groups, in violation of the EU Charter of Fundamental Rights. One of the potential causes of discriminative behavior, i.e., unfairness, lies in the quality of the data used to train such ADM systems.Using a data quality measurement approach combined with risk management, both defined in ISO standards, we focus on balance characteristics and we aim to understand how balance indexes (Gini, Simpson, Shannon, Imbalance Ratio) identify discrimination risk in six large datasets containing the classification output of ADM systems.The best result is achieved using the Imbalance Ratio index. Gini and Shannon indexes tend to assume high values and for this reason they have modest results in both aspects: further experimentation with different thresholds is needed.In terms of policies, the risk-based approach is a core element of the EU approach to regulate algorithmic systems: in this context, balance measures can be easily assumed as risk indicators of propagation – or even amplification – of bias in the input data of ADM systems.     

Finding related sentence pairs in MEDLINE

We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning. We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function, achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average. We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts by this automatic procedure.     

Children's dependence upon visual context in sentence comprehension

This study explored the manner in which young children utilize meaning clues implicit in nonlinguistic aspects of context to aid comprehension of difficult sentences. Comprehension of reversible passive sentences was measured for the degree to which visual context could be helpful to sentence understanding. Results indicated a strong reliance on visual cues by children from three to seven years of age. The use of visual aids as a means of helping children develop comprehension strategies is suggested.     

科技期刊中常见详略失当和混杂错误例析

针对科技期刊中常见又不易辨识的语句表达失当问题,采用列举典型实例的方式,逐一剖析重复错误、苟简错误、混杂错误、逻辑错误,并给出了相应的修正示范.     

Statistical query expansion for sentence retrieval and its effects on weak and strong queries

The retrieval of sentences that are relevant to a given information need is a challenging passage retrieval task. In this context, the well-known vocabulary mismatch problem arises severely because of the fine granularity of the task. Short queries, which are usually the rule rather than the exception, aggravate the problem. Consequently, effective sentence retrieval methods tend to apply some form of query expansion, usually based on pseudo-relevance feedback. Nevertheless, there are no extensive studies comparing different statistical expansion strategies for sentence retrieval. In this work we study thoroughly the effect of distinct statistical expansion methods on sentence retrieval. We start from a set of retrieved documents in which relevant sentences have to be found. In our experiments different term selection strategies are evaluated and we provide empirical evidence to show that expansion before sentence retrieval yields competitive performance. This is particularly novel because expansion for sentence retrieval is often done after sentence retrieval (i.e. expansion terms are mined from a ranked set of sentences) and there are no comparative results available between both types of expansion. Furthermore, this comparison is particularly valuable because there are important implications in time efficiency. We also carefully analyze expansion on weak and strong queries and demonstrate clearly that expanding queries before sentence retrieval is not only more convenient for efficiency purposes, but also more effective when handling poor queries.