《唐五代文学编年史》商订

《唐五代文学编年史》以编年史的形式展示了唐五代文学发展的流程图,其成绩有目共睹。但在作家生平事迹、交游、作品系年等方面也有一些错误和疏漏。本文仅就其中的一些失误试作匡正。     

略谈刘文典对<淮南鸿烈>的贡献

东汉时期，许慎、高诱先后为《淮南鸿烈》作注。但至迟在宋代，二注已难见完帙且彼此相溷。有清一代，考据之风影响所及，治《鸿烈》者指不胜屈。刘文典的《淮南鸿烈焦解》钩沉抉隐，绳贯珠联，汇集了清代以来有关《鸿烈》校释文字；系统而完整地爬梳了《太平御览》、《文选》注等几部巨著，以征异文，辑旧注，更加以校订考释。较之于庄逵吉、王念孙、陶方琦、愈樾诸家，《集解》的校对功夫尤著。     

《词话丛编》本《窥词管见》等五种斠正

唐圭璋先生所纂《词话丛编》,为当代治词学者案头必备之要籍。然中华书局于20世纪80年代出版此编时,唐先生年事已高,无力亲自标点。书局乃不得不另请其他学者代庖。由于标点难度较大,标点者功力有所未逮,故全编错误丛出。兹就其中清代李渔《窥词管见》、王又华《古今词论》、刘体仁《七颂堂词绎》、王士旗《花草蒙拾》、贺裳《皱水轩词筌》五种里较严重的句读和标点错误加以辩正。     

学生德育主体性的匡正

学生德育主体性是影响德育实效性的重要因素。传统德育的突出问题是抑制了学生主体性:目标和内容上,以社会为本位的价值取向造成对学生主体地位的缺失;方法上,单一的道德灌输,抑制了个人主体性;实践中,德育与生活世界阻隔造成学生主体地位缺失。当前正处于"从传统德育向现代德育"的转型期,因此,要匡正学生德育主体性,提高德育实效:建立交往德育新模式;使德育回归生活世界;建立新型的师生关系。     

论添附行为对物权的消极影响及其补正

物权法中的添附制度作为取得所有权的方法,被普遍认为对增进财富、提高效率、促进物尽其用、减少交易成本具有重要的作用。然而,其对物权的消极影响不可小觑,破坏了《物权法》所确立的物权排他效力、优先效力、追及效力、物权公示原则以及物权法的立法目的。现有物权请求权并不能有效补正添附行为对物权造成的消极影响,故应对相关理论及适用规则予以甄别和补正,以体现对物权人的特殊保护。     

Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, $F_2$-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.