档案学研究热点分析——基于2004-2008年《档案学研究》、《档案学通讯》论文关键词的词频分析

本文以《档案学研究》、《档案学通讯》为统计源,运用文献计量学的词频分析方法,对2004-2008年《档案学研究》、《档案学通讯》这两种期刊论文的关键词进行统计,通过词频分析描述该学科学术研究状况,进而揭示档案学的研究热点和发展趋势。     

"城中隐士"之歌--论林语堂《京华烟云》的悲剧意蕴和快乐哲学

《京华烟云》是林语堂先生小说的最高成就之作,他以史诗的规模向我们细述了20世纪前期三四十年间北京城内三个富贵之家的兴衰变幻和三代人的悲欢离合。书中所渗透着浓厚的悲剧意蕴和快乐哲学则是作者独特的人生哲学的深层表现。     

一部科学揭示字谜奥秘的修辞学著作——评曹石珠教授的《走进字谜的艺术宫殿》

曹石珠教授独著的《走进字谜的艺术宫殿——汉字修辞视野下的字谜研究》,从字谜的思维方法、修辞特点与修辞方式、字谜的分类和解谜的方法等角度科学地揭示了字谜的奥秘——利用字形的离合、增损,对于字谜的创作和猜射具有重要意义。     

Cognitive-inspired domain adaptation of sentiment lexicons

Sentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specific sentiment lexicons are made available, it is impractical to build an ex ante lexicon that fully reflects the characteristics of the language usage in endless domains. In this article, we propose a novel approach to simultaneously train a vanilla sentiment classifier and adapt word polarities to the target domain. Specifically, we sequentially track the wrongly predicted sentences and use them as the supervision instead of addressing the gold standard as a whole to emulate the life-long cognitive process of lexicon learning. An exploration-exploitation mechanism is designed to trade off between searching for new sentiment words and updating the polarity score of one word. Experimental results on several popular datasets show that our approach significantly improves the sentiment classification performance for a variety of domains by means of improving the quality of sentiment lexicons. Case-studies also illustrate how polarity scores of the same words are discovered for different domains.     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.     

The hidden depths of new word knowledge: Using graded measures of orthographic and semantic learning to measure vocabulary acquisition

We investigated whether the presence of orthography promotes new word learning (orthographic facilitation). In Study 1 (N = 41) and Study 2 (N = 74), children were taught 16 unknown polysyllabic words. Half of the words appeared with orthography present and half without orthography. Learning assessments captured the degree of semantic and orthographic learning; they were administered one week after teaching (Studies 1 and 2), and, unusually, eight months later (Study 1 only). Bayesian analyses indicated that the presence of orthography was associated with more word learning, though this effect was estimated with more certainty for orthographic than semantic learning. Newly learned word knowledge was well retained over time, indicating that our paradigm was sufficient to support long-term learning. Our approach provides an example of how word learning studies can look beyond simple accuracy measures to reveal the cumulative nature of lexical learning.     

Word sense disambiguation based on context selection using knowledge-based word similarity

In this paper, we introduce a novel knowledge-based word-sense disambiguation (WSD) system. In particular, the main goal of our research is to find an effective way to filter out unnecessary information by using word similarity. For this, we adopt two methods in our WSD system. First, we propose a novel encoding method for word vector representation by considering the graphical semantic relationships from the lexical knowledge bases, and the word vector representation is utilized to determine the word similarity in our WSD system. Second, we present an effective method for extracting the contextual words from a text for analyzing an ambiguous word based on word similarity. The results demonstrate that the suggested methods significantly enhance the baseline WSD performance in all corpora. In particular, the performance on nouns is similar to those of the state-of-the-art knowledge-based WSD models, and the performance on verbs surpasses that of the existing knowledge-based WSD models.     

《马氏文通》中的修辞研究述评

在蒋文野(1985)、周远富(1998)、廖序东(1999)、刘昌海(2001)等学者的研究背景下,在参阅文献的基础上,归纳出《马氏文通》在修辞学领域的研究成果。马氏在微观修辞学领域主要涉及了字的运用、语音节奏的协调、句式的选用;在理论修辞学领域涉及了辞格研究;在应用修辞学领域涉及了语体风格研究。     

词族现象探微

词族的大量出现,有其独特的语言学价值。它说明了附加构词法表现非常突出,汉语的最佳词音长度受到挑战,某些外来悟睚成为汉语中能产性极强的构词语素。词族现象也为我们提供了透视社会变革,考察人们思想观念变化等方面的认识价值。     

《说文》前叙称经说

许慎《说文》第十五卷包括许氏自撰的前后两叙和其子许冲的进书表三项内容。其中对前叙的末尾几句,历来学者有不同的见解。许慎所学《孝经》确属古文本。许氏的学术渊源兼有今、古,显示其博通明达,不愧“五经无双”之号。如以古文限之,未免小视许氏了。