Examining Scientific Writing Styles from the Perspective of Linguistic Complexity

Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity; and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.

Measuring Book Impact Based on the Multi-granularity Online Review Mining

As with articles and journals, the customary methods for measuring books' academic impact mainly involve citations, which is easy but limited to interrogating traditional citation databases and scholarly book reviews, Researchers have attempted to use other metrics, such as Google Books, libcitation, and publisher prestige. However, these approaches lack content-level information and cannot determine the citation intentions of users. Meanwhile, the abundant online review resources concerning academic books can be used to mine deeper information and content utilizing altmetric perspectives. In this study, we measure the impacts of academic books by multi-granularity mining online reviews, and we identify factors that affect a book's impact. First, online reviews of a sample of academic books on Amazon.cn are crawled and processed. Then, multi-granularity review mining is conducted to identify review sentiment polarities and aspects' sentiment values. Lastly, the numbers of positive reviews and negative reviews, aspect sentiment values, star values, and information regarding helpfulness are integrated via the entropy method, and lead to the calculation of the final book impact scores. The results of a correlation analysis of book impact scores obtained via our method versus traditional book citations show that, although there are substantial differences between subject areas, online book reviews tend to reflect the academic impact. Thus, we infer that online reviews represent a promising source for mining book impact within the altmetric perspective and at the multi-granularity content level. Moreover, our proposed method might also be a means by which to measure other books besides academic publications.

Online shopping behavior study based on multi-granularity opinion mining: China vs. America

With the development of e-commerce, many products are now being sold worldwide, and manufacturers are eager to obtain a better understanding of customer behavior in various regions. To achieve this goal, most previous efforts have focused mainly on questionnaires, which are time-consuming and costly. The tremendous volume of product reviews on e-commerce websites has seen a new trend emerge, whereby manufacturers attempt to understand user preferences by analyzing online reviews. Following this trend, this paper addresses the problem of studying customer behavior by exploiting recently developed opinion mining techniques. This work is novel for three reasons. First, questionnaire-based investigation is automatically enabled by employing algorithms for template-based question generation and opinion mining-based answer extraction. Using this system, manufacturers are able to obtain reports of customer behavior featuring a much larger sample size, more direct information, a higher degree of automation, and a lower cost. Second, international customer behavior study is made easier by integrating tools for multilingual opinion mining. Third, this is the first time an automatic questionnaire investigation has been conducted to compare customer behavior in China and America, where product reviews are written and read in Chinese and English, respectively. Our study on digital cameras, smartphones, and tablet computers yields three findings. First, Chinese customers follow the Doctrine of the Mean, and often use euphemistic expressions, while American customers express their opinions more directly. Second, Chinese customers care more about general feelings, while American customers pay more attention to product details. Third, Chinese customers focus on external features, while American customers care more about the internal features of products.

Bilingual Terminology Extraction Using Multi-level Termhood

Purpose: Terminology is the set of technical words or expressions used in specific contexts, which denotes the core concept in a formal discipline and is usually applied in the fields of machine translation, information retrieval, information extraction and text categorization, etc. Bilingual terminology extraction plays an important role in the application of bilingual dictionary compilation, bilingual Ontology construction, machine translation and cross-language information retrieval etc. This paper addresses the issues of monolingual terminology extraction and bilingual term alignment based on multi-level termhood. Design/methodology/approach: A method based on multi-level termhood is proposed. The new method computes the termhood of the terminology candidate as well as the sentence that includes the terminology by the comparison of the corpus. Since terminologies and general words usually have differently distribution in the corpus, termhood can also be used to constrain and enhance the performance of term alignment when aligning bilingual terms on the parallel corpus. In this paper, bilingual term alignment based on termhood constraints is presented. Findings: Experiment results show multi-level termhood can get better performance than existing method for terminology extraction. If termhood is used as constrain factor, the performance of bilingual term alignment can be improved.

Termhood-based Comparability Metrics of Comparable Corpus in Special Domain

Cross-Language Information Retrieval (CLIR) and machine translation (MT) resources, such as dictionaries and parallel corpora, are scarce and hard to come by for special domains. Besides, these resources are just limited to a few languages, such as English, French, and Spanish and so on. So, obtaining comparable corpora automatically for such domains could be an answer to this problem effectively. Comparable corpora, that the subcorpora are not translations of each other, can be easily obtained from web. Therefore, building and using comparable corpora is often a more feasible option in multilingual information processing. Comparability metrics is one of key issues in the field of building and using comparable corpus. Currently, there is no widely accepted definition or metrics method of corpus comparability. In fact, Different definitions or metrics methods of comparability might be given to suit various tasks about natural language processing. A new comparability, namely, termhood-based metrics, oriented to the task of bilingual terminology extraction, is proposed in this paper. In this method, words are ranked by termhood not frequency, and then the cosine similarities, calculated based on the ranking lists of word termhood, is used as comparability. Experiments results show that termhood-based metrics performs better than traditional frequency-based metrics.