Public Corpus Research Articles

Where Texas Meets the Sea: Corpus Christi & Its History. By Alan Lessoff.

Urban historian Alan Lessoff’s Where Texas Meets the Sea is a thoughtful, well-written, and wide-ranging analysis. Lessoff taught at Texas A&M University–Corpus Christi and wrote this rich and detailed book after leaving that position, and he writes as an outsider with an insider’s knowledge. Lessoff demonstrates his deep understanding of the stories that residents tell about their past and of the frustrations they feel about their second-tier status among Texas cities. Lessoff begins by saying he has two audiences: historians interested in urban history and public history and Corpus Christi residents. The introduction explains that this is not a chronological history but “a consideration of different ways that the city reveals and relates to its history” (p. 12). The six chapters include an urban historian’s overview of Corpus Christi; intriguing examinations of conflicting stories about Anglo and Tejano founders; a detailed account of community quarrels about public art; a thorough study of varied efforts to revitalize downtown; and a balanced analysis of debates about recent economic stagnation. Lessoff emphasizes the past half-century and offers valuable insights into the city’s potential future direction.

Different PCA scenarios for email filtering

Improving email filtering (Ham vs. Spam emails) is a very important process. The objective of this paper is to increase the filtering accuracy and to decrease the processing time. It discusses different scenarios for Principal Component Analysis-Document Reconstruction (PCADR) classifier implemented for email filtering process The study highlights on the variation in the accuracy of a PCADR classifier with respect to the variation in feature preprocessing.Four scenarios were considered:• Scenario 1: Ham and Spam classes are represented with different features.• Scenario 2: Ham and Spam classes are represented with same features.• Scenario 3: Ham and Spam classes are represented with common terms.• Scenario 4: Ham and Spam classes are represented with common Features and Characteristic terms.Different experiments were done using a public corpus extracted from the University of California-Irvine Machine Learning Repository. Different training and test sets were used. A comparison of PCADR with Support Vector Machine and Bayes detector was done to prove its superior behavior.

Ethel Tobach (1921-2015).

With the passing of Ethel Tobach, the world of psychology has lost one of its few remaining giants. While her death brings to an end an important era in 20th-century comparative psychology, her contributions went beyond her research specialty into leadership and advocacy roles where she also had a substantial impact. Ethel's professional work and societal work were well recognized and often intertwined. Her many awards included the Kurt Lewin Award (1993) from Division 9 of the American Psychological Association (APA; Society for the Psychological Study of Social Issues), for "outstanding contributions to the development and integration of psychological research and social action." She served as president of APA Division 6 (1984 -1985, Comparative and Physiological Psychology) and APA's Division 48, (2004, the Society for the Study of Peace, Conflict, and Violence). Her professional contributions were acknowledged by being awarded the APA's Gold Medal Award for Lifetime Achievement in Psychology in the Public Interest (2003). In addition to a corpus of research publications and essays on various aspects of psychology, she is known for coediting several important books.

A protein-protein interaction extraction approach based on deep neural network

Protein-Protein Interactions PPIs information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Machine learning methods have been the most popular ones in PPI extraction area. However, these methods are still feature engineering-based, which means that their performances are also heavily dependent on the appropriate feature selection which is still a skill-dependent task. This paper presents a deep neural network-based approach which can learn complex and abstract features automatically from unlabelled data by unsupervised representation learning methods. This approach first employs the training algorithm of auto-encoders to initialise the parameters of a deep multilayer neural network. Then the gradient descent method using back propagation is applied to train this deep multilayer neural network model. Experimental results on five public PPI corpora show that our method can achieve better performance than can a multilayer neural network: on two 'toughest handling' corpora AImed and BioInfer, the former outperforms the latter with the improvements of 3.10 and 2.89 percentage units in F-score, respectively. In addition, the performance comparison with APG also verifies the effectiveness of our method.

A nearest neighbour classifier based on probabilistically/possibilistically intervals' number for spam filtering

Today, e-mail has become one of the fastest and most economical forms of communication in modern life. However, the increase in e-mail users has resulted in a significant boosting in unsolicited e-mails, widely known as spam, during the past few years. This paper presents an application of Interval's Number KNN (INKNN) for spam filtering. The INKNN algorithm was described lately as a lattice data domain extension of KNN classifier. In our experiment a spam e-mail was presented in the metric space of lattice ordered Interval's Number. Indeed a population of spam e-mails was presented by an Intervals Number. Then INKNN classifier was employed distinguish spam e-mails from non-spam. To investigate the effectiveness of our methods, we conduct extensive experiments on SpamAssassin public mail corpus. Experimental results show that the proposed model is able to achieve higher performance in comparison with those from a number of state-of-the-art machine learning techniques published in the literature.

If you study a word do you use it more often? Lexical repetition priming in a corpus of Natural Semantic Metalanguage publications

Psycholinguistic and corpus studies have shown that syntactic repetition priming can influence linguistic analyses. The impact of lexical repetition priming on linguistic work, on the other hand, has not been assessed. The current study finds evidence of lexical priming in a corpus of linguistics publications on the Natural Semantic Metalanguage (NSM), in which semantic analyses are written using several dozen ‘semantic primitives’ such as something, know and place. NSM theorists are repeatedly exposed to a small set of words, much like subjects in lexical repetition priming experiments. When all analyses written in NSM are removed from NSM publications, these texts are found to nevertheless include significantly more ‘primitives’ than control publications, suggesting that the study of particular words can affect linguists’ lexical choices. This is potentially problematic for semantic analyses in NSM, which consist of strings of primitives selected by the analysts. These primitives are not considered to be English words, but have the same forms as English words. If priming occurs between the NSM analyses and their English environment, theorists’ exposure to English may impact their choice of primitives and the content of their analyses.

Semantics, knowledge and grids

Summary Semantics, knowledge and grids represent three streams of studying humans and cyberspace that humans create and interact. Relevant research concerns all areas of computing, especially web semantics, artificial intelligence, and advanced computing infrastructures. This special issue selected eight papers reporting relevant research and applications from the 9th International Conference on Semantics, Knowledge and Grids. Copyright © 2015 John Wiley & Sons, Ltd.

Hidden struggles: Presentations of Korea in translated Korean literature

ABSTRACTInteraction between images and cultural references can make illustrations, and especially book covers, become more than mere supplements to texts. In this study I analyze the topics, paratextual presentations and covers of Korean literature translated into English and published in the USA after the Korean War. I organize topics, discourses and images into four categories of analysis (Modernity, Tradition, Religion and Struggle) and I look into the relations between the four. I aim to discern, first, the main image presented of Korea in this corpus of publications. Second, I want to uncover to what extent these three categories, which may correspond to the three stages of the creation of a literary translation (selection – translation – marketing), have a common focus. Third, based on the results, I draw conclusions regarding the different visions of Korea and the agents involved in the translation of Korean literature. The results show contradictory presentations of the different agents involved with a common hidden discourse: Struggle.

Microblog Sentiment Analysis with Emoticon Space Model

Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model (ESM) that leverages more emoticons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus (NLP&CC 2013) indicate that ESM effectively leverages emoticon signals and outperforms previous state-of-the-art strategies and benchmark best runs.

Reference rot in scholarly statement: threat and remedy

As the scholarly communication system evolves to become natively web based, citations now commonly include hyperlinks to content that is issued on the web. The content at the end of those hyperlinks is subject to what has been termed ‘reference rot’: a link may break or the content at the end of the link may no longer represent what was first noted as significant. Reference rot threatens both the usability of what is published and the long-term integrity of the scholarly record. The aim of the Hiberlink project has been to focus on this problem and then to compile and analyse a large corpus of full-text publications in order to quantify the extent of reference rot. The results are now out, and the task has shifted to alerting publishers and libraries on what to do in order to ensure that published web-based references do not rot over time. This has implications for the integrity of the scholarly record and for authors of that record. Fortunately, the Hiberlink project has progressed further than originally envisaged and has recommended remedies aimed at alleviating reference rot.

Designing a novel linear‐time graph kernel for semantic link network

SummaryGraph is an efficient tool for representing structured data such as proteins, molecular compounds, and social networks. Graph kernel is a technique to measure the similarity between graphs. However, existing graph kernels still have several limitations: (1) semantics on link is ignored; (2) node is associated with single label; (3) most graph kernels require more than cubic time, which is still computationally expensive; and, (4) there is seldom consideration of handling the graph comparison when the number of node types becomes huge. In this paper, we utilize semantic link network (SLN) to represent complex structured data with richer semantic information. Topic model is employed for dimension reduction and tagging each node with multiple labels. And a novel linear‐time graph kernel for SLN is designed to calculate the similarity between two SLNs. This work remedies the limitations of the conventional graph kernels. The effectiveness and efficiency of this approach are evaluated by the document classification task on public corpora. Empirical results demonstrate that the proposed method can achieve better performance than the traditional topic model‐based classification approach. Copyright © 2015 John Wiley & Sons, Ltd.

Bibliometrics as a tool to map uncharted territory: A study on non-professional interpreting

The descriptive and predictive applications of bibliometric methods are used in this paper to identify and describe non-professional interpreting as an emerging specialty within Translation and Interpreting Studies. ‘Collective communication efforts’ are used as a token of scholarly interest on this new area and exploited bibliometrically to build a corpus of relevant publications. Publication count methods are applied to this corpus of 268 scientific works published between 1973 and 2013 in order to shed light on the evolution of this research front, as well as its main features in terms of creation and dissemination of knowledge (authorship, publication types and disciplinary affiliations). Results point to an early apparition of the topic in the early 1970s, but a delayed growth in scholarly production starting in the early 2000s and taking the shape of journal articles and chapters in collective volumes. Research is spearheaded by a small group of scholars with a continuous interest in the issue and complemented by the research activities of ‘one-timers’ from a wide array of disciplines – from Translation and Interpreting Studies to Health Sciences, Psychology and Education – who tend to collaborate, if ever, within their discipline rather than across them.

AuthorSynth: a collaboration network and behaviorally-based visualization tool of activation reports from the neuroscience literature

Targeted collaboration is becoming more challenging with the ever-increasing number of publications, conferences, and academic responsibilities that the modern-day researcher must synthesize. Specifically, the field of neuroimaging had roughly 10,000 new papers in PubMed for the year 2013, presenting tens of thousands of international authors, each a potential collaborator working on some sub-domain in the field. To remove the burden of synthesizing an entire corpus of publications, talks, and conference interactions to find and assess collaborations, we combine meta-analytical neuroimaging informatics methods with machine learning and network analysis toward this goal. We present “AuthorSynth,”1 a novel application prototype that includes (1) a collaboration network to identify researchers with similar results reported in the literature; and (2) a 2D plot—”brain lattice”—to visually summarize a single author’s contribution to the field, and allow for searching of authors based on behavioral terms. This method capitalizes on intelligent synthesis of the neuroimaging literature, and demonstrates that data-driven approaches can be used to confirm existing collaborations, reveal potential ones, and identify gaps in published knowledge. We believe this tool exemplifies how methods from neuroimaging informatics can better inform researchers about progress and knowledge in the field, and enhance the modern workflow of finding collaborations.

First Appraisal of the Current Structure of Research on Land and Soil Degradation as Evidenced by Bibliometric Analysis of Publications on Desertification

AbstractFighting land and soil degradation is in the mandate of the United Nations Convention to Combat Desertification (UNCCD). However, it is often suggested that a lack of scientific guidance has resulted from inadequate institutional mechanisms to channel science into UNCCD decision making, rather than from a lack of research on UNCCD‐related issues. To explore this issue in more depth, this study explores the corpus of international publications dealing with land and/or with soil degradation. We extracted information from the ‘Web of Science’. By applying bibliometric methods and data mining, we mapped the key actors (laboratories, teams, and institutions) involved in research on land and on soils. Several filters were applied to the databases in combination with the word ‘desertification’. The further use of text mining software (Tetralogie®) allowed the analyses of similarities and differences between keywords, disciplines, authors, and regions and identifies obvious clusters. Understanding interconnections between these clusters ultimately allowed a first diagnosis of some of the strengths and weaknesses of the scientific community dealing with desertification. This type of detailed analyses can contribute to clarify the scientific landscape at large, and further in‐depth studies could lead to improvements in the way scientific advice is channeled into the UNCCD. Copyright © 2015 John Wiley & Sons, Ltd.

A decade of research in statistics: a topic model approach

Topic models are a well known clustering approach for textual data, which provides promising applications in the bibliometric context for the purpose of discovering scientific topics and trends in a corpus of scientific publications. However, topic models per se provide poorly descriptive metadata featuring the discovered clusters of publications and they are not related to the other important metadata usually available with publications, such as authors affiliation, publication venue, and publication year. In this paper, we propose a methodological approach to topic modeling and post-processing of topic models results to the end of describing in depth a field of research over time. In particular, we work on a selection of publications from the international statistical literature, we propose an approach that allows us to identify sophisticated topic descriptors, and we analyze the links between topics and their temporal evolution.

A practical application of text mining to literature on cognitive rehabilitation and enhancement through neurostimulation.

The exponential growth in publications represents a major challenge for researchers. Many scientific domains, including neuroscience, are not yet fully engaged in exploiting large bodies of publications. In this paper, we promote the idea to partially automate the processing of scientific documents, specifically using text mining (TM), to efficiently review big corpora of publications. The “cognitive advantage” given by TM is mainly related to the automatic extraction of relevant trends from corpora of literature, otherwise impossible to analyze in short periods of time. Specifically, the benefits of TM are increased speed, quality and reproducibility of text processing, boosted by rapid updates of the results. First, we selected a set of TM-tools that allow user-friendly approaches of the scientific literature, and which could serve as a guide for researchers willing to incorporate TM in their work. Second, we used these TM-tools to obtain basic insights into the relevant literature on cognitive rehabilitation (CR) and cognitive enhancement (CE) using transcranial magnetic stimulation (TMS). TM readily extracted the diversity of TMS applications in CR and CE from vast corpora of publications, automatically retrieving trends already described in published reviews. TMS emerged as one of the important non-invasive tools that can both improve cognitive and motor functions in numerous neurological diseases and induce modulations/enhancements of many fundamental brain functions. TM also revealed trends in big corpora of publications by extracting occurrence frequency and relationships of particular subtopics. Moreover, we showed that CR and CE share research topics, both aiming to increase the brain's capacity to process information, thus supporting their integration in a larger perspective. Methodologically, despite limitations of a simple user-friendly approach, TM served well the reviewing process.

A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems.

BackgroundText mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in literature. A relation extraction system achieving high performance is expensive to develop because of the substantial time and effort required for its design and implementation. Here, we report a novel framework to facilitate the development of a pattern-based biomedical relation extraction system. It has several unique design features: (1) leveraging syntactic variations possible in a language and automatically generating extraction patterns in a systematic manner, (2) applying sentence simplification to improve the coverage of extraction patterns, and (3) identifying referential relations between a syntactic argument of a predicate and the actual target expected in the relation extraction task.ResultsA relation extraction system derived using the proposed framework achieved overall F-scores of 72.66% for the Simple events and 55.57% for the Binding events on the BioNLP-ST 2011 GE test set, comparing favorably with the top performing systems that participated in the BioNLP-ST 2011 GE task. We obtained similar results on the BioNLP-ST 2013 GE test set (80.07% and 60.58%, respectively). We conducted additional experiments on the training and development sets to provide a more detailed analysis of the system and its individual modules. This analysis indicates that without increasing the number of patterns, simplification and referential relation linking play a key role in the effective extraction of biomedical relations.ConclusionsIn this paper, we present a novel framework for fast development of relation extraction systems. The framework requires only a list of triggers as input, and does not need information from an annotated corpus. Thus, we reduce the involvement of domain experts, who would otherwise have to provide manual annotations and help with the design of hand crafted patterns. We demonstrate how our framework is used to develop a system which achieves state-of-the-art performance on a public benchmark corpus.

Exploring features of highly productive research contexts in Asia: a comparison of knowledge production in educational leadership in Israel and Hong Kong

A recent review of research identified two Asian societies that represented “positive outliers” in the production of published knowledge on educational leadership and management: Hong Kong and Israel. These were the only Asian societies that had produced a critical mass of publications in this field in international journals over the past two decades. The current study examined the nature of the publication corpus from Hong Kong and Israel in an effort to understand factors that may have contributed to this capacity for high research productivity. The results suggested that different strategies may have accounted for research capacity development in these two societies. Directions are identified for future research.

A discussion on Brazil-focused publications

Purpose – The aim of this article is to carry out a critical analysis of the view that core countries’ knowledge holds sway over that of the periphery and to offer a reassessment of Lahiri’s Anglo-Eurocentric point of view and the implications this has for scientific discussion in the field of management. To do this the authors made use of both Lahiri’s (2011) comments as a representative of mainstream thought and the existing literature on this issue. Design/methodology/approach – In this research, the authors used the methodology proposed by Lahiri (2011), but with some adjustments that they thought were necessary. They also analysed scientific production on management in the most important Brazilian journals to put the field of organizational studies in Brazil in context and to situate the reader as regarding their criticism of the North-centric viewpoint. This is because the authors consider organizational studies as a synonym for management studies. Findings – The results presented in Lahiri’s (2011) article “Brazil-focused publications in leading business journals” are invalid because the sample on which the article is based is limited to 14 academic journals, of which 7 are American, 6 are British and 1 is German. It can be questioned whether this particular selection of journals accurately represents the corpus of publications that deal with Brazil, given that Lahiri does not include any articles published in Brazil and thus reinforces his North-centric standpoint. Moreover, the article emphasizes authors with ties to English institutions and concludes that the most relevant academic contributions on Brazil have been produced at non-Brazilian institutions. Moreover, the authors also discovered a contradiction in the sample that is important for the thesis presented in this article because, in fact, the scholars selected for the sample emphasized in that work have ties to both a foreign and a Brazilian institution. Originality/value – The originality of this article lies in the fact that it proposes to discuss the production and consumption of knowledge in the area of administration in such a way as to reassess North-centric hegemony in this field.

Driving forces of researchers mobility

Starting from the dataset of the publication corpus of the APS during the period 1955–2009, we reconstruct the individual researchers trajectories, namely the list of the consecutive affiliations for each scholar. Crossing this information with different geographic datasets we embed these trajectories in a spatial framework. Using methods from network theory and complex systems analysis we characterise these patterns in terms of topological network properties and we analyse the dependence of an academic path across different dimensions: the distance between two subsequent positions, the relative importance of the institutions (in terms of number of publications) and some socio–cultural traits. We show that distance is not always a good predictor for the next affiliation while other factors like “the previous steps” of the career of the researchers (in particular the first position) or the linguistic and historical similarity between two countries can have an important impact. Finally we show that the dataset exhibit a memory effect, hence the fate of a career strongly depends from the first two affiliations.