Text Domain Research Articles

Introduction: Multimodal Anthropology and the Politics of Invention

This essay and the articles included in this special issue theorize the possibilities – and pitfalls – that emerge as anthropologists utilise a combination of audio, video, text, still images, performance methodologies, and web platforms to iteratively, collaboratively, and sensually generate relations with research participants, interdisciplinary colleagues and beyond. We are not necessarily interested in developing multimedia approaches to representing or disseminating anthropological knowledge – rather, we are concerned with how multimodality may contribute to a politics of invention for the discipline. We argue that multimodality offers a line of flight for an anthropology yet to come: multi-sensorial rather than text-based, performative rather than representational, and inventive rather than descriptive. This reimagined anthropology requires a move away from established forms of authorship, representation and academic publishing towards projects that experiment with unanticipated forms, collaborations, audiences and correspondences – including questioning what the open in Open Access should signify, as Anand Pandian (2018) has compellingly argued. As importantly, a focus on multimodality and invention invites a reconsideration of the pedagogy of anthropology – both in the sense of what gets formally taught within the disciplinary canon, and in relation to the manifold ways of teaching and learning together that emerge during fieldwork, not always made visible, and which exceed the textual and conceptual domain. Indeed, we use multimodality and invention to refer to the multiple ways of doing ethnography - and the resulting multiple anthropologies - that create ways of knowing and learning together differently. In the essay that follows we offer several provocations that multimodality and invention produce with regards to pedagogy, publication, and collaboration – which are picked up in novel ways in each of the articles included as part of this collection. Our essay is not meant as an enclosure, or a boundary, but rather a framing – that is, a point of view or an orientation to the multiple questions that emerge in each of the essays, where the respective anthropologists rethink engagement, form, and purpose in their ethnographic endeavours. We draw from John Jackson Jr. to argue that framing is at once “a gesture toward contextualization (a conceptual framing of the relevant issues) and a singular impression captured in time (as in the presentation of a framed painting or the relative irreducibility of a film or video still)”. Taking Jackson Jr.’s second point to heart, we offer this introductory essay as a still image by which to see with and through the ethnographic engagements of others. In this still image, the concepts of multimodality and invention are unpacked and interrogated in ways we hope offer an alternative way to think about ethnography and anthropological theory in a moment where the discipline is grappling with how to find ways to engage more effectively with the increasingly fractured and precarious worlds we inhabit.

Enhanced Security for Dynamic Multi Keyword Ranked Search Using Greedy Best First Search

Today, due to the enormous growth of data technology in cloud computing, the data owners are stimulated to outsource their data in data management to reduce cost and for the convenient. Data confidentiality, in general, can be obtained by encrypting the data before it is outsourced. The client stores the encrypted data to the cloud using Searchable encryption schemes and applies keyword search techniques over cipher text domain. But the main problem in outsourcing is the lack of security and privacy for the sensitive data. So, to overcome this, for privacy requirement, the sensitive data can be encrypted before it is outsourced. Various methods were proposed to preserve the privacy and to provide security to the cloud data which are encrypted. Here in this paper, we proposed a tree-based search method over the encrypted datain the cloud that supports dynamic operation and multi-keyword ranked search. Clearly, the commonly used “inverse document frequency (IDF) term frequency (TF)” model and the vector space method are joined in the query generation and index creation to give multi-keyword ranked search. To get high search efficiency, a tree-type index structure, “Greedy Best-first Search” algorithm is proposed based on the tree- index.

Extraction of Concept and Concept Relation for Islamic Term Using Syntactic Pattern Approach

Ontology Learning is a semi automation step to learn ontology from text. The identification of a term become a prerequisite for all aspects of Ontology Learning. The Ontological Learning Layer is started by identifying terms, synonyms, concepts, hierarchical concepts, relationships and rules for various domain text including Islamic-based text or Islamic Glossary. The glossary of Islamic terms translated into English has been in abundance and requires extraction of important information for a clear understanding of an Islamic term. The existence of a list of Islamic terms is to minimize the spelling diversity, to seek the concept of the term and provide guidance for a unique Islamic concept. However, this electronic form provides a serious problem to achieve machine operation. The study aims to identify and extract concepts, taxonomies, relationships and rules that can be built on the domain of the terms in Islamic glossary specific to the field of Islamic Pillars. This extraction involves the use of Hearst pattern approach. The data set used is from the Dictionary or Islamic Glossary of the International Islamic University of Malaysia (DEED 2015). The dictionary consists of Islamic terms, which are the concepts and intentions of each concepts in alphabetical order. The study used six phases involving the preparation, processing and testing phase that are merged with the methodology design of the study. A total of 41 concepts were successfully extracted based on 6 Hearst pattern, 31 manually generated rules from 19 sentences and 9 non-taxonomic relationships. The result of the study concluded that the objective of this study was achieved in the scope determined when the results of the study and testing conducted by the assessors in the domain showed positive results. Research constraints are presented to enable researchers to improve the research from time to time. Research proposals for future research have been described so that this study will be more useful and expanded to the next comprehensive guide of Muslims.

E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation

Currently, a plethora of industrial and academic sentiment analysis methods for classifying the opinion polarity of a text are available and ready to use. However, each of those methods have their strengths and weaknesses, due mainly to the approach followed in their design (supervised/unsupervised) or the domain of text used in their development. The weaknesses are usually related to the capacity of generalisation of machine learning algorithms, and the lexical coverage of linguistic resources. Those issues are two of the main causes of one of the challenges of Sentiment Analysis, namely the domain adaptation problem. We argue that the right ensemble of a set of heterogeneous Sentiment Analysis Methods will lessen the domain adaptation problem. Thus, we propose a new methodology for optimising the contribution of a set of off-the-shelf Sentiment Analysis Methods in an ensemble classifier depending on the domain of the input text. The results clearly show that our claim holds.

“IT’S OVER 9000.” APEIRON NARRATIVE CONFIGURATIONS IN CONTEMPORARY MEDIASCAPE

Recently, a peculiar narrative configuration has developed and is spreading through the internet culture and new media. Characterised by a specific representation of the individual growth process, Apeiron narratives find their origin in pen & paper role-playing games, but it is only after the development of digital games and the diffusion of the Japanese cultural codex through the contemporary mediascape that they have become a coherent, autonomous and viral phenomenon.
 In the following pages, this narrative configuration will be described through a series of paradigmatic examples; its roots will be traced back to the peculiar traits of role-playing games, and the importance of recent digital adaptation will be highlighted. Finally, I will describe its diffusion beyond the domain of fictional text, hinting at possible environments for its diffusion.

코퍼스 기반 프랑스어 텍스트 정규화 평가

This paper aims to present a taxonomy of non-standard words (NSW) for developing a French text normalization system and to propose a method for evaluating this system based on a corpus. The proposed taxonomy of French NSWs consists of 13 categories, including 2 types of letter-based categories and 9 types of number-based categories. In order to evaluate the text normalization system, a representative test set including NSWs from various text domains, such as news, literature, non-fiction, social-networking services (SNSs), and transcriptions, is constructed, and an evaluation equation is proposed reflecting the distribution of the NSW categories of the target domain to which the system is applied. The error rate of the test set is 1.64%, while the error rate of the whole corpus is 2.08%, reflecting the NSW distribution in the corpus. The results show that the literature and SNS domains are assessed as having higher error rates compared to the test set.

코퍼스 기반 프랑스어 텍스트 정규화 평가

This paper aims to present a taxonomy of non-standard words (NSW) for developing a French text normalization system and to propose a method for evaluating this system based on a corpus. The proposed taxonomy of French NSWs consists of 13 categories, including 2 types of letter-based categories and 9 types of number-based categories. In order to evaluate the text normalization system, a representative test set including NSWs from various text domains, such as news, literature, non-fiction, social-networking services (SNSs), and transcriptions, is constructed, and an evaluation equation is proposed reflecting the distribution of the NSW categories of the target domain to which the system is applied. The error rate of the test set is 1.64%, while the error rate of the whole corpus is 2.08%, reflecting the NSW distribution in the corpus. The results show that the literature and SNS domains are assessed as having higher error rates compared to the test set.

Evaluation Review on Effectiveness and Security Performances of Text Steganography Technique

Steganography is one of the categories in information hiding that is implemented to conceal the hidden message to ensure it cannot be recognized by human vision. This paper focuses on steganography implementation in text domain namely text steganography. Text steganography consists of two groups, which are word-rule based and feature-based techniques. This paper analysed these two categories of text steganography based on effectiveness and security evaluation because the effectiveness is critically important in order to determine that technique has the appropriate quality. Meanwhile, the security is important due to the intensity performance in securing the hidden message. The main goal of this paper is to review the evaluation of text steganography in terms of effectiveness and security that have been developed by previous research efforts. It is anticipated that this paper will identify the performance of text steganography based on effectiveness and security measurement.

Deep Learning – Now and Next in Text Mining and Natural Language Processing

This study was conducted to find out what has not been discussed in last research in domain text mining and NLP using Deep Learning. In this literature review has covered more than 50 articles that can be accessed from a various portal of scientific articles. The focuses are conducted on important elements of a network and what influences them. The results of this study indicate that currently, more research discusses how data is presented. This is due to the assumption that input data is very important in performance of an algorithm. modification of network architecture and a combination of techniques are also attracted researchers. The next studies are many research points that can be done in text mining and NLP using Deep Learning. Especially on input features, learning methods, and the others issues in the domain of Text mining and NLP.

Detecting new Chinese words from massive domain texts with word embedding

Textual information retrieval (TIR) is based on the relationship between word units. Traditional word segmentation techniques attempt to discern the word units accurately from texts; however, they are unable to appropriately and efficiently identify all new words. Identification of new words, especially in languages such as Chinese, remains a challenge. In recent years, word embedding methods have used numerical word vectors to retain the semantic and correlated information between words in a corpus. In this article, we propose the word-embedding-based method (WEBM), a novel method that combines word embedding and frequent n-gram string mining for discovering new words from domain corpora. First, we mapped all word units in a domain corpus to a high-dimension word vector space. Second, we used a frequent n-gram word string mining method to identify a set of candidates for new words. We designed a pruning strategy based on the word vectors to quantify the possibility of a word string being a new word, thereby allowing the evaluation of candidates based on the similarity of word units in the same string. In a comparative study, our experimental results revealed that WEBM had a great advantage in detecting new words from massive Chinese corpora.

Dependency parsing with finite state transducers and compression rules

This article proposes a syntactic parsing strategy based on a dependency grammar containing formal rules and a compression technique that reduces the complexity of those rules. Compression parsing is mainly driven by the ‘single-head’ constraint of Dependency Grammar, and can be seen as an alternative method to the well-known constructive strategy. The compression algorithm simplifies the input sentence by progressively removing from it the dependent tokens as soon as binary syntactic dependencies are recognized. This strategy is thus similar to that used in deterministic dependency parsing. A compression parser was implemented and released under General Public License, as well as a cross-lingual grammar with Universal Dependencies, containing only broad-coverage rules applied to Romance languages. The system is an almost delexicalized parser which does not need training data to analyze Romance languages. The rule-based cross-lingual parser was submitted to CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. The performance of our system was compared to the other supervised systems participating in the competition, paying special attention to the parsing of different treebanks of the same language. We also trained a supervised delexicalized parser for Romance languages in order to compare it to our rule-based system. The results show that the performance of our cross-lingual method does not change across related languages and across different treebanks, while most supervised methods turn out to be very dependent on the text domain used to train the system.

Establishing User-centric Cloud Service Registries

Many potential cloud consumers are overburdened by the challenges persisting when discovering, assessing, and selecting contemporary Cloud Service offerings: the cloud market is vast and fast-moving, the selection criteria are ambiguous, service knowledge is scattered through the Internet, and features as well as prices are complex and incomparable. Much research has been carried out to create cloud service registries to help users select cloud services for eventual consumption, especially within the field of semantic web services. Through analyzing real-world requirements of six use cases we identified a gap in research for user-centric technologies. We fill this gap by creating a business vocabulary reflecting common service selection criteria, defining a textual domain specific language to let any user describe services easily, and implementing a novel brokering and matchmaking component to support users in their selection process. As a combination of those technologies, we create the Open Service Compendium (OSC), a crowd-sourced cloud service registry. Our evaluation activities highlight how these developments solve real-world challenges in diverse near-production settings. All of this implies that a substantial benefit for service registry users can be created by following a simple architecture that is focused on their concrete needs — instead of aiming for highest sophistication and broadest applicability as observed in many of the related works.

MwTExt: automatic extraction of multi-word terms to generate compound concepts within ontology

Multiword expressions are omnipresent element of natural language, whose construal as a linguistic resource has significant importance in various applications. This paper presents an architecture-MwTExt, for automatic extraction of multi-word terms-MWTs from such expressions within un-annotated English documents. Natural Language Processing techniques such as Shallow parsing and syntactic structure analysis are used to extract MWTs, with specific focus on lexical patterns as (Noun Preposition Noun), (Noun Preposition Noun + Noun) and (Noun Preposition Noun Preposition Noun). The MWTs extracted can be further used to form compound concepts within Ontology. The lexical descriptions of MWTs are encoded in Web Ontology Language OWL/XML. MwTExt has been tested on Computer Science domain texts, and the results obtained are compared with those obtained by Text2Onto, an Ontology learning tool and term extractors such as TermRaider and TerMine. The result signifies that MwTExt performs better for extraction of accurate lexicalized MWTs with average precision of 97%.

Practical privacy‐preserving deep packet inspection outsourcing

SummaryHardware‐based middleboxes are ubiquitous in computer networks, which usually incur high deployment and management expenses. A recently arising trend aims to address those problems by outsourcing the functions of traditional hardware‐based middleboxes to high volume servers in a cloud. This technology is promising but still faces a few challenges from different aspects, including privacy concerns, middlebox functionality, and performance. In this paper, we propose two practical approaches to implementing a cloud‐based DPI middlebox. The outsourced DPI middlebox performs payload inspection over encrypted traffic while preserving the privacy of both communication data and inspection rules. Our first approach employs a modified reversible sketch structure, which is used for efficient error‐free membership testing, and our second approach extends the famous AC pattern matching algorithm to the cipher text domain. We utilize unkeyed one‐way hash functions instead of complex cryptographic protocols to achieve the privacy preservation requirements. Our system supports a wide range of real‐world inspection rules. We conduct evaluations on the ClamAV rule set, and the experiment results demonstrate the effectiveness of our proposals.

Semi supervised classification of scientific and technical literature based on semi supervised hierarchical description of improved latent dirichlet allocation (LDA)

Chinese text classification problem was studied based on domain ontology graph (DOG) of semi-supervised conceptual clustering to solve the problem that English word disambiguation method cannot be applied to Chinese text classification. Structure model of domain ontology graph, text classification algorithm in HowNet dictionary and KLSeeker ontology and so on were used to realize accurate classification of Chinese text and display effectiveness of algorithm. Chinese text classification model in domain ontology graph based on conceptual clustering was developed from the angle of decreasing human participation in ontology construction as much as possible in the paper. Aimed at application domain of Chinese web text, the algorithm can generate DOG of knowledge conceptualization automatically. At the same time, document ontology graph (DocOG) was defined to represent contents of individual text document. DocOG extracting target realized text classification based on ontology by matching of single document ontology and domain ontology. Finally, example calculation analysis and actual data test set experiment were given in experimental stage. The result shows that proposed Chinese text classification method has higher classification accuracy and reflects effectiveness of design.

Natural Language Processing

Abstract Natural language processing (NLP) is a subfield of artificial intelligence that is concerned with the automatic understanding of human language by computers. NLP has seen much success in recent years due to increased computing power and the rise of deep learning, and this success has extended into the domains of biomedical and clinical text. NLP has contributed to tasks such as the discovery of drug interactions, the development of clinical decision support systems, and the facilitation of chart review. Part I of this chapter provides an introduction to NLP, some common tasks in the biomedical domain, and the methods for accomplishing these tasks. Part II gives a survey of recent applications of NLP in cardiovascular medicine.

Новый комплекс инструментов автоматической обработки текста для платформы TXM и его апробация на корпусе для анализа экстремистских текстов

TXM platform provides a wide range of corpus analysis tools including correspondence analysis, clustering, lexical table construction, and parametrized subcorpus selection. The default structural unit of analysis for TXM is a token. The only TXM extension available by default is TreeTagger which performs automated morphological analysis and lemmatization during the corpus import process. However, it is possible to supply each token with a number of features enabling a more advanced text analysis. In this work we present a number of tools developed for even a more extensive, complex and flexible corpus analysis with TXM relying both on the tools previously developed by our team and on publicly available software libraries. We focus in particular on a stemming technique that uses a word structural pattern method and on noun phrase recognition that together make it possible to perform more sophisticated and powerful queries and analyses of the corpus not limited to word forms. The structural pattern stemming method is based on a set of specific language rules that allow separating a word stem from all affixes. The recognition of noun phrases is based on rules allowing the detection of subordination and coordination relations among nouns. These extensions result in the improvement of performance of statistical tools used by TXM, such as specificity scores and correspondence analysis. The new set of tools has been tested on a corpus including texts marked as «extremist» by experts along with «neutral» texts in similar domains. The corpus of approximately 900,000 words is divided into eight subcorpora: neutral texts oppose seven thematic subcorpora considered as extremist (namely aggressive, fascist, ideological, nationalistic, religious, separatist, and terroristic). The specificity analysis detects the words (or other structural units) that are significantly more or less frequent in a given subcorpus compared to the entire corpus. The specificity score for selected units can be compared across all the subcorpora in order to verify their difference or similarity. The correspondence analysis produces a chart where the subcorpora are represented as points in a twodimensional space based on their similarity as to the frequency of selected units. All tests demonstrated a significant difference between neutral texts, on one side, and marked, on the other. Two «extremist» subcorpora, religious and ideological, demonstrated similar results and can probably be merged. These facts encourage further research on fully automatic or computer-aided expert recognition of extremist texts.

A cognitive pragmatic approach to punctuation

A cognitive pragmatic approach to punctuation

Формування сценаріїв управлінської діяльності на основі дослідження інформаційного простору

It is considered the development of a scenario approach and the construction of an information technology, which allows in an automated mode, based on the analysis of the input documentation package, to solve the problem of the formation and ranking of scenarios of influencing the objects that correspond to the selected key concepts.A mathematical model of qualitative estimation of management activity scenarios for a subject area is worked out. To construct a scenario, an abstract automaton is proposed that describes the requirements based on the description of the analytic task. The scenarios are singled out from the semantic network. There are the vertices of concepts, and the arcs to be mutual relations, for example, «cause – consequence». A modified ant colony optimization (ACO) approach for forming scenarios is proposed. To check out the suggested technology, text domains were created for such subject areas as «Brexit», «Mass Shuting», «Catalan referendum», «Saudi Arabian purge», «Zimbabwean coup d’etat», «Syrian civil war».

Towards self-learning based hypotheses generation in biomedical text domain.

The overwhelming amount of research articles in the domain of bio-medicine might cause important connections to remain unnoticed. Literature Based Discovery is a sub-field within biomedical text mining that peruses these articles to formulate high confident hypotheses on possible connections between medical concepts. Although many alternate methodologies have been proposed over the last decade, they still suffer from scalability issues. The primary reason, apart from the dense inter-connections between biological concepts, is the absence of information on the factors that lead to the edge-formation. In this work, we formulate this problem as a collaborative filtering task and leverage a relatively new concept of word-vectors to learn and mimic the implicit edge-formation process. Along with single-class classifier, we prune the search-space of redundant and irrelevant hypotheses to increase the efficiency of the system and at the same time maintaining and in some cases even boosting the overall accuracy. We show that our proposed framework is able to prune up to 90% of the hypotheses while still retaining high recall in top-K results. This level of efficiency enables the discovery algorithm to look for higher-order hypotheses, something that was infeasible until now. Furthermore, the generic formulation allows our approach to be agile to perform both open and closed discovery. We also experimentally validate that the core data-structures upon which the system bases its decision has a high concordance with the opinion of the experts.This coupled with the ability to understand the edge formation process provides us with interpretable results without any manual intervention. The relevant JAVA codes are available at: https://github.com/vishrawas/Medline-Code_v2. Supplementary data are available at Bioinformatics online.