Collection Of Textual Data Research Articles

Beyond vector space model for hierarchical Arabic text classification: A Markov chain approach

The vector space model (VSM) is a textual representation method that is widely used in documents classification. However, it remains to be a space-challenging problem. One attempt to alleviate the space problem is by using dimensionality reduction techniques, however, such techniques have deficiencies such as losing some important information. In this paper, we propose a novel text classification method that neither uses VSM nor dimensionality reduction techniques. The proposed method is a space efficient method that utilizes the first order Markov model for hierarchical Arabic text classification. For each category and sub-category, a Markov chain model is prepared based on the neighboring characters sequences. The prepared models are then used for scoring documents for classification purposes. For evaluation, we used a hierarchical Arabic text data collection that contains 11,191 documents that belong to eight topics distributed into 3-levels. The experimental results show that the Markov chains based method significantly outperforms the baseline system that employs the latent semantic indexing (LSI) method. That is, the proposed method enhances the F1-measure by 3.47%. The novelty of this work lies on the idea of decomposing words into sequences of characters, which found to be a promising approach in terms of space and accuracy. Based on our best knowledge, this is the first attempt to conduct research for hierarchical Arabic text classification with such relatively large data collection.

Document representation techniques and their effect on the document Clustering and Classification: A Review

Text data is the most common form of storing information. When engine search an query, user obtained the large collection of text data. All this retrieve text data are not relevant to the required information. So, it needs to organise the massive amount of text data. Analysing and processing the text data is mainly considered in text mining. Text mining uses the standard data mining methods- classification and clustering. These two methods are used to arrange the documents which are usually represented by hundreds or thousands of texts (words) data. Text data in the document can be represented in various representation methods. In this paper, we have presented a study of various research paper that explore the area of text mining including different document representation methods and their impact on clustering and classification results.

EKSISTENSIALISME DAN ABSURDISME DALAM DRAMA KARYA PUTU WIJAYA

This study aims to (1) describe the existential drama Oh, Edan, and Dag Dig Dug works of Putu Wijaya which include: (a) freedom, (b) anxiety, (c) the failure, (d) the futility, (e) death, (2) describe absurdism in drama Ah, Edan, and Dag Dig Dug by Putu Wijaya which includes: (a) suicide philosophical, (b) the nature of the symbolic, and (c) of tragedy and comedy. This study focuses on (1) existentialism in the drama Oh, Edan, and Dag Dig Dug by Putu Wijaya which include: (a) freedom, (b) anxiety, (c) the failure, (d) the futility, (e) death and (2) absurdism in drama Ah, Edan, and Dag Dig Dug. This study is qualitative research, using descriptive qualitative research data collection. Descriptive method is a method which seeks to describe a phenomenon, the events that occurred at that time. In this study, descriptive method is used to describe the concept of existentialism and absurdism. This research data use existantialism theory and absurdism in playwrights by PutuWijaya. By using the technique with the data source in the form of three plays, the relevant data collection technique is a technique read. Reading the drama text data collection activities conducted by member’s attention totally focused on the object. Reading and listening is a set of techniques to obtain valid data followed by activities recorded data. This study concluded five concepts of existentialism and three concepts of absurdism. The results showed that existentialism in the drama Oh, Edan, Dag Dig Dug by Putu Wijaya, looked in terms of freedom, anxiety, failure, futility, and death. Three absurdism concepts in drama Ah, Edan, Dag Dig Dug by Putu Wijaya looked through suicide philosophical, symbolic nature, tragedy and comedy.

Annotation Approach for Document with Recommendation

An enormous number of organizations generate and share textual descriptions of their products, facilities, and activities. Such collections of textual data comprise a significant amount of controlled information, which residues buried in the unstructured text. Whereas information extraction systems simplify the extraction of structured associations, they are frequently expensive and incorrect, particularly when working on top of text that does not comprise any examples of the targeted structured data. Projected an alternative methodology that simplifies the structured metadata generation by recognizing documents that are possible to contain information of awareness and this data will be beneficial for querying the database. Moreover, we intend algorithms to extract attribute-value pairs, and similarly devise new mechanisms to map such pairs to manually created schemes. We apply clustering technique to the item content information to complement the user rating information, which improves the correctness of collaborative similarity, and solves the cold start problem.

Entity Centric Information Retrieval

In the past decade, the prosperity of the World Wide Web has led to fast explosion of information, and there is a long-standing demand on how to access such a huge volume of information effectively and efficiently. Information Retrieval (IR) aims to tackle the challenge by exploring approaches to obtain relevant information items (e.g., documents) relevant to a given information need (e.g., query) from a huge collection of textual data (e.g., the Web). Named entity (e.g., person, location, product, event, organization) is a type of term compound widely existing in textual data. Recent advances in Information Extraction make it possible to extract entities from large volume of free text efficiently, and the research community are actively exploring whether entities would contribute to the retrieval effectiveness. In this thesis, we investigate how to leverage entities to improve retrieval in several directions. We start with finding entities with certain semantic relation, which aims at retrieving entities and their associated attributes to meet user's information need directly. This is different from traditional search paradigm in which only documents are retrieved. Entity retrieval is performed by first retrieving a list of documents and then extracting entities from those documents. We propose a novel probabilistic framework which leverages supporting documents as bridge to model the relevance between query and entities and rank entities accordingly. On the other side, we also explore how to leverage entities to improve effectiveness of ad hoc document retrieval in two directions. The first direction is entity-centric query expansion. We find related entities of query, and perform query expansion using the names and relations of related entities. Significant improvements over several state-of-the-art feedback models could be observed on multiple data collections. Besides, we explore another direction: entity-centric relevance modeling. We propose a novel retrieval approach, i.e., Latent Entity Space (LES), which models the relevance by leveraging entity profiles to represent semantic content of documents and queries. Experimental results over several TREC collections show that LES is effective on capturing latent semantic content and can significantly improve the search accuracy of several state-of-the-art retrieval models for entity-bearing queries. This thesis presents a series of research efforts on entity centric information retrieval in several directions, and reveals promising potential of entities on improving the retrieval effectiveness. With the fast curation of high-quality knowledge base, more information about entities could be easily accessed and integrated into retrieval models. We hope our work could serve as guideline for future work on leveraging entities to improve information retrieval in more applications.

Analytic Filmmaking and the Persistence of Narrative: A Response to Roy Germano

While there is much to appreciate in Roy Germano's essay, I argue that the analytic filmmaking project as currently presented has flaws that need to be addressed before we can proceed with this approach. First, Germano underemphasizes many of the similarities among visual, audio, and textual data collection and analysis while at the same time failing to note important ways in which the two differ. Second, the discussion of documentary filmmaking focuses on a particular style of documentary and neglects to consider documentaries that are similar in their construction to the type of analytic filmmaking he argues for in the article. This emphasis on the differences creates needless and potentially problematic schisms between two groups of empirical researchers who have a great deal in common, in terms of interests and methods. Finally, the theoretical framework and the connection between theory and data need to be presented much more explicitly so that the viewer can follow the argument of the film.

Feasibility, acceptability and potential effectiveness of a mobile health (mHealth) weight management programme for New Zealand adults.

BackgroundMobile health (mHealth) behaviour change programmes use mobile phones and the internet to deliver health information and behaviour change support to participants. Such programmes offer a potentially cost-effective way to reach many individuals who do not currently access weight loss services. We developed a mHealth weight management programme using proven face-to-face behaviour change techniques and incorporating target population input. Our aim was to evaluate the feasibility, acceptability and potential effectiveness of this programme for ethnically diverse adults with a view to informing a larger trial.ResultsFifty three adults who had a BMI of ≥25 kg/m2 and wanted to lose weight (81% female, mean age 42 years, mean BMI 35.7 kg/m2, 26% Maori, 34% Pacific) received the eight-week mHealth weight loss programme. Anthropometric measures were taken at two face-to-face assessments at baseline and 12-weeks (i.e. four weeks after cessation of intervention).Twelve-week follow-up measurements were available for 36/53 participants (68%). Non-completers were younger and more likely to be male and of Pacific ethnicity. Thirty five participants (66%) reported reading ‘all or most’ text messages sent and 96% responded to at least one text data collection question over the eight-week active intervention period. Eighty one per cent of participants logged in to the study website at least once during the eight-week study period. In the intention-to-treat analysis, mean weight change was -1.0 kg (SD 3.1) at 12 weeks (p = 0.024) and change in BMI was -0.34 kg/m2 (SD 1.1) (p = 0.026). In the completers only analysis (n = 36), mean weight change was -1.4 kg (SD 3.6) (p = 0.023) and change in BMI was -0.50 kg/m2 (SD 1.3) (p = 0.025).ConclusionsA mHealth weight management programme is feasible to deliver to an ethnically diverse population. Changes in body weight and BMI at 12 weeks indicate that the programme could be effective in supporting people with weight loss. However, the high dropout rate indicates a need for further improvements to the programme.Trial registrationACTRN12612000850875

Facilitating Document Annotation Using Content and Querying Value

A large number of organizations today generate and share textual descriptions of their products, services, and actions. Such collections of textual data contain significant amount of structured information, which remains buried in the unstructured text. While information extraction algorithms facilitate the extraction of structured relations, they are often expensive and inaccurate, especially when operating on top of text that does not contain any instances of the targeted structured information. We present a novel alternative approach that facilitates the generation of the structured metadata by identifying documents that are likely to contain information of interest and this information is going to be subsequently useful for querying the database. Our approach relies on the idea that humans are more likely to add the necessary metadata during creation time, if prompted by the interface; or that it is much easier for humans (and/or algorithms) to identify the metadata when such information actually exists in the document, instead of naively prompting users to fill in forms with information that is not available in the document. As a major contribution of this paper, we present algorithms that identify structured attributes that are likely to appear within the document, by jointly utilizing the content of the text and the query workload. Our experimental evaluation shows that our approach generates superior results compared to approaches that rely only on the textual content or only on the query workload, to identify attributes of interest.

Personal Food Systems of Male Collegiate Football Players: A Grounded Theory Investigation

Factors that affect food choices include the physical and social environments, quality, quantity, perceived healthfulness, and convenience. The personal food choice process was defined as the procedures used by athletes for making food choices, including the weighing and balancing of activities of daily life, physical well-being, convenience, monetary resources, and social relationships. To develop a theoretical model explaining the personal food choice processes of collegiate football players. Qualitative study. National Collegiate Athletic Association Division II football program. Fifteen football players were purposefully sampled to represent various positions, years of athletic eligibility, and ethnic backgrounds. For text data collection, we used predetermined, open-ended questions. Data were analyzed using the constant comparison method. The athletes' words were used to label and describe their interactions and experiences with the food choice process. Member checks and an external audit were conducted by a qualitative methodologist and a nutrition specialist, and the findings were triangulated with the current literature to ensure trustworthiness of the text data. Time was the core category and yielded a cyclic graphic of a theoretical model for the food choice system. Planning hydration, macronutrient strategies, snacks, and healthful food choices emerged as themes. The athletes planned meals and snacks around their academic and athletic schedules while attempting to consume foods identified as healthful. Healthful foods were generally lower in fat but high in preferred macronutrients. High-protein foods were the players' primary goal; carbohydrate consumption was secondary. The athletes had established plans to maintain hydration. Professionals may use these findings to implement educational programs on food choices for football players.

Enhanced semantic expansion for question classification

Most question and answering systems are based on three research themes: question classification and analysis, document retrieval and answer extraction. The performance in every stage affects the final result. To respond correctly to a question given a large collection of textual data is not an easy task. There is a need to perceive and recognise the question at a level that permits to detect some constraints that the question imposes on possible answers. The classification of questions appears as an important task because it deduces the type of expected answers. The purpose is to provide additional information to reduce the gap between answer and question. A method to improve the performance of question classification focusing on linguistic analysis and statistical approaches is presented. This work also proposes two methods of questions expansion. Various questions representation, term weighting and diverse machine learning algorithms are studied. Experiments conducted on actual data are presented. Of interest is the improvement in the precision on the classification of questions.

Kernel-based machine learning for fast text mining in R

Recent advances in the field of kernel-based machine learning methods allow fast processing of text using string kernels utilizing suffix arrays. kernlab provides both kernel methods’ infrastructure and a large collection of already implemented algorithms and includes an implementation of suffix-array-based string kernels. Along with the use of the text mining infrastructure provided by tm these packages provide R with functionality in processing, visualizing and grouping large collections of text data using kernel methods. The emphasis is on the performance of various types of string kernels at these tasks.

Learning question classifiers: the role of semantic information

To respond correctly to a free form factual question given a large collection of text data, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. This work presents a machine learning approach to question classification. Guided by a layered semantic hierarchy of answer types, we develop a hierarchical classifier that classifies questions into fine-grained classes. This work also performs a systematic study of the use of semantic information sources in natural language classification tasks. It is shown that, in the context of question classification, augmenting the input of the classifier with appropriate semantic category information results in significant improvements to classification accuracy. We show accurate results on a large collection of free-form questions used in TREC 10 and 11.

Efficient single‐pass index construction for text databases

AbstractEfficient construction of inverted indexes is essential to provision of search over large collections of text data. In this article, we review the principal approaches to inversion, analyze their theoretical cost, and present experimental results. We identify the drawbacks of existing inversion approaches and propose a single‐pass inversion method that, in contrast to previous approaches, does not require the complete vocabulary of the indexed collection in main memory, can operate within limited resources, and does not sacrifice speed with high temporary storage requirements. We show that the performance of the single‐pass approach can be improved by constructing inverted files in segments, reducing the cost of disk accesses during inversion of large volumes of data.

Strategies for the Prevention, Detection, and Correction of Measurement Error in Data Collected from Textual Sources

This article considers the problem of measurement error in data collected from textual sources (for example, newspapers, police records, medical records). The article discusses several levels in the strategy of both detection and correction of errors, on-line and off-line, during and after data collection. It introduces social scientists to useful concepts and tools such as performance-shaping factors and acceptance sampling, which have been developed in the field of engineering. It proposes the use of semantic text grammars in lieu of traditional content analysis schemes for the collection of textual data. Linguistics-based coding schemes yield much richer data. The article shows that these data are also more reliable, because they retain the semantic coherence of the original text.

A Numerically Coded File of Operative Procedures Derived from a Free Text Data Collection System : A Measure of the Accuracy

A free text data collection system has been developed at the University of Illinois utilizing single word, syntax free dictionary lookup to process data for retrieval. The source document for the system is the Surgical Pathology Request and Report form. To date 12,653 documents have been entered into the system.The free text data was used to create an IRS (Information Retrieval System) database. A program to interrogate this database has been developed to numerically coded operative procedures. A total of 16,519 procedures records were generated. One and nine tenths percent of the procedures could not be fitted into any procedures category; 6.1% could not be specifically coded, while 92% were coded into specific categories. A system of PL/1 programs has been developed to facilitate manual editing of these records, which can be performed in a reasonable length of time (1 week). This manual check reveals that these 92% were coded with precision = 0.931 and recall = 0.924. Correction of the readily correctable errors could improve these figures to precision = 0.977 and recall = 0.987. Syntax errors were relatively unimportant in the overall coding process, but did introduce significant error in some categories, such as when right-left-bilateral distinction was attempted.The coded file that has been constructed will be used as an input file to a gynecological disease/PAP smear correlation system. The outputs of this system will include retrospective information on the natural history of selected diseases and a patient log providing information to the clinician on patient follow-up.Thus a free text data collection system can be utilized to produce numerically coded files of reasonable accuracy. Further, these files can be used as a source of useful information both for the clinician and for the medical researcher.