Information In Natural Language Text Research Articles

A modified approach to data hiding in Microsoft Word documents by change-tracking technique

Steganography is a hidden way of communication, where one person communicates with the other by using some medium under the garb of a cover, without any suspicion created on the part of any unauthorized intermediary about the existence of the secret message. Linguistic steganography is about hiding information in natural language text. Synonym substitution is an important transformation used in linguistic steganography. In this paper, we are going to show a method of redesigning the change tracking technique for hiding the secret message in Microsoft Word document to increase the embedding capacity and to decrease the suspicion of any intermediary about the message’s existence. The secret message in bit form is concealed by performing linguistic steganography using synonym substitution method. In conventional change tracking technique for information hiding in Microsoft Word document, the generated Huffman’s codes of rare words are lengthier as compared to common words, leading to more suspicion in the stego-document due to the usage of rare words as compared to common words as synonyms. To overcome this problem, the proposed algorithm is designed in such a way that familiar synonyms are used more often to hide more bits and avoid suspicions.

Sampling strategies for information extraction over the deep web

Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections—and in which order—to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deep-web text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.

Practical Linguistic Steganography using Contextual Synonym Substitution and a Novel Vertex Coding Method

Linguistic steganography is concerned with hiding information in natural language text. One of the major transformations used in linguistic steganography is synonym substitution. However, few existing studies have studied the practical application of this approach. In this article we propose two improvements to the use of synonym substitution for encoding hidden bits of information. First, we use the Google n-gram corpus for checking the applicability of a synonym in context, and we evaluate this method using data from the SemEval lexical substitution task and human annotated data. Second, we address the problem that arises from words with more than one sense, which creates a potential ambiguity in terms of which bits are represented by a particular word. We develop a novel method in which words are the vertices in a graph, synonyms are linked by edges, and the bits assigned to a word are determined by a vertex coding algorithm. This method ensures that each word represents a unique sequence of bits, without cutting out large numbers of synonyms, and thus maintains a reasonable embedding capacity.

When speed has a price

A wealth of information produced by individuals and organizations is expressed in natural language text. This is a problem since text lacks the explicit structure that is necessary to support rich querying and analysis. Information extraction systems are sophisticated software tools to discover structured information in natural language text. Unfortunately, information extraction is a challenging and time-consuming task. In this paper, we address the limitations of state-of-the-art systems for the optimization of information extraction programs, with the objective of producing efficient extraction executions. Our solution relies on exploiting a wide range of optimization opportunities. For efficiency, we consider a wide spectrum of execution plans, including approximate plans whose results differ in their precision and recall. Our optimizer accounts for these characteristics of the competing execution plans, and uses accurate predictors of their extraction time, recall, and precision. We demonstrate the efficiency and effectiveness of our optimizer through a large-scale experimental evaluation over real-world datasets and multiple extraction tasks and approaches.

A Steganography on Synonym Frequency Distribution

Linguistic steganography is concerned with hiding secret information in natural language text imperceptibly. One of the major methods for doing so is synonym substitution. Existing schemes hardly consider the influence on word frequency so that they can’t resist the text steganalysis which bases on synonym frequency. This study takes the features of word frequency into account, and proposes a novel method using a combination of synonym substitution and multiple-base. This method mainly consists of three steps. Firstly, synonym database is to be preprocessed according to synonym frequency. Then, the secret data will be transformed into the expression of multiple-base expressions. Finally, the chosen synonym will be used to hide the secret message according to its continuous dimension and the proposed algorithm. In this paper, continuous dimension is a new concept to be proposed, and it is important to the embedding and extracting processes. The proposed method possesses superiority to other related methods in terms of security and robustness. Moreover, the most important goal in this method is trying to remain the synonym frequency distribution and achieve good imperceptibility. This paper describes in detail the steganography algorithm that involves the embedding and extracting processes, and presents its advantages and limitations.

Modified Linguistic Steganography Approach by Using Syntax Bank and Digital Signature

  Abstract—In today digital age, there are more demands to improve techniques for information security. Steganography is one of the popular areas in information protection in order to establish communication between two parties whose existence is unknown to a possible attacker. Among the variety of steganographic methods, linguistic approach is concerned with hiding information in natural language text. Here, our proposed system uses syntax transformations to hide the intended secret message into the cover text. The transformation bases on a syntax bank that consists of a number of syntax sets. The syntax set is a set of all available syntax forms of input sentence. Moreover, our system utilizes shannon-fano compression algorithm, semi-random number assignment, and SHA-512 hash algorithm and digital signature algorithm (DSA) based digital signature to support the capacity, robustness, and innocent-looking capabilities.

Temporal Reasoning in Natural Language Processing: A Survey

Reasoning with temporal information in natural language text has attracted great attention due to its potential applications in summarization, question answering and other tasks. For example, the chronological ordering of events described in a text is important for presenting the information in the summary. Linking information in a natural language text with temporal relations is essential in question answering system to address time sensitive and dynamic world. A crucial first step towards the computational treatment of the temporal information in these applications is the automatic extraction of events described in the text and identification of temporal relations to link these events. Much of the work done in this direction can be classified as -Annotation schemes for identification of events and time implicit in the text, linking the events using temporal relations and Temporal reasoning for solving practical applications. The present paper is a survey of various proposals to address these issues. Various annotation schemas developed to represent temporal information in natural language text. A discussion of the frameworks for temporal reasoning and tractable classes is described. The usefulness of these models to applications such as summarization and question answering are also presented.

Introduction to Special Issue on Advances in Question Answering

This special issue on advanced topics in question answering is devoted to two critically important areas of annotation and reasoning in support of this task: recognizing opinions, speculations, and emotions in text; and identifying temporally sensitive expressions in text. This issue contains four articles addressing “these” two problems: Annotating Expressions of Opinions and Emotions in Language , by Janyce Wiebe, Theresa Wilson, and Claire Cardie; Temporal and Event Information in Natural Language Text, by James Pustejovsky, Robert Knippen, Jessica Littman, and Roser Sauri; Temporal Closure in an Annotation Environment, by Marc Verhagen; and The Role of Inference in the Temporal annotation and Analysis of Text, by Andrea Setzer, Robert Gaizauskas, and Mark Hepple. In this introduction, we situate this work in the larger context of question answering systems, and describe briefly the content of each article. Research in the area of question answering has recently become one of the fastest growing and most challenging topics in computational linguistics and information access. It has been the topic of numerous workshops, seminars, and conferences over the past few years. Some of the recent activities in question answering include a TREC QA track, two phases of ARDA-funded research into question answering, i.e., AQUAINT, and three NRRC summer workshops in 2002 and 2003. It is the results of these latter workshops that are reported on, in part, in this special issue. These workshops focused on the temporal and multiple perspective aspects of question answering, and the difficulties of annotating complex relational information in natural language texts.