450045
A Bootstrapping Approach to Named Entity Classification Using Successive Learners
This paper presents a new bootstrapping approach to named entity (NE) classification. This approach only requires a few common noun/pronoun seeds that correspond to the concept for the target NE type, e.g. he/she/man/woman for PERSON NE. The entire bootstrapping procedure is implemented as training two successive learners: (i) a decision list is used to learn the parsing-based high precision NE rules; (ii) a Hidden Markov Model is then trained to learn string sequence-based NE patterns. The second learner uses the training corpus automatically tagged by the first learner. The resulting NE system approaches supervised NE performance for some NE types. The system also demonstrates intuitive support for tagging user-defined NE types. The differences of this approach from the co-training-based NE bootstrapping are also discussed.
7870275
Information Extraction Supported Question Answering
Abstract : This paper discusses the use of our information extraction (IE) system, Textract, in the question-answering (QA) track of the recently held TREC-8 tests. One of our major objectives is to examine how IE can help IR (Information Retrieval) in applications like QA. Our study shows: (1) IE can provide solid support for QA; (2) low-level IE like Named Entity tagging is often a necessary component in handling most types of questions; (3) a robust natural language shallow parser provides a structural basis for handling questions; (4) high-level domain independent IE, i.e. extraction of multiple relationships and general events, is expected to bring about a breakthrough in QA.
8155707
A Question Answering System Supported by Information Extraction
This paper discusses an information extraction (IE) system, Textract, in natural language (NL) question answering (QA) and examines the role of IE in QA application. It shows: (i) Named Entity tagging is an important component for QA, (ii) an NL shallow parser provides a structural basis for questions, and (iii) high-level domain independent IE can result in a QA breakthrough.
9473484
Bootstrapping for Named Entity Tagging Using Concept-based Seeds
A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. The bootstrapping procedure is implemented as training two successive learners. First, decision list is used to learn the parsing-based NE rules. Then, a Hidden Markov Model is trained on a corpus automatically tagged by the first learner. The resulting NE system approaches supervised NE performance for some NE types.
2713110
Orthographic case restoration using supervised learning without manual annotation
One challenge in text processing is the treatment of case insensitive documents such as speech recognition results. The traditional approach is to re-train a language model excluding case-related features. This paper presents an alternative two-step approach whereby a preprocessing module (Step 1) is designed to restore case-sensitive form which is subsequently processed by the original system (Step 2). Step 1 is mainly implemented as a Hidden Markov Model trained on a large raw corpus of case sensitive documents. It is demonstrated that this approach (i) outperforms the feature exclusion approach for named entity tagging, (ii) leads to limited degradation for parsing, relationship extraction and case insensitive question answering, (iii) reduces system complexity, and (iv) has wide applicability: the restored text can be used in both statistical model and rule-based systems.
31100803
Web Search Based on Ant Behavior: Approach and Implementation in Case of Interlegis
The World Wide Web today is considered the biggest information and knowledge source in every field. These Web pages are accessed daily by a great quantity of people with different interests. However, they are heterogeneously constructed both in available information and quality and majorities are presented in HTML language. This manner limits and makes it difficult to extract useful information and knowledge using simple search by Internet. The process of surfing the Web is similar to ant colonies foraging (Beckers, Deneubourg & AbstrAct
6293846
Cryptographic Key Generation from Biometric Data Using Lattice Mapping
Crypto-biometric systems are recently emerging as an effective process of key management to address the security weakness of conventional key release systems using pass-codes, tokens or pattern recognition based biometrics. This paper presents a lattice mapping based fuzzy commitment method for cryptographic key generation from biometric data. The proposed method not only outputs high entropy keys, but also conceals the original biometric data such that it is impossible to recover the biometric data even when the stored information in the system is open to an attacker. Simulated results have demonstrated that its authentication accuracy is comparable to the well-known k-nearest neighbour classification
14087384
Rapid development of Hindi named entity recognition using conditional random fields and feature induction
This paper describes our application of conditional random fields with feature induction to a Hindi named entity recognition task. With only five days development time and little knowledge of this language, we automatically discover relevant features by providing a large array of lexical tests and using feature induction to automatically construct the features that most increase conditional likelihood. In an effort to reduce overfitting, we use a combination of a Gaussian prior and early stopping based on the results of 10-fold cross validation.
15110406
Grid Service Agents for Real Time Traffic Synchronization
Grid Service Agents for Real Time Traffic Synchronization is proposed in this research. The paper presents Air Traffic Flow Management (ATFM) problem and its synchronization property. For such a complex problem, using grid computing with multi-agent coordination and negotiation techniques to improve ATFM computational efficiency is the main objective of actual even further research. To demonstrate the developed model - Air Traffic Flow Management in Grid Computing (ATFMGC), the grid architecture, the basic components and relationships among them are described. At the same time, the function of agents, their knowledge representation and inference processes are also discussed. As an example, a tactical planning case study related with some Brazilian airports is illustrated.
18531635
A generalized model for network survivability
The high expectation of network to be available and perform at all time has created growing concerns between network operators and engineers all over the globe. Network should be available to users whenever they want to use them. In the wake of an attack, network should be also able to recover as soon as possible. A survivable network is one that can provide services in the presence of an attack or failure and be able to recover fully in a timely manner. Designing a model for network survivability basically involves the understanding of three key issues, i.e, how the system will function in the wake of failures, what will be the impact of failures on the user, and how to overcome these failures. To achieve these goals, this paper proposes a quantitative approach to evaluate the network survivability in terms of using a generalized model; we consider a more complex system with several faults to study the survivability of a network using Markov chain. In presenting a better survivability performance, some numerical comparisons for the network availability, failure frequency and survivability is also developed.