2986415
An Expert Lexicon Approach to Identifying English Phrasal Verbs
Phrasal Verbs are an important feature of the English language. Properly identifying them provides the basis for an English parser to decode the related structures. Phrasal verbs have been a challenge to Natural Language Processing (NLP) because they sit at the borderline between lexicon and syntax. Traditional NLP frameworks that separate the lexicon module from the parser make it difficult to handle this problem properly. This paper presents a finite state approach that integrates a phrasal verb expert lexicon between shallow parsing and deep parsing to handle morpho-syntactic interaction. With precision/recall combined performance benchmarked consistently at 95.8%-97.5%, the Phrasal Verb identification problem has basically been solved with the presented method.
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.
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.
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.
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.
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.
37514455
An Algorithm for Determining the Controllers of Supervised Entities at the First Level: Brazilian Central Bank Case Study
To find controllers from the stock shares of a financial institution is an important task in the Brazilian Central Bank (BCB). The original problem is typically called Sum of Subset and suggested to be resolved by backtrack algorithm with NP-complete complexity. Currently BCB solves this problem manually which is time consuming and suffered from errors. In this study, an approximation algorithm with polynomial complexity O(n) for determining controllers was developed and implemented in BCB. This algorithm includes subroutines for determining controller at levels 1, 2, 3 and the main loops of combination. This paper describes the basic concepts and business rules currently employed in BCB, complexity analysis of the algorithm and its step by step applications at the first level. The implementation and experiment results indicate the feasibility of automation of controllers finding process. Though developed for BCB, it works equally well for all financial institutions which are controlled by stockholders in Brazil.
17819380
Feature Selection for Complex Patterns
Feature selection is an important data preprocessing step in data mining and pattern recognition. Many algorithms have been proposed in the past for simple patterns that can be characterised by a single feature vector. Unfortunately, these algorithms are hardly applicable to what are referred as complex patterns that have to be described by a finite set of feature vectors. This paper addresses the problem of feature selection for the complex patterns. First, we formulated the calculation of mutual information for complex patterns based on Gaussian mixture model. A hybrid feature selection algorithm is then proposed based on the formulated mutual information calculation (filter) and Baysian classification (wrapper). Experimental results on XM2VTS speaker recognition database have not only verified the performance of the proposed algorithm, but also demonstrated that traditional feature selection algorithms designed for simple patterns would perform poorly for complex patterns.
20117363
Performance analysis of a channel allocation scheme with preemptive priority for integrated voice/data mobile networks
This paper presents a new channel allocation scheme with preemptive priority for the integrated voice/data mobile network. Based on the scheme, a two-dimensional Markov process is built, and the steady-state probability is solved by the matrix-analytic method. The preemptive priority mechanism introduced gives more benefit to voice traffic at the expense of degrading some data traffic, while the victim buffer compensates the degradation and has no negative effect on voice traffic. From the analysis and numerical results, we can conclude that our scheme can guarantee different QoS requirements from different traffic classes, and significantly improve the channel utilization.
36676803
A Hypergraph Separator Based Variable Ordering Heuristic for Solving Real World SAT
Problem structure has been used to guide the variable ordering heuristic in backtracking search since [1]. Recent experimental results show that guiding the variable ordering heuristic using tree decompositions can improve the performance of SAT and CSP solvers. Huang and Darwiche's [2] variable ordering heuristic uses a Dtree, a static binary tree decomposition, to compute the variable group ordering. Since the Dtree has to be constructed before search, the pre-established variable groupings never change during the execution of the solving. Boolean Constraint Propagation (BCP) is the look-ahead strategy for all of the cutting-edge SAT solvers. Because long implication chains exist in real world instances and a large number of variables on the implication chains can be instantiated after making a relatively small number of decisions, the internal structure of real world instances often changes dramatically in different parts of the search tree.
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.