13958484
A case restoration approach to named entity tagging in degraded documents
This paper describes a novel approach to namedentity (NE) tagging on degraded documents. NE taggingis the process of identifying salient text strings inunstructured text, corresponding to names of people,places, organizations, times/dates, etc. Although NEtagging is typically part of a larger informationextraction process, it has other applications, such asimproving search in an information retrieval system, andpost-processing the results of an OCR system. We focuson degraded documents, i.e. case insensitive documentsthat lack orthographic information. Examples includeoutput of speech recognition systems, as well as e-mail.The traditional approach involves retraining an NEtagger on degraded text, a cumbersome operation. Thispaper describes an approach whereby text is first"restored" to its implicit case sensitive form, andsubsequently processed by the original NE tagger.Results show that this new approach leads to far lessprecision loss in NE tagging of degraded documents.
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.
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.
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.
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.
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.
524613
Locating Compromised Sensor Nodes Through Incremental Hashing Authentication
While sensor networks have recently emerged as a promising computing model, they are vulnerable to various node compromising attacks. In this paper, we propose COOL, a COmpromised nOde Locating protocol for detecting and locating compromised nodes once they misbehave in the sensor network. We exploit a proven collision-resilient incremental hashing algorithm and design secure steps to confidently locate compromised nodes. The scheme can also be combined with existing en-route false report filtering schemes to achieve both early false report dropping and accurate compromised nodes isolation.
17487983
A novel network allocation scheme in wireless mobile networks
A novel network allocation scheme of wireless mobile communications networks is investigated in this paper. We study the handoff rates of calls from one cell to another and obtain the blocking probabilities for new calls and handoff calls. Actual call connection time (ACCT) of a call is investigated, which is the total time a mobile user engaged in communications over a wireless network during a call connection and can be used to design appropriate charging schemes. Our numerical results show that how these measures depend on the new call arrival rate with specific reserved channels for handoff calls in each cell. These results are expected to be useful for the cost analysis for updating location and paging in wireless mobile network.
13091278
Dynamics of local search heuristics for the traveling salesman problem
This paper experimentally investigates the dynamical behavior of a search process in a local heuristic search system for a combinatorial optimization problem. Or-opt heuristic algorithm is chosen as the study subject, and the well-known traveling salesman problem (TSP) serves as a problem testbed. This study constructs the search trajectory by using the time-delay method, evaluates the dynamics of the local search system by estimating the correlation dimension for the search trajectory, and illustrates the transition of the local search process from high-dimensional stochastic to low dimensional chaotic behavior.
9445959
Guiding real-world SAT solving with dynamic hypergraph separator decomposition
The general solution of satisfiability problems is NP-complete. Although state-of-the-art SAT solvers can efficiently obtain the solutions of many real-world instances, there are still a large number of real-world SAT families which cannot be solved in reasonable time. Much effort has been spent to take advantage of the internal structure of SAT instances. Existing decomposition techniques are based on preprocessing the static structure of the original problem. We present a dynamic decomposition method based on hypergraph separators. Integrating the separator decomposition into the variable ordering of a modern SAT solver leads to speedups on large real-world satisfiability problems. Compared with a static decomposition based variable ordering, such as Dtree (Huang and Darwiche, 2003), our approach does not need time to construct the full tree decomposition, which sometimes needs more time than the solving process itself. Our primary focus is to achieve speedups on large real-world satisfiability problems. Our results show that the new solver often outperforms both regular zChaff and zChaff integrated with Dtree decomposition. The dynamic separator decomposition shows promise in that it significantly decreases the number of decisions for some real-world problems.
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.