8772483
Location Normalization for Information Extraction
Ambiguity is very high for location names. For example, there are 23 cities named 'Buffalo' in the U.S. Country names such as 'Canada', 'Brazil' and 'China' are also city names in the USA. Almost every city has a Main Street or Broadway. Such ambiguity needs to be handled before we can refer to location names for visualization of related extracted events. This paper presents a hybrid approach for location normalization which combines (i) lexical grammar driven by local context constraints, (ii) graph search for maximum spanning tree and (iii) integration of semi-automatically derived default senses. The focus is on resolving ambiguities for the following types of location names: island, town, city, province, and country. The results are promising with 93.8% accuracy on our test collections.
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.
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.
14663700
A scan BIST generation method using a markov source and partial bit-fixing
Recently, Markov sources were shown to be effective in designing pseudo-random test pattern generators with low area overhead for built-in self-test of scan designs. This paper presents a new test pattern generation scheme based on a Markov source and a partial bit-fixing technique. A new method is proposed for the computation of the state transition probabilities of the Markov source based on the statistics of a deterministic test set. This is enhanced by partial bit-fixing logic, which fixes a group of consecutive inputs to all-0 or all-1. Experimental results show that the proposed BIST scheme can achieve 100% fault coverage for large benchmark circuits with reduced hardware overhead and reduced pattern counts compared to the earlier method using Markov sources.
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.
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.
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.
8035776
Semantic Integration in Heterogeneous Databases Using Neural Networks
One important step in integrating heterogeneous databases is matching equivalent attributes: Determining which fields in two databases refer to the same data. The meaning of information may be embodied within a. database model, a conceptual schema, application programs, or data contents. Integration involves extracting semantics, expressing them as metadata, and matching semantically equivalent data elements. We present a procedure using a classifier to categorize attributes according to their field specifications and data values, then train a neural network to recognize similar attributes. In our technique, the knowledge of how to match equivalent data elements is “discovered” from metadata , not “pre-programmed”.
6504334
Modeling and analysis of hierarchical cellular networks with bidirectional overflow and take-back strategies under generally distributed cell residence times
The rapid growth of wireless services and mobile users drives a great interest in cellular networks with a hierarchical structure. Hierarchical cellular networks (HCNs) can provide high system capacity, efficient channel utilization and inherent load-balancing capability. In this paper, we develop an analytical model and a performance analysis method for a two-layer HCN with bidirectional overflow and take-back strategies. Mobile users are divided into two classes. The call requests (including new and handoff calls) of fast and slow users are preferably assigned to the macrolayer and microlayer, respectively. A call from a fast user or slow user can overflow to its non-preferable layer if there is no channel available. The successful overflow call can be taken back to its preferable layer if a channel becomes available. Since the commonly used exponentially distributed assumption for cell residence time and then the channel occupancy time does not hold for emerging mobile networks, we model various cell residence times by general distributions to adapt to more flexible mobility environments. The channel occupancy times are derived in terms of the Laplace transforms of various cell residence times. The handoff rates, overflow rates and take-back rates of each layer are also derived in terms of the new call arrival rates and related probabilities. The stationary probabilities (and then the performance measures) are determined on the basis of the theory of multi-dimensional loss systems.
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
16138674
Mesh editing with an embedded network of curves
We propose a new topological data structure for representing a set of polygonal curves embedded in a meshed surface. In this embedding, the vertices of the curve do not necessarily correspond to the vertices of the surface. The partition of the surface yielded by the intersecting curves is efficiently represented as a "cut-graph". The cut-graph stores combinatorial information of the network of curves. In our approach, the combinatorial form of information is systematically preferred to geometrical information since it improves both robustness and efficiency. Thanks to the topological data structure and algorithms, the cut-graph can be sketched through iterations of designing and erasing curves on the mesh surface in a "nondestructive" way, i.e. without modifying the mesh until the cutting operation is committed. We also demonstrate several prototype curve design tools inspired by 2D vector and bitmap graphics paradigms. We show how to sketch the cut-graph and how these tools can be mixedly used.