Pattern matching : a sheaf-theoretic approach

A general theory of pattern matching is presented by adopting an extensional, geometric view of patterns. The extension of the matching relation consists of the occurrences of all possible patterns in a particular target. The geometry of the pattern describes the structure of the pattern and the spatial relationships among parts of the pattern. The extension and the geometry, when combined, produce a structure called a sheaf. Sheaf theory is a well developed branch of mathematics which studies the global consequences of locally defined properties. For pattern matching, an occurrence of a pattern, a global property of the pattern, is obtained by gluing together occurrences of parts of the pattern, which are locally defined properties.A sheaf-theoretic view of pattern rnatching provides a uniforrn treatrnent of pattern matching on any kind of data structure-strings, trees, graphs, hypergraphs, and so on. Such a parametric description is achieved by using the language of category theory, a highly abstract description of commonly occurring structures and relationships in mathematics.A generalized version of the Knuth-Morris-Pratt pattern matching algorithm is derived by gradually converting the extensional description of pattern rnatching as a sheaf into an intensional description. The algorithm results from a synergy of four very general program synthesis/transformation techniques: (1) Divide and conquer: exploit the sheaf condition; assemble a full match by gluing together partial matches; (2) Finite differencing: collect and update partial matches incrementally while traversing the target; (3) Backtracking: instead of saving all partial matches, save just one; when this partial match cannot be extended, fail back to another; (4) Partial evaluation: precompute pattern-based (and therefore constant) computations.The derivation is carried out in a general frarnework using Grothendieck topologies. By appropriately instantiating the underlying data structures and topologies, the sarne scheme results in matching algorithms for patterns with variables and with multiple patterns. Slight variations of the derivation result in Earley's algorithm for context-free parsing, and Waltz filtering, a relaxation algorithm for providing 3-D interpretations to 2-D irnages.Other applications of a geometric view of patterns are briefly considered: rewrites, parallel algorithms, induction and computability

A hybrid architecture for robust parsing of german

This paper provides an overview of current research on a hybrid and robust parsing architecture for the morphological, syntactic and semantic annotation of German text corpora. The novel contribution of this research lies not in the individual parsing modules, each of which relies on state-of-the-art algorithms and techniques. Rather what is new about the present approach is the combination of these modules into a single architecture. This combination provides a means to significantly optimize the performance of each component, resulting in an increased accuracy of annotation

A Large-scale Distributed Video Parsing and Evaluation Platform

Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world. However, existing systems for video analysis still lack the ability to handle the problems of scalability, expansibility and error-prone, though great advances have been achieved in a number of visual recognition tasks and surveillance applications, e.g., pedestrian/vehicle detection, people/vehicle counting. Moreover, few algorithms explore the specific values/characteristics in large-scale surveillance videos. To address these problems in large-scale video analysis, we develop a scalable video parsing and evaluation platform through combining some advanced techniques for Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed Filesystem (HDFS). Also, a Web User Interface is designed in the system, to collect users' degrees of satisfaction on the recognition tasks so as to evaluate the performance of the whole system. Furthermore, the highly extensible platform running on the long-term surveillance videos makes it possible to develop more intelligent incremental algorithms to enhance the performance of various visual recognition tasks.Comment: Accepted by Chinese Conference on Intelligent Visual Surveillance 201

A Semantic Approach for Keyword Search on Relational Databases

Today’s search engines make it easier for the user to browse and query the online available data. But when it comes to structured data, the queries have to be structured too, in order to retrieve the data. This makes it difficult for novice users, with no knowledge of the underlying schema or query language, to access the relational data. Therefore, to query the structured data in an unstructured language of web, there is a need to map the user keyword queries to their equivalent SQL format. This research is intended to bridge the gap by introducing a framework named STRUCT. Unlike most of the existing work which pays very little attention to the contextual information provided by the user, our approach takes these details into account to elucidate the implied structural information necessary for constructing the SQL clauses. One fundamental issue on keyword search in traditional databases is how to interpret users’ information needs behind keywords they provided. A common approach of many prototype systems is to make such interpretation as a designer’s choice (such as imposing AND or OR semantics, or a combination), leaving no choice to users. A much more meaningful approach would be allowing users themselves to specify the required semantics through contextual information. So can we build a system which stays with the simplicity of Keyword search, yet can incorporate the contextual information provided in the user query? STRUCT answers this question by taking English language queries involving intended keywords. Instead of resorting on a full-fledged natural language processing, the unneeded words in the queries are discarded. Only the specific contextual information along with the keywords containing database contents will be used to construct SQL queries. The contextual information is used to interpret the meaning of the queries, including the semantics involving AND,OR and NOT. In this thesis we describe the architecture of STRUCT, procedure of English query processing (parsing), basic idea of the grouping algorithm, SQL query construction and sample results of experiments

An Intelligent Text Extraction and Navigation System

We present sppc, a high-performance system for intelligent text extraction and navigation from German free text documents. The main purpose of sppc is to extract as much linguistic structure as possible for performing domain-specific processing. sppc consists of a set of domain-independent shallow core components which are realized by means of cascaded weighted finite state machines and generic dynamic tries. All extracted information is represented uniformly in one data structure (called the text chart) in a highly compact and linked form in order to support indexing and navigation through the set of solutions. Germa

Bilingual Knowledge Extraction Using Chunk Alignment

In this paper, we propose a new method for effectively acquiring bilingual knowledge by exploiting the dependency relations among the aligned chunks and words. We use a monolingual dependency parser to automatically obtain dependency parses of target language using chunk and word alignment. For reducing the computational complexity of structural alignment, we use a bilingual dictionary and adopt a divide-and-conquer strategy. By sharing the dependency relations of a given source sentence, we automatically obtain a dependency parse of a target sentence that is structurally consistent with the source sentence. Moreover, we extract bilingual knowledge bases from translation correspondences of singletons to surface verb subcategorization patterns by exploiting the bilingual dependency relations. To acquire reliable ones, we take a stepwise filtering method based on statistical test