Memory-Based Shallow Parsing

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement

Memory-Based Shallow Parsing

We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this paper show competitive results, the F-value for the Wall Street Journal (WSJ) treebank is: 93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and 79.0% for object detection.Comment: 8 pages, to appear in: Proceedings of the EACL'99 workshop on Computational Natural Language Learning (CoNLL-99), Bergen, Norway, June 199

Implementasi RSS Reader Dengan KXML Pada Telepon Genggam

RSS merupakan singkatan dari Rich Site Summary, RDF Site Summary atau Really SimpleSyndication. RSS yang formatnya mengikuti standar XML 1.0, mempunyai struktur yang sederhana danramping, dan didesain untuk dapat membungkus informasi dengan lebih efisien. Pada penelitian ini dibuataplikasi RSS Reader pada telepon genggam. Aplikasi RSS Reader memanfaatkan API parser kXML. ParserkXML mendukung parsing secara text-based dan parsing secara binary encoding (WBXML --Wireless BinaryXML--). Kedua metode parsing tersebut dibandingkan dari sisi waktu (delay parsing) dan kebutuhan sumberdaya (memory).Dari implementasi dan analisis perbandingan kedua aplikasi tersebut didapat bahwa metode parsingdengan binary encoding memebutuhkan waktu proses yang lebih singkat dibandingkan dengan parsing secaratext-based. Dokumen WBXML lebih kecil ukurannya daripada dokumen text-based karena dokumen WBXMLdirepresentasi dalam tokens dan literal string. Karena itu memory yang dibutuhkan untuk melakukan parsingdokumen WBXML juga lebih sedikit

Retrieval Effects in Sentence Parsing and Interpretation

This dissertation is an investigation into the memory mechanisms that support parsing and how they constrain parsing success. Constraints can occur from two directions: the ability to store items participating in grammatical dependencies, or else the dependencies themselves, and the ability to retrieve items required for creating grammatical dependencies. A review of available evidence regarding memory constraints on parsing suggests that although attention has more often been focused on the storage constraints on parsing, the evidence is actually more consistent with a retrieval account of parsing breakdown. I present a framework for investigating retrieval effects in parsing, based on existing models of associative memory retrieval and illustrate how the work traditionally carried out by the parser can be understood as an instance of working memory retrieval. Five experiments present new evidence in support of the retrieval approach. Two of these illustrate effects of semantic interference, in which semantic properties of candidate NPs affect the ability to complete grammatical dependencies, and the remaining three illustrate effects of referential interference, in which the availability of items in the situation model affect parsing success. It is argued that storage accounts of parsing breakdown cannot account for these results, promoting the conclusion that an associative memory mechanism underlies both parsing and sentence interpretation

Memory-Based Parsing

The development of robust parsing systems can be greatly expedited by the application of memory-based parsing techniques. Described here are the parsing techniques used by the Integrated Partial Parser (IPP), a system designed to read and generalize from large numbers of news stories. These techniques include top-down predictions generated from high-level memory structures, and simple bottom-up heuristics to handle language-specific problems. A detailed example is presented

Memory-Based Parsing

The development of robust parsing systems can be greatly expedited by the application of memory-based parsing techniques. Described here are the parsing techniques used by the Integrated Partial Parser (IPP), a system designed to read and generalize from large numbers of news stories. These techniques include top-down predictions generated from high-level memory structures, and simple bottom-up heuristics to handle language-specific problems. A detailed example is presented

An Empirical Evaluation of BFS, and DFS Search Algorithms on J2ME Platform, and SVG Tiny Parsing on J2ME Platform Using SAX, StAX, and DOM Parsers

SVG (Scalable Vector Graphics) Tiny, an XML-based data representation format was used in our Global Train Route Planner J2ME application to render and manipulate train network  images. The SVG Tiny format enables the application to be adaptable with any train network map. We compared three parsing models namely DOM (Document Object Model), SAX (Simple API for XML), and StAX (Streaming API for XML) which were used to visualize the images on mobile phone. We present here the result of the runtime performances, and memory footprints of those parsing models. This is a significant study because handheld devices like mobile phones require seamless interactivity (i.e. high performance) with users and an efficient parsing mechanism with less memory footprints. We also empirically investigated two route searching algorithms - graph and matrix based implementation of DFS (Depth First Search), and matrix based BFS (Breadth First Search) – for performance and memory footprints on a J2ME mobile device emulator. We concluded that DOM parser and DFS based on graph implementation are of better performance than the others