Dashboard: A Tool for Integration, Validation, and Visualization of Distributed NLP Systems on Heterogeneous Platforms (original) (raw)

Enriched Dashboard: An Integration and Visualization Tool for Distributed NLP Systems on Heterogeneous Platforms

2013 13th International Conference on Computational Science and Its Applications, 2013

Dashboard is a tool for integration, validation, and visualization of Natural Language Processing (NLP) systems. It provides infrastructural facilities using which individual NLP modules may be evaluated and refined, and multiple NLP modules may be combined to build a large end-user NLP system. It helps system integration team to integrate and validate NLP systems. The tool provides a visualization interface that helps developers to profile (time and memory) for each module. It helps researchers to evaluate and compare their module with the earlier versions of same module. The tool promotes reuse of existing modules to build new NLP systems. Dashboard supports execution of modules that are distributed on heterogeneous platforms. It provides a powerful notation to specify runtime properties of NLP modules. It provides an easy-touse graphical interface that is developed using Eclipse RCP. Users can choose an I/O perspective (view) that allows him better visualization of intermediate outputs. Additionally, Eclipse RCP provides plugin architecture; hence customized end-user specific functionality can be easily added to the tool.

Dashboard: An integration and testing platform based on backboard architecture for NLP applications

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010), 2010

The paper presents a software integration, testing and visualization tool, called Dashboard, which is based on pipe-lined backboard architecture for family of natural language processing (NLP) application. The Dashboard helps in testing of a module in isolation, facilitating the training and tuning of a module, integration and testing of a set of heterogeneous modules, and building and testing of complete

Software Infrastructure for Natural Language Processing

Computing Research Repository, 1997

We classify and review current approaches to software infrastructure for research, development and delivery of NLP systems. The task is motivated by a discussion of current trends in the field of NLP and Language Engineering. We describe a system called GATE (a General Architecture for Text Engineering) that provides a software infrastructure on top of which heterogeneous NLP processing modules may be evaluated and refined individually, or may be combined into larger application systems. GATE aims to support both researchers and developers working on component technologies (e.g. parsing, tagging, morphological analysis) and those working on developing end-user applications (e.g. information extraction, text summarisation, document generation, machine translation, and second language learning). GATE promotes reuse of component technology, permits specialisation and collaboration in large-scale projects, and allows for the comparison and evaluation of alternative technologies. The first release of GATE is now available.

Kathaa: A Visual Programming Framework for NLP Applications

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, 2016

In this paper, we present Kathaa 1 , an open source web based Visual Programming Framework for NLP applications. It supports design, execution and analysis of complex NLP systems by choosing and visually connecting NLP modules from an already available and easily extensible Module library. It models NLP systems as a Directed Acyclic Graph of optionally parallalized information flow, and lets the user choose and use available modules in their NLP applications irrespective of their technical proficiency. Kathaa exposes a precise Module definition API to allow easy integration of external NLP components (along with their associated services as docker containers), it allows everyone to publish their services in a standardized format for everyone else to use it out of the box.

An open distributed architecture for reuse and integration of heterogeneous NLP components

Proceedings of the fifth conference on Applied natural language processing -, 1997

The shift from Computational Linguistics to Language Engineering is indicative of new trends in NLP. This paper reviews two NLP engineering problems: reuse and integration, while relating these concerns to the larger context of applied NLP. It presents a software architecture which is geared to support the development of a variety of large-scale NLP applications: Information Retrieval, Corpus Processing, Multilingual MT, and integration of Speech Components.

@ rabLearn : a Model of NLP Tools Integration in ICALL Systems

This paper describes the development of an Intelligent Computer Assisted Language Learning (ICALL) platform for Arabic using Natural Language Processing (NLP) tools, this environment called @rabLearn. As a first step, we have developed a Part Of Speech Tagger (POS tagger) for Arabic. In a second step, we used this NLP tool to create educational activities for Arabic learners by the use of @rabLearn (an NLP-based authoring system). We will focus, in this paper, on the integration of an NLP tool like our morphological analyser in a platform of language learning to generate activities intended for learner. We prove that this tool can make platforms more powerful by creating various kinds of activities.

Current issues in software engineering for natural language processing

2003

In Natural Language Processing (NLP), research results from software engineering and software technology have often been neglected. This paper describes some factors that add complexity to the task of engineering reusable NLP systems (beyond conventional software systems). Current work in the area of design patterns and composition languages is described and claimed relevant for natural language processing. The benefits of NLP componentware and barriers to reuse are outlined, and the dichotomies "system versus experiment" and "toolkit versus framework" are discussed. It is argued that in order to live up to its name language engineering must not neglect component quality and architectural evaluation when reporting new NLP research.

Integrated NLP evaluation system for pluggable evaluation metrics with extensive interoperable toolkit

Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing - SETQA-NLP '09, 2009

To understand the key characteristics of NLP tools, evaluation and comparison against different tools is important. And as NLP applications tend to consist of multiple semiindependent sub-components, it is not always enough to just evaluate complete systems, a fine grained evaluation of underlying components is also often worthwhile. Standardization of NLP components and resources is not only significant for reusability, but also in that it allows the comparison of individual components in terms of reliability and robustness in a wider range of target domains. But as many evaluation metrics exist in even a single domain, any system seeking to aid inter-domain evaluation needs not just predefined metrics, but must also support pluggable user-defined metrics. Such a system would of course need to be based on an open standard to allow a large number of components to be compared, and would ideally include visualization of the differences between components. We have developed a pluggable evaluation system based on the UIMA framework, which provides visualization useful in error analysis. It is a single integrated system which includes a large ready-to-use, fully interoperable library of NLP tools.

Workshop on the evaluation of natural language processing systems

1990

In the past few years, the computational linguistics research community has begun to wrestle with the problem of how to evaluate its progress in developing natural language processing systems. With the exception of natural language interfaces, there are few working systems in existence, and they tend to focus on very different tasks using equally different techniques.