ClearTK 2.0: Design Patterns for Machine Learning in UIMA

2014-05-01 · LREC 2014 5 · Steven Bethard, Philip Ogren, Lee Becker

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

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Tasks

BIG-bench Machine Learning Chunking

The CLaC Discourse Parser at CoNLL-2015

2017-08-19 · CONLL 2015 7 · Majid Laali, Elnaz Davoodi, Leila Kosseim

This paper describes our submission (kosseim15) to the CoNLL-2015 shared task on shallow discourse parsing. We used the UIMA framework to develop our parser and used ClearTK to add machine learning functionality to the U…

BIG-bench Machine Learning Discourse Parsing

PEARL: ProjEction of Annotations Rule Language, a Language for Projecting (UIMA) Annotations over RDF Knowledge Bases

2012-05-01 · LREC 2012 5 · Maria Teresa Pazienza, Arm Stellato, o, Andrea Turbati

In this paper we present a language, PEARL, for projecting annotations based on the Unstructured Information Management Architecture (UIMA) over RDF triples. The language offer is twofold: first, a query mechanism, built…

Management

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2013-06-01 · SEMEVAL 2013 6 · Steven Bethard

Chunking Relation Classification Temporal Information Extraction

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2012-05-01 · LREC 2012 5 · Hern, Nicolas ez

One of the major issues dealing with any workflow management frameworks is the components interoperability. In this paper, we are concerned with the Apache UIMA framework. We address the problem by considering separately…

Management POS

Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns

2024-06-13 · Kaavya Rekanar, Martin Hayes, Ganesh Sistu, Ciaran Eising

Visual Question Answering (VQA) models play a critical role in enhancing the perception capabilities of autonomous driving systems by allowing vehicles to analyze visual inputs alongside textual queries, fostering natura…

Autonomous Driving Question Answering Visual Question Answering Visual Question Answering (VQA)