Fred Popowich


2018

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Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers
Artaches Ambartsoumian | Fred Popowich
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Sentiment Analysis has seen much progress in the past two decades. For the past few years, neural network approaches, primarily RNNs and CNNs, have been the most successful for this task. Recently, a new category of neural networks, self-attention networks (SANs), have been created which utilizes the attention mechanism as the basic building block. Self-attention networks have been shown to be effective for sequence modeling tasks, while having no recurrence or convolutions. In this work we explore the effectiveness of the SANs for sentiment analysis. We demonstrate that SANs are superior in performance to their RNN and CNN counterparts by comparing their classification accuracy on six datasets as well as their model characteristics such as training speed and memory consumption. Finally, we explore the effects of various SAN modifications such as multi-head attention as well as two methods of incorporating sequence position information into SANs.

2016

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Automatic Tweet Generation From Traffic Incident Data
Khoa Tran | Fred Popowich
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)

2013

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Generating Natural Language Questions to Support Learning On-Line
David Lindberg | Fred Popowich | John Nesbit | Phil Winne
Proceedings of the 14th European Workshop on Natural Language Generation

2012

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Interactive Natural Language Query Construction for Report Generation
Fred Popowich | Milan Mosny | David Lindberg
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference

2009

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session
Michael Johnston | Fred Popowich
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session

2007

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Integration of an Arabic Transliteration Module into a Statistical Machine Translation System
Mehdi M. Kashani | Eric Joanis | Roland Kuhn | George Foster | Fred Popowich
Proceedings of the Second Workshop on Statistical Machine Translation

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Simultaneous Identification of Biomedical Named-Entity and Functional Relation Using Statistical Parsing Techniques
Zhongmin Shi | Anoop Sarkar | Fred Popowich
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

2006

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Automatic Transliteration of Proper Nouns from Arabic to English
Mehdi M. Kashani | Fred Popowich | Fatiha Sadat
Proceedings of the International Conference on the Challenge of Arabic for NLP/MT

After providing a brief introduction to the transliteration problem, and highlighting some issues specific to Arabic to English translation, a three phase algorithm is introduced as a computational solution to the problem. The algorithm is based on a Hidden Markov Model approach, but also leverages information available in on-line databases. The algorithm is then evaluated, and shown to achieve accuracy approaching .80%

2001

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What is example-based machine translation?
Davide Turcato | Fred Popowich
Workshop on Example-Based machine Translation

We maintain that the essential feature that characterizes a Machine Translation approach and sets it apart from other approaches is the kind of knowledge it uses. From this perspective, we argue that Example-Based Machine Translation is sometimes characterized in terms of inessential features. We show that Example-Based Machine Translation, as long as it is linguistically principled, significantly overlaps with other linguistically principled approaches to Machine Translation. We make a proposal for translation knowledge bases that make such an overlap explicit.

2000

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Pre-processing Closed Captions for Machine Translation
Davide Turcato | Fred Popowich | Paul McFetridge | Devlan Nicholson | Janine Toole
ANLP-NAACL 2000 Workshop: Embedded Machine Translation Systems

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Flexible Speech Act Based Dialogue Management
Eli Hagen | Fred Popowich
1st SIGdial Workshop on Discourse and Dialogue

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Adapting a synonym database to specific domains
Davide Turcato | Fred Popowich | Janine Toole | Dan Fass | Devlan Nicholson | Gordon Tisher
ACL-2000 Workshop on Recent Advances in Natural Language Processing and Information Retrieval

1999

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A bootstrap approach to automatically generating lexical transfer rules
Davide Turcato | Paul McFetridge | Fred Popowich | Janine Toole
Proceedings of Machine Translation Summit VII

We describe a method for automatically generating Lexical Transfer Rules (LTRs) from word equivalences using transfer rule templates. Templates are skeletal LTRs, unspecified for words. New LTRs are created by instantiating a template with words, provided that the words belong to the appropriate lexical categories required by the template. We define two methods for creating an inventory of templates and using them to generate new LTRs. A simpler method consists of extracting a finite set of templates from a sample of hand coded LTRs and directly using them in the generation process. A further method consists of abstracting over the initial finite set of templates to define higher level templates, where bilingual equivalences are defined in terms of correspondences involving phrasal categories. Phrasal templates are then mapped onto sets of lexical templates with the aid of grammars. In this way an infinite set of lexical templates is recursively defined. New LTRs are created by parsing input words, matching a template at the phrasal level and using the corresponding lexical categories to instantiate the lexical template. The definition of an infinite set of templates enables the automatic creation of LTRs for multi-word, non-compositional word equivalences of any cardinality.

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A unified example-based and lexicalist approach to Machine Translation
Davide Turcato | Paul McFetridge | Fred Popowich | Janine Toole
Proceedings of the 8th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

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Explanation-based learning for Machine Translation
Janine Toole | Fred Popowich | Devlan Nicholson | Davide Turcato | Paul McFetridge
Proceedings of the 8th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1997

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A lexicalist approach to the translation of colloquial text
Fred Popowich | Davide Turcato | Olivier Laurens | Paul McFetridge | J. Devlan Nicholson | Patrick McGivern | Maricela Corzo-Pena | Lisa Pidruchney | Scott McDonald
Proceedings of the 7th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1996

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Concept clustering and knowledge integration from a children’s dictionary
Caroline Barrière | Fred Popowich
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

1992

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Processing Complex Noun Phrases in a Natural Language Interface to a Statistical Database
Fred Popowich | Paul McFetridge | Dan Fass | Gary Hall
COLING 1992 Volume 1: The 14th International Conference on Computational Linguistics

1989

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Tree Unification Grammar
Fred Popowich
27th Annual Meeting of the Association for Computational Linguistics

1985

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SAUMER: Sentence Analysis Using Metarules
Fred Popowich
Second Conference of the European Chapter of the Association for Computational Linguistics