Wael Salloum


2018

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From dictations to clinical reports using machine translation
Gregory Finley | Wael Salloum | Najmeh Sadoughi | Erik Edwards | Amanda Robinson | Nico Axtmann | Michael Brenndoerfer | Mark Miller | David Suendermann-Oeft
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

A typical workflow to document clinical encounters entails dictating a summary, running speech recognition, and post-processing the resulting text into a formatted letter. Post-processing entails a host of transformations including punctuation restoration, truecasing, marking sections and headers, converting dates and numerical expressions, parsing lists, etc. In conventional implementations, most of these tasks are accomplished by individual modules. We introduce a novel holistic approach to post-processing that relies on machine callytranslation. We show how this technique outperforms an alternative conventional system—even learning to correct speech recognition errors during post-processing—while being much simpler to maintain.

2017

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Deep Learning for Punctuation Restoration in Medical Reports
Wael Salloum | Greg Finley | Erik Edwards | Mark Miller | David Suendermann-Oeft
BioNLP 2017

In clinical dictation, speakers try to be as concise as possible to save time, often resulting in utterances without explicit punctuation commands. Since the end product of a dictated report, e.g. an out-patient letter, does require correct orthography, including exact punctuation, the latter need to be restored, preferably by automated means. This paper describes a method for punctuation restoration based on a state-of-the-art stack of NLP and machine learning techniques including B-RNNs with an attention mechanism and late fusion, as well as a feature extraction technique tailored to the processing of medical terminology using a novel vocabulary reduction model. To the best of our knowledge, the resulting performance is superior to that reported in prior art on similar tasks.

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Automated Preamble Detection in Dictated Medical Reports
Wael Salloum | Greg Finley | Erik Edwards | Mark Miller | David Suendermann-Oeft
BioNLP 2017

Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of state-of-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.

2016

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SPLIT: Smart Preprocessing (Quasi) Language Independent Tool
Mohamed Al-Badrashiny | Arfath Pasha | Mona Diab | Nizar Habash | Owen Rambow | Wael Salloum | Ramy Eskander
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Text preprocessing is an important and necessary task for all NLP applications. A simple variation in any preprocessing step may drastically affect the final results. Moreover replicability and comparability, as much as feasible, is one of the goals of our scientific enterprise, thus building systems that can ensure the consistency in our various pipelines would contribute significantly to our goals. The problem has become quite pronounced with the abundance of NLP tools becoming more and more available yet with different levels of specifications. In this paper, we present a dynamic unified preprocessing framework and tool, SPLIT, that is highly configurable based on user requirements which serves as a preprocessing tool for several tools at once. SPLIT aims to standardize the implementations of the most important preprocessing steps by allowing for a unified API that could be exchanged across different researchers to ensure complete transparency in replication. The user is able to select the required preprocessing tasks among a long list of preprocessing steps. The user is also able to specify the order of execution which in turn affects the final preprocessing output.

2014

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The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction
Alla Rozovskaya | Nizar Habash | Ramy Eskander | Noura Farra | Wael Salloum
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

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Tharwa: A Large Scale Dialectal Arabic - Standard Arabic - English Lexicon
Mona Diab | Mohamed Al-Badrashiny | Maryam Aminian | Mohammed Attia | Heba Elfardy | Nizar Habash | Abdelati Hawwari | Wael Salloum | Pradeep Dasigi | Ramy Eskander
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We introduce an electronic three-way lexicon, Tharwa, comprising Dialectal Arabic, Modern Standard Arabic and English correspondents. The paper focuses on Egyptian Arabic as the first pilot dialect for the resource, with plans to expand to other dialects of Arabic in later phases of the project. We describe Tharwa’s creation process and report on its current status. The lexical entries are augmented with various elements of linguistic information such as POS, gender, rationality, number, and root and pattern information. The lexicon is based on a compilation of information from both monolingual and bilingual existing resources such as paper dictionaries and electronic, corpus-based dictionaries. Multiple levels of quality checks are performed on the output of each step in the creation process. The importance of this lexicon lies in the fact that it is the first resource of its kind bridging multiple variants of Arabic with English. Furthermore, it is a wide coverage lexical resource containing over 73,000 Egyptian entries. Tharwa is publicly available. We believe it will have a significant impact on both Theoretical Linguistics as well as Computational Linguistics research.

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Sentence Level Dialect Identification for Machine Translation System Selection
Wael Salloum | Heba Elfardy | Linda Alamir-Salloum | Nizar Habash | Mona Diab
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Dialectal Arabic to English Machine Translation: Pivoting through Modern Standard Arabic
Wael Salloum | Nizar Habash
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Elissa: A Dialectal to Standard Arabic Machine Translation System
Wael Salloum | Nizar Habash
Proceedings of COLING 2012: Demonstration Papers

2011

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Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation
Wael Salloum | Nizar Habash
Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties