Frugal Paradigm Completion

no code implementations • ACL 2020 • Alex Erdmann, er, Tom Kenter, Markus Becker, Christian Schallhart

Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies, yet building them is expensive.

CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing

1 code implementation • LREC 2020 • Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alex Erdmann, er, Nizar Habash

We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.

Arabic Sentiment Analysis Arabic Text Diacritization +6

A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance

no code implementations • WS 2019 • Alex Erdmann, er, Salam Khalifa, Mai Oudah, Nizar Habash, Houda Bouamor

We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring only minimal language specific input.

Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities

2 code implementations • NAACL 2019 • Alex Erdmann, er, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bod{\'e}n{\`e}s, Micha Elsner, Yukun Feng, Brian Joseph, B{\'e}atrice Joyeux-Prunel, Marie-Catherine de Marneffe

Scholars in inter-disciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora.

Active Learning General Classification +3

Complementary Strategies for Low Resourced Morphological Modeling

no code implementations • WS 2018 • Alex Erdmann, er, Nizar Habash

Morphologically rich languages are challenging for natural language processing tasks due to data sparsity.

Morphological Analysis Word Embeddings

Addressing Noise in Multidialectal Word Embeddings

no code implementations • ACL 2018 • Alex Erdmann, er, Nasser Zalmout, Nizar Habash

Arabic dialects lack large corpora and are noisy, being linguistically disparate with no standardized spelling.

Sentence Transliteration +1

Noise-Robust Morphological Disambiguation for Dialectal Arabic

no code implementations • NAACL 2018 • Nasser Zalmout, Alex Erdmann, er, Nizar Habash

User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging.

Lexical Normalization Morphological Analysis +3

Unified Guidelines and Resources for Arabic Dialect Orthography

no code implementations • LREC 2018 • Nizar Habash, Fadhl Eryani, Salam Khalifa, Owen Rambow, Dana Abdulrahim, Alex Erdmann, er, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al-Shargi, Sakhar Alkhereyf, Basma Abdulkareem, Esk, Ramy er, Mohammad Salameh, Hind Saddiki

Speech Recognition Transliteration

The MADAR Arabic Dialect Corpus and Lexicon

no code implementations • LREC 2018 • Houda Bouamor, Nizar Habash, Mohammad Salameh, Wajdi Zaghouani, Owen Rambow, Dana Abdulrahim, Ossama Obeid, Salam Khalifa, Fadhl Eryani, Alex Erdmann, er, Kemal Oflazer

Transliteration

Challenges and Solutions for Latin Named Entity Recognition

no code implementations • WS 2016 • Alex Erdmann, er, Christopher Brown, Brian Joseph, Mark Janse, Petra Ajaka, Micha Elsner, Marie-Catherine de Marneffe

Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English.

Active Learning Domain Adaptation +5