Papers by Alexander Erdmann
Frugal Paradigm Completion (2020.acl-main)
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| Challenge: | Lexica distinguishing all morphologically related forms of each lexeme is crucial to many language technologies, yet building them is expensive. |
| Approach: | They propose a paradigm completion approach that predicts all related forms from as few manually provided forms as possible. |
| Outcome: | The proposed method reduces manual labor by 16-63% and is the most robust to typological variation. |
Addressing Noise in Multidialectal Word Embeddings (P18-2)
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| Challenge: | Dialectal Arabic (DA) is problematically noisy and lacks a large corpus of non-noisy words. |
| Approach: | They propose to use word embedding tools to maximize the informative content leveraged in each training sentence and analyze methods for representing disparate dialects in one embeddable space. |
| Outcome: | The proposed methods improve performance on low and high frequency words while preserving accuracy on low frequency forms. |
The MADAR Arabic Dialect Corpus and Lexicon (L18-1)
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Houda Bouamor, Nizar Habash, Mohammad Salameh, Wajdi Zaghouani, Owen Rambow, Dana Abdulrahim, Ossama Obeid, Salam Khalifa, Fadhl Eryani, Alexander Erdmann, Kemal Oflazer
| Challenge: | Using a corpus of 25 Arabic city dialects and a lexicon of 1,045 concepts, we study 25 cities in a travel domain . focus on cities opens new avenues for research from dialectology to dialect identification and machine translation. |
| Approach: | They present two Arabic language resources that are part of the Multi Arabic Dialect Applications and Resources project. |
| Outcome: | The proposed resources are the first of their kind in terms of their coverage and fine granularity. |
Noise-Robust Morphological Disambiguation for Dialectal Arabic (N18-1)
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| Challenge: | Noisy content is non-canonical in nature, with lexical, orthographic, and phonetic variations. |
| Approach: | They propose a neural morphological tagging and disambiguation model for Egyptian Arabic with various extensions to handle noisy content. |
| Outcome: | The proposed model achieves about 5% relative error reduction over a state-of-the-art baseline for Egyptian Arabic. |
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities (N19-1)
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Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Béatrice Joyeux-Prunel, Marie-Catherine de Marneffe
| Challenge: | Scholars in interdisciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora. |
| Approach: | They propose an active learning solution for named entity recognition that maximizes a custom model’s improvement per additional unit of manual annotation. |
| Outcome: | The proposed model reduces required annotation by 20-60% and outperforms a competitive active learning baseline. |
Unified Guidelines and Resources for Arabic Dialect Orthography (L18-1)
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Nizar Habash, Fadhl Eryani, Salam Khalifa, Owen Rambow, Dana Abdulrahim, Alexander Erdmann, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al-Shargi, Sakhar Alkhereyf, Basma Abdulkareem, Ramy Eskander, Mohammad Salameh, Hind Saddiki
| Challenge: | Existing efforts to conventionalize the dialectal orthography of Arabic have focused on specific dialects and made ad hoc decisions. |
| Approach: | They propose a set of guidelines and meta-guidelines for conventional orthography of Arabic dialects . they apply them to 28 Arab city dialects from Rabat to Muscat . |
| Outcome: | The proposed guidelines and resources are being used by three large Arabic dialect processing projects in three universities. |
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing (2020.lrec-1)
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Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, Nizar Habash
| Challenge: | CAMeL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Approach: | They present CAMeL Tools, an open-source Python toolkit for Arabic natural language processing . CAMeleL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Outcome: | The proposed tools are based on CAMeL Tools, an open-source Python toolkit for Arabic natural language processing. |
The Paradigm Discovery Problem (2020.acl-main)
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| Challenge: | a paradigm discovery problem is a task of learning an inflectional morphological system from unannotated sentences. |
| Approach: | They formalize the paradigm discovery problem and develop evaluation metrics for judging systems . they use word embeddings and string similarity to cluster forms by cell and by paradigm . |
| Outcome: | The proposed system suggests clustering by cell across different inflection classes is the most pressing challenge for future work. |