33 research outputs found
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging
Semitic languages can be highly ambiguous, having several interpretations of
the same surface forms, and morphologically rich, having many morphemes that
realize several morphological features. This is further exacerbated for
dialectal content, which is more prone to noise and lacks a standard
orthography. The morphological features can be lexicalized, like lemmas and
diacritized forms, or non-lexicalized, like gender, number, and part-of-speech
tags, among others. Joint modeling of the lexicalized and non-lexicalized
features can identify more intricate morphological patterns, which provide
better context modeling, and further disambiguate ambiguous lexical choices.
However, the different modeling granularity can make joint modeling more
difficult. Our approach models the different features jointly, whether
lexicalized (on the character-level), where we also model surface form
normalization, or non-lexicalized (on the word-level). We use Arabic as a test
case, and achieve state-of-the-art results for Modern Standard Arabic, with 20%
relative error reduction, and Egyptian Arabic (a dialectal variant of Arabic),
with 11% reduction
Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text
Automatic Arabic diacritization is useful in many applications, ranging from
reading support for language learners to accurate pronunciation predictor for
downstream tasks like speech synthesis. While most of the previous works
focused on models that operate on raw non-diacritized text, production systems
can gain accuracy by first letting humans partly annotate ambiguous words. In
this paper, we propose 2SDiac, a multi-source model that can effectively
support optional diacritics in input to inform all predictions. We also
introduce Guided Learning, a training scheme to leverage given diacritics in
input with different levels of random masking. We show that the provided hints
during test affect more output positions than those annotated. Moreover,
experiments on two common benchmarks show that our approach i) greatly
outperforms the baseline also when evaluated on non-diacritized text; and ii)
achieves state-of-the-art results while reducing the parameter count by over
60%.Comment: Arabic text diacritization, partially-diacritized text, Arabic
natural language processin
Tagging Classical Arabic Text using Available Morphological Analysers and Part of Speech Taggers
Focusing on Classical Arabic, this paper in its first part evaluates morphological analysers and POS taggers that are available freely for research purposes, are designed for Modern Standard Arabic (MSA) or Classical Arabic (CA), are able to analyse all forms of words, and have academic credibility. We list and compare supported features of each tool, and how they differ in the format of the output, segmentation, Part-of-Speech (POS) tags and morphological features. We demonstrate a sample output of each analyser against one CA fully-vowelized sentence. This evaluation serves as a guide in choosing the best tool that suits research needs. In the second part, we report the accuracy and coverage of tagging a set of classical Arabic vocabulary extracted from classical texts. The results show a drop in the accuracy and coverage and suggest an ensemble method might increase accuracy and coverage for classical Arabic
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
International audienceWe introduce the first treebank for a romanized user-generated content variety of Algerian, a North-African Arabic dialect known for its frequent usage of code-switching. Made of 1500 sentences, fully annotated in morpho-syntax and Universal Dependency syntax, with full translation at both the word and the sentence levels, this treebank is made freely available. It is supplemented with 50k unlabeled sentences collected from Common Crawl and web-crawled data using intensive data-mining techniques. Preliminary experiments demonstrate its usefulness for POS tagging and dependency parsing. We believe that what we present in this paper is useful beyond the low-resource language community. This is the first time that enough unlabeled and annotated data is provided for an emerging user-generated content dialectal language with rich morphology and code switching, making it an challenging test-bed for most recent NLP approaches
Multi-Task sequence prediction for Tunisian Arabizi multi-level annotation
In this paper we propose a multi-task sequence prediction system, based on recurrent neural networks and used to annotate on multiple levels an Arabizi Tunisian corpus. The annotation performed are text classification, tokenization, PoS tagging and encoding of Tunisian Arabizi into CODA* Arabic orthography. The system is learned to predict all the annotation levels in cascade, starting from Arabizi input. We evaluate the system on the TIGER German corpus, suitably converting data to have a multi-task problem, in order to show the effectiveness of our neural architecture. We show also how we used the system in order to annotate a Tunisian Arabizi corpus, which has been afterwards manually corrected and used to further evaluate sequence models on Tunisian data. Our system is developed for the Fairseq framework, which allows for a fast and easy use for any other sequence prediction problem
Overview of the SPMRL 2013 shared task: cross-framework evaluation of parsing morphologically rich languages
This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given different representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios
Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages
International audienceThis paper reports on the first shared task on statistical parsing of morphologically rich lan- guages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the eval- uation metrics for parsing MRLs given dif- ferent representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios