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2012
This paper presents the results of an approach to automatically acquire large-scale, probabilistic Lexical-Functional Grammar (LFG) resources for Arabic from the Penn Arabic Treebank (ATB). Our starting point is the earlier, work of (Tounsi et al., 2009) on automatic LFG f(eature)-structure annotation for Arabic using the ATB. They exploit tree configuration, POS categories, functional tags, local heads and trace information to annotate nodes with LFG feature-structure equations. We utilize this annotation to automatically acquire grammatical function (dependency) based subcategorization frames and paths linking long-distance dependencies (LDDs). Many state-of-the-art treebank-based probabilistic parsing approaches are scalable and robust but often also shallow: they do not capture LDDs and represent only local information. Subcategorization frames and LDD paths can be used to recover LDDs from such parser output to capture deep linguistic information. Automatic acquisition of langu...
A number of papers have reported on methods for the automatic acquisition of large-scale, probabilistic LFG-based grammatical resources from treebanks for English (Cahill and al.. Here, we extend the LFG grammar acquisition approach to Arabic and the Penn Arabic Treebank (ATB) (Maamouri and Bies, 2004), adapting and extending the methodology of (Cahill and al., 2004) originally developed for English. Arabic is challenging because of its morphological richness and syntactic complexity. Currently 98% of ATB trees (without FRAG and X) produce a covering and connected f-structure. We conduct a qualitative evaluation of our annotation against a gold standard and achieve an f-score of 95%. îi ø © Ë d © © tªË d Al-Eunofu Al˜a * iy yuhad˜idu Al-salAma
2006
This paper describes the construction of a dependency bank gold standard for Arabic, DCU 250 Arabic Dependency Bank (DCU 250), based on the Arabic Penn Treebank Corpus (ATB) (Bies and Maamouri, 2003; Maamouri and Bies, 2004) within the theoretical framework of Lexical Functional Grammar (LFG). For parsing and automatically extracting grammatical and lexical resources from treebanks, it is necessary to evaluate against established gold standard resources. Gold standards for various languages have been developed, but to our knowledge, such a resource has not yet been constructed for Arabic. The construction of the DCU 250 marks the first step towards the creation of an automatic LFG f-structure annotation algorithm for the ATB, and for the extraction of Arabic grammatical and lexical resources.
ACM Transactions on Asian and Low-Resource Language Information Processing, 2022
Treebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to part-of-speech tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this article is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accuracy of statistical parsers. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts. The first concept is the approach of determining the main word of the sentence, and the second concept is the representation of the joined and covert pronouns. To evaluate I3rab, we compared its performance against a subset of Pr...
2010
We investigate Arabic Context Free Grammar parsing with dependency annotation comparing lexicalised and unlexicalised parsers. We study how morphosyntactic as well as function tag information percolation in the form of grammar transforms (Johnson, 1998 affects the performance of a parser and helps dependency assignment. We focus on the three most frequent functional tags in the Arabic Penn Treebank: subjects, direct objects and predicates . We merge these functional tags with their phrasal categories and (where appropriate) percolate case information to the non-terminal (POS) category to train the parsers. We then automatically enrich the output of these parsers with full dependency information in order to annotate trees with Lexical Functional Grammar (LFG) f-structure equations with produce f-structures, i.e. attribute-value matrices approximating to basic predicate-argument-adjunct structure representations. We present a series of experiments evaluating how well lexicalized, history-based, generative (Bikel) as well as latent variable PCFG (Berkeley) parsers cope with the enriched Arabic data. We measure quality and coverage of both the output trees and the generated LFG f-structures. We show that joint functional and morphological information percolation improves both the recovery of trees as well as dependency results in the form of LFG f-structures.
The Arabic Treebank at the Linguistic Data Consortium has significantly revised and enhanced its annotation guidelines and annotation procedure over the past year. The revised syntactic guidelines are now being applied in annotation production, and the combination of the revised guidelines and a period of intensive annotator training has raised inter-annotator agreement f-measure scores already. Revised morphological/part-of-speech (POS) guidelines are nearly complete as well, and will be applied in annotation production in the near future. This paper reports on an experiment in automatically enhancing the old morphological/POS tags in the right direction and the resulting parsing improvement. Finally, a new division of the POS analysis marking both morphological form and POS function is proposed.
Research in Computing Science
Linguistic resources are very important to any natural language processing task. Unfortunately, the manual construction of these resources is laborious and time-consuming. The use of annotated corpora as a knowledge database might be a solution to a fast construction of a grammar for a given language. In this paper, we present our method to automatically induce a syntactic grammar from an Arabic annotated corpus (The Penn Arabic TreeBank), a probabilistic context free grammar in our case. To construct our resource, we first induce context free rules from the annotated corpus trees as a first step and then we calculate a specific probability for each induced rule. Finally, we present and discuss the obtained grammar.
The International Conference on Information and Communication Technology Research (ICTRC), 2015
A Treebank is a linguistic resource that is composed of a large collection of manually annotated and verified syntactically analyzed sentences. Statistical Natural Language Processing (NLP) approaches have been successful in using these annotations for developing basic NLP tasks such as tokenization, diacritization, part-of-speech tagging, parsing, among others. In this paper, we address the problem of exploiting Treebank resources for statistical parsing of Modern Standard Arabic (MSA) sentences. Statistical parsing is significant for NLP tasks that use parsed text as an input such as Information Retrieval, and Machine Translation. We conducted an experiment on Pen Arabic Treebank (PATB) and the parsing performance obtained in terms of Precision, Recall, and F-measure was 82.4%, 86.6%, 84.4%, respectively.
… on Arabic Language Resources and Tools, …, 2009
The Columbia Arabic Treebank (CATiB) is a database of syntactic analyses of Arabic sentences. CATiB contrasts with previous ap-proaches to Arabic treebanking in its emphasis on faster production with some constraints on linguistic richness. Two basic ideas inspire ...
2008
In this paper, we report on several software implementations that we have developed within Prague Arabic Dependency Treebank or some other projects concerned with Arabic Natural Language Processing. We try to guide the reader through some essential tasks and note the solutions that we have designed and used. We as well point to third-party computational systems that the research community might exploit in the future work in this field. Arabic, dependency grammar, treebank, language annotation and processing, application programming. 1.
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