This paper presents the results of an approach to automatically acquire large-scale, probabilisti... more 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 longdistance 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 language resources from existing treebanks saves time and effort involved in creating such resources by hand. Moreover, data-driven automatic acquisition naturally associates probabilistic information with subcategorization frames and LDD paths. Finally, based on the statistical distribution of LDD path types, we propose empirical bounds on traditional regular expression based functional uncertainty equations used to handle LDDs in LFG.
Abstract We develop an open-source large-scale finite-state morphological processing toolkit (Ara... more Abstract We develop an open-source large-scale finite-state morphological processing toolkit (AraComLex) for Modern Standard Arabic (MSA) distributed under the GPLv3 license (http://aracomlex. sourceforge. net). The morphological transducer is based on a lexical database specifically constructed for this purpose. In contrast to previous resources, the database is tuned to MSA, eliminating lexical entries no longer attested in contemporary use.
ABSTRACT A spelling error detection and correction application is based on three main components:... more ABSTRACT A spelling error detection and correction application is based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in the overall performance of the system.
In recent years, research as well as software applications have been concentrating on Machine Tra... more In recent years, research as well as software applications have been concentrating on Machine Translation (MT). This is due to many factors, the most important of which is the increasing need to create online communication between different parts of the world and between people speaking different languages. People felt the need for machine translation since the advent of computers, but the early attempts they made were completely dissatisfactory.
Abstract Arabic is a language known for its rich and complex morphology. Although many research p... more Abstract Arabic is a language known for its rich and complex morphology. Although many research projects have focused on the problem of Arabic morphological analysis using different techniques and approaches, very few have addressed the issue of generation of fully inflected words for the purpose of text authoring. Available open-source spell checking resources for Arabic are too small and inadequate.
ABSTRACT Unknown words, or out of vocabulary words (OOV), cause a significant problem to morpholo... more ABSTRACT Unknown words, or out of vocabulary words (OOV), cause a significant problem to morphological analysers, syntactic parses, MT systems and other NLP applications. Unknown words make up 29% of the word types in in a large Arabic corpus used in this study. With today's corpus sizes exceeding 109 words, it becomes impossible to manually check corpora for new words to be included in a lexicon.
Abstract We provide lexical profiling for Arabic by covering two important linguistic aspects of ... more Abstract We provide lexical profiling for Arabic by covering two important linguistic aspects of Arabic lexical information, namely morphological inflectional paradigms and syntactic subcategorization frames, making our database a rich repository of Arabic lexicographic details. First, we provide a complete description of the inflectional behaviour of Arabic lemmas based on statistical distribution.
Abstract A morphological analyser only recognizes words that it already knows in the lexical data... more Abstract A morphological analyser only recognizes words that it already knows in the lexical database. It needs, however, a way of sensing significant changes in the language in the form of newly borrowed or coined words with high frequency. We develop a finite-state morphological guesser in a pipelined methodology for extracting unknown words, lemmatizing them, and giving them a priority weight for inclusion in a lexicon.
Abstract We develop an open-source large-scale finitestate morphological processing toolkit (AraC... more Abstract We develop an open-source large-scale finitestate morphological processing toolkit (AraComLex) for Modern Standard Arabic (MSA) distributed under the GPLv3 license. The morphological transducer is based on a lexical database specifically constructed for this purpose. In contrast to previous resources, the database is tuned to MSA, eliminating lexical entries no longer attested in contemporary use.
We build a large-scale, corpus-based lexical database that is representative of Modern Standard A... more We build a large-scale, corpus-based lexical database that is representative of Modern Standard Arabic (MSA). We use a corpus of 1,089,111,204 words, a pre-annotation tool, machine learning techniques, and knowledge-based templatic matching to automatically acquire lexical knowledge about morpho-syntactic attributes and inflection paradigms of each entry. Our lexical database is scalable, interoperable and suitable for constructing a morphological analyser, regardless of the design approach and programming language used. The database is formatted according to the international ISO standard in lexical resource representation, the Lexical Markup Framework (LMF). This lexical database is used in developing an open-source finite-state morphological processing toolkit. We build a system, AraComLex (Arabic Computer Lexicon), for human lexicographers to manage and curate the standardized and scalable lexical database.
We describe the design and implementation of largescale data processing techniques for the automa... more We describe the design and implementation of largescale data processing techniques for the automatic acquisition of lexical resources for Modern Standard Arabic (MSA) from annotated and unannotated corpora and demonstrate their usefulness for creating a widecoverage, general domain lexicon. Modern lexicographic principles (Atkins and Rundell, 2008) emphasise that the corpus is the only viable evidence that a lexical entry still exists in a speech community. Unlike most available Arabic lexicons which are based on previous historical (and dated) dictionaries, our lexicon starts off with corpora of contemporary texts. The lexicon is encoded in Lexical Markup Framework (LMF) which is a metamodel that provides a standardized framework for the construction of electronic lexical resources. The aim of LMF is to optimize the production and extension of electronic lexicons and to facilitate the exchange of data between all aspects of lexical resources and the interoperability with NLP applications. This lexical resource will serve as the core of our Arabic annotation tools: morphological analysis, tokenization, diacritization and base phrase chunking.
We received 18 submissions, and, given our limited capacity as a one-day workshop, we were only a... more We received 18 submissions, and, given our limited capacity as a one-day workshop, we were only able to accept eight full papers for oral presentation: an acceptance rate of 44%. We further accepted four papers as posters. The regular papers were distributed in three sessions: Lexical Representation, Identification and Extraction, and Applications. The workshop also featured two invited talks, by Kyo Kageura and by Aravind K. Joshi, and a panel discussion.
In this paper we maintain that a unified analysis of the copula constructions in LFG is necessary... more In this paper we maintain that a unified analysis of the copula constructions in LFG is necessary to capture syntactic generalizations. We discuss the various options available in the LFG literature and investigate their feasibility in order to arrive at the most appropriate representation. In doing so, we make use of the concepts and mechanisms already available in the framework of LFG without violating any fixed conditions or breaking with any established conventions. The conclusion we reach is that the different strategies employed in the predicational structures are language-specific variations that basically express the same grammatical function. We assume that the copula complement is a closed function, i.e. PREDLINK, which can account for almost all the different constituents that can occupy the predicate position.
We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity ... more We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWN's instantiable nouns and identify the corresponding categories and hyponym subcategories in AWK. Then, we exploit Wikipedia inter-lingual links to locate correspondences between articles in ten different languages in order to identify Named Entities (NEs). We apply keyword search on AWK abstracts to provide for Arabic articles that do not have a correspondence in any of the other languages. In addition, we perform a post-processing step to fetch further NEs from AWK not reachable through AWN. Finally, we investigate diacritization using matching with geonames databases, MADA-TOKAN tools and different heuristics for restoring vowel marks of Arabic NEs. Using this methodology, we have extracted approximately 45,000 Arabic NEs and built, to the best of our knowledge, the largest, most mature and well-structured Arabic NE lexical resource to date. We have stored and organised this lexicon following the Lexical Markup Framework (LMF) ISO standard. We conduct a quantitative and qualitative evaluation of the lexicon against a manually annotated gold standard and achieve precision scores from 95.83% (with 66.13% recall) to 99.31% (with 61.45% recall) according to different values of a threshold.
Advances in Natural Language Processing, Jan 1, 2006
Multiword expressions (MWEs) vary in syntactic category, structure, the degree of semantic opaque... more Multiword expressions (MWEs) vary in syntactic category, structure, the degree of semantic opaqueness, the ability of one or more constituents to undergo inflection and processes such as passivization, and the possibility of having intervening elements. Therefore, there is no straight-forward way of dealing with them. This paper shows how MWEs can be dealt with at different levels of analysis starting with tokenization, and going through the stages of morphological analysis and syntactic parsing.
This paper presents a study of the impact of using simple and complex morphological clues to impr... more This paper presents a study of the impact of using simple and complex morphological clues to improve the classification of rare and unknown words for parsing. We compare this approach to a language-independent technique often used in parsers which is based solely on word frequencies. This study is applied to three languages that exhibit different levels of morphological expressiveness: Arabic, French and English. We integrate information about Arabic affixes and morphotactics into a PCFG-LA parser and obtain stateof-the-art accuracy. We also show that these morphological clues can be learnt automatically from an annotated corpus.
This paper presents the results of an approach to automatically acquire large-scale, probabilisti... more 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 longdistance 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 language resources from existing treebanks saves time and effort involved in creating such resources by hand. Moreover, data-driven automatic acquisition naturally associates probabilistic information with subcategorization frames and LDD paths. Finally, based on the statistical distribution of LDD path types, we propose empirical bounds on traditional regular expression based functional uncertainty equations used to handle LDDs in LFG.
Abstract We develop an open-source large-scale finite-state morphological processing toolkit (Ara... more Abstract We develop an open-source large-scale finite-state morphological processing toolkit (AraComLex) for Modern Standard Arabic (MSA) distributed under the GPLv3 license (http://aracomlex. sourceforge. net). The morphological transducer is based on a lexical database specifically constructed for this purpose. In contrast to previous resources, the database is tuned to MSA, eliminating lexical entries no longer attested in contemporary use.
ABSTRACT A spelling error detection and correction application is based on three main components:... more ABSTRACT A spelling error detection and correction application is based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in the overall performance of the system.
In recent years, research as well as software applications have been concentrating on Machine Tra... more In recent years, research as well as software applications have been concentrating on Machine Translation (MT). This is due to many factors, the most important of which is the increasing need to create online communication between different parts of the world and between people speaking different languages. People felt the need for machine translation since the advent of computers, but the early attempts they made were completely dissatisfactory.
Abstract Arabic is a language known for its rich and complex morphology. Although many research p... more Abstract Arabic is a language known for its rich and complex morphology. Although many research projects have focused on the problem of Arabic morphological analysis using different techniques and approaches, very few have addressed the issue of generation of fully inflected words for the purpose of text authoring. Available open-source spell checking resources for Arabic are too small and inadequate.
ABSTRACT Unknown words, or out of vocabulary words (OOV), cause a significant problem to morpholo... more ABSTRACT Unknown words, or out of vocabulary words (OOV), cause a significant problem to morphological analysers, syntactic parses, MT systems and other NLP applications. Unknown words make up 29% of the word types in in a large Arabic corpus used in this study. With today's corpus sizes exceeding 109 words, it becomes impossible to manually check corpora for new words to be included in a lexicon.
Abstract We provide lexical profiling for Arabic by covering two important linguistic aspects of ... more Abstract We provide lexical profiling for Arabic by covering two important linguistic aspects of Arabic lexical information, namely morphological inflectional paradigms and syntactic subcategorization frames, making our database a rich repository of Arabic lexicographic details. First, we provide a complete description of the inflectional behaviour of Arabic lemmas based on statistical distribution.
Abstract A morphological analyser only recognizes words that it already knows in the lexical data... more Abstract A morphological analyser only recognizes words that it already knows in the lexical database. It needs, however, a way of sensing significant changes in the language in the form of newly borrowed or coined words with high frequency. We develop a finite-state morphological guesser in a pipelined methodology for extracting unknown words, lemmatizing them, and giving them a priority weight for inclusion in a lexicon.
Abstract We develop an open-source large-scale finitestate morphological processing toolkit (AraC... more Abstract We develop an open-source large-scale finitestate morphological processing toolkit (AraComLex) for Modern Standard Arabic (MSA) distributed under the GPLv3 license. The morphological transducer is based on a lexical database specifically constructed for this purpose. In contrast to previous resources, the database is tuned to MSA, eliminating lexical entries no longer attested in contemporary use.
We build a large-scale, corpus-based lexical database that is representative of Modern Standard A... more We build a large-scale, corpus-based lexical database that is representative of Modern Standard Arabic (MSA). We use a corpus of 1,089,111,204 words, a pre-annotation tool, machine learning techniques, and knowledge-based templatic matching to automatically acquire lexical knowledge about morpho-syntactic attributes and inflection paradigms of each entry. Our lexical database is scalable, interoperable and suitable for constructing a morphological analyser, regardless of the design approach and programming language used. The database is formatted according to the international ISO standard in lexical resource representation, the Lexical Markup Framework (LMF). This lexical database is used in developing an open-source finite-state morphological processing toolkit. We build a system, AraComLex (Arabic Computer Lexicon), for human lexicographers to manage and curate the standardized and scalable lexical database.
We describe the design and implementation of largescale data processing techniques for the automa... more We describe the design and implementation of largescale data processing techniques for the automatic acquisition of lexical resources for Modern Standard Arabic (MSA) from annotated and unannotated corpora and demonstrate their usefulness for creating a widecoverage, general domain lexicon. Modern lexicographic principles (Atkins and Rundell, 2008) emphasise that the corpus is the only viable evidence that a lexical entry still exists in a speech community. Unlike most available Arabic lexicons which are based on previous historical (and dated) dictionaries, our lexicon starts off with corpora of contemporary texts. The lexicon is encoded in Lexical Markup Framework (LMF) which is a metamodel that provides a standardized framework for the construction of electronic lexical resources. The aim of LMF is to optimize the production and extension of electronic lexicons and to facilitate the exchange of data between all aspects of lexical resources and the interoperability with NLP applications. This lexical resource will serve as the core of our Arabic annotation tools: morphological analysis, tokenization, diacritization and base phrase chunking.
We received 18 submissions, and, given our limited capacity as a one-day workshop, we were only a... more We received 18 submissions, and, given our limited capacity as a one-day workshop, we were only able to accept eight full papers for oral presentation: an acceptance rate of 44%. We further accepted four papers as posters. The regular papers were distributed in three sessions: Lexical Representation, Identification and Extraction, and Applications. The workshop also featured two invited talks, by Kyo Kageura and by Aravind K. Joshi, and a panel discussion.
In this paper we maintain that a unified analysis of the copula constructions in LFG is necessary... more In this paper we maintain that a unified analysis of the copula constructions in LFG is necessary to capture syntactic generalizations. We discuss the various options available in the LFG literature and investigate their feasibility in order to arrive at the most appropriate representation. In doing so, we make use of the concepts and mechanisms already available in the framework of LFG without violating any fixed conditions or breaking with any established conventions. The conclusion we reach is that the different strategies employed in the predicational structures are language-specific variations that basically express the same grammatical function. We assume that the copula complement is a closed function, i.e. PREDLINK, which can account for almost all the different constituents that can occupy the predicate position.
We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity ... more We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWN's instantiable nouns and identify the corresponding categories and hyponym subcategories in AWK. Then, we exploit Wikipedia inter-lingual links to locate correspondences between articles in ten different languages in order to identify Named Entities (NEs). We apply keyword search on AWK abstracts to provide for Arabic articles that do not have a correspondence in any of the other languages. In addition, we perform a post-processing step to fetch further NEs from AWK not reachable through AWN. Finally, we investigate diacritization using matching with geonames databases, MADA-TOKAN tools and different heuristics for restoring vowel marks of Arabic NEs. Using this methodology, we have extracted approximately 45,000 Arabic NEs and built, to the best of our knowledge, the largest, most mature and well-structured Arabic NE lexical resource to date. We have stored and organised this lexicon following the Lexical Markup Framework (LMF) ISO standard. We conduct a quantitative and qualitative evaluation of the lexicon against a manually annotated gold standard and achieve precision scores from 95.83% (with 66.13% recall) to 99.31% (with 61.45% recall) according to different values of a threshold.
Advances in Natural Language Processing, Jan 1, 2006
Multiword expressions (MWEs) vary in syntactic category, structure, the degree of semantic opaque... more Multiword expressions (MWEs) vary in syntactic category, structure, the degree of semantic opaqueness, the ability of one or more constituents to undergo inflection and processes such as passivization, and the possibility of having intervening elements. Therefore, there is no straight-forward way of dealing with them. This paper shows how MWEs can be dealt with at different levels of analysis starting with tokenization, and going through the stages of morphological analysis and syntactic parsing.
This paper presents a study of the impact of using simple and complex morphological clues to impr... more This paper presents a study of the impact of using simple and complex morphological clues to improve the classification of rare and unknown words for parsing. We compare this approach to a language-independent technique often used in parsers which is based solely on word frequencies. This study is applied to three languages that exhibit different levels of morphological expressiveness: Arabic, French and English. We integrate information about Arabic affixes and morphotactics into a PCFG-LA parser and obtain stateof-the-art accuracy. We also show that these morphological clues can be learnt automatically from an annotated corpus.
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Papers by Mohammed Attia