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ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction

There is a massive amount of different information and data in the World Wide Web, and the number of Arabic users and contents is widely increasing. Information extraction is an essential issue to access and sort the data on the web. In this regard, information extraction becomes a challenge, especially for languages, which have a complex morphology like Arabic. Consequently, the trend today is to build a new corpus that makes the information extraction easier and more precise. This paper presents Arabic linguistically analyzed corpus, including dependency relation. The collected data includes five fields; they are a sport, religious, weather, news and biomedical. The output is CoNLL universal lattice file format (CoNLL-UL). The corpus contains an index for the sentences and their linguistic meta-data to enable quick mining and search across the corpus. This corpus has seventeenth morphological annotations and eight features based on the identification of the textual structures help to recognize and understand the grammatical characteristics of the text and perform the dependency relation. The parsing and dependency process conducted by the universal dependency model and corrected manually. The results illustrated the enhancement in the dependency relation corpus. The designed Arabic corpus helps to quickly get linguistic annotations for a text and make the information Extraction techniques easy and clear to learn. The gotten results illustrated the average enhancement in the dependency relation corpus.


Introduction
Resources, such as corpora, are essential for researchers working on Arabic Natural Language Processing (NLP) [1,2]. One of the most critical problems in the corpora is a general weakness and low accuracy that refer to the complexity of the Arabic langue that has more morphologies than other languages [3]. There are some limitations in the number of available Arabic linguistic resources for research purposes; all of them are not including dependency relation and the scale of the datasets not sufficiently large [4]. Which, in turn, led to obtaining the degree of efficiency required in most of the research related to the Arabic NLP, especially the researches related to the information extraction process. The Information extraction is considered as a base of researches interested in the classifyingpolarity sentiments, summarizing the reviews, and classifying the emotions or opinion mining depending on the dependency parsing corpus. The most recent research in NLP in universal dependency have achieved promising results in different applications and languages [5]. Wherefore, this research aims at taking advantage of the universal dependency annotation in Arabic by making an available, revised and reliable corpus to use in the future.
In recent years, there are several Arabic treebank and corpora that have been introduced. However, they did not include dependency except the treebank in The Linguistic Data Consortium (LDC) which available at [6] and not free available there has another corpus. However, it does not include dependency and is used for a social subject like sentiment and classification [7]. Wherefore, this paper presents a new corpus includes mainly on the dependency parsing. Arabic text essays from the web have been collected in five different fields they are a sport, religion, weather, news, and biomedical. After that, for producing CoNLL-U formatted files, UDPipe tool is used for completing the

Related Work
Recently, number of Arabic corpora types is presented such as Raw Text Corpora, Annotated Corpora, Lexical Databases Speech Corpora and handwriting. Most of the corpora are designed to be used in specific Arabic natural language tasks such as optical character recognition for both handwriting and printing text [10], and speech recognition [11]. Also, ARALEX online is a dataset interested in the morphology and steam of the word which is used in translation [12]. Furthermore, QUWI aims to develop the database in size to make it more advanced in the handwriting [10]. Saad et al. presented a method based on transfering annotation from language to another with subjective/objective labels. They collected dataset from Wikipedia and euro-news website in English and Arabic. The experiments show that there are distinction between subjective/objective texts [13].
Alsolamy et al. built manually corpus-based sentiment lexicon for Arabic opinion Mining [7]. Furthermore, Gamal et al. constructed a benchmark dataset of Arabic Dialect Tweets [14]. Zaghouani et al. built Annotation Procedure that consists annotation management and MT post-editing annotation for Modern Standard Arabic corpora Machine Translation. [15]. Also, there is Arabic Scripts Dataset for writer identification [16]. It is also, there is annotated corpora that useful for corpus-based building automatic spelling correction tools [17]. Although these corpora are useful for different Arabic NLP tasks, there is not any available revised dataset for the Arabic dependency parsing that could be used in Arabic information extraction [16].

Dependency Annotation Scheme
The Arabic language accommodates more than one of noun compounds named the nested noun compound, which makes the technique of extraction extra difficult [18]. Many parsers for Arabic langue have been implemented such as;In MaltParser, Each token has five characteristics in MaltParser as follows: 1) word's ordinal position in the sentence, 2) part-of-speech (POS) tag, 3) form of word, 4) parent word identifer, and 5) the current word and its parent dependency relation (deprel) [19].
Stanford Arabic parser uses Buckwalter Arabic morphological analyzer (Bama) to complete segmentation. The strategy of Bama system is concatenative lexicon-driven. This system contains the lexicon, the compatibility tables and the analysis engine [20]. In bama, the pronouns, prepositions and conjunctions clitics are almost separated off as separate words. However, Inflectional morphology, derivational morphology and the clitic determiner "Al" ‫)ال(‬ is not separated off. So, Stanford Arabic Parser augments "augmented Bies" tag set for representing words that starts by the determiner "Al" ‫)ال(‬ and uses extra tags. these extra tags appear for all parts of speech (NN, CD, etc.) preceded by determiner (DT). To the best of our knowledge, there is no typed dependencies (grammatical relations) analysis available for Arabic from an architectural perspective; Stanford CoreNLP does not attempt to do everything. It is nothing more than a straightforward pipeline architecture [21] [22].
Farasa parser uses a Support vector machine (SVM) for ranking [23]. In [23], The authors used Farasa Base and Farasa Lookup. The concatenated stop-words list is used in Farasa Base and all words are directly segmented by the classifier. In Farasa Lookup FarasaLookup, those words that were previously segmented or appeared more than four times during training are stored, but the unseen words are classified.
UDPipe used the Columbia Arabic Conversion Tool. It creates cross-linguistically consistent annotation guidelines that facilitate the creation of treebanks and the structural basis and the same label sets. The trainable pipeline (UDPipe) splits the sentence into segments and tokens, removes affixes and produces lemma (dictionary form ) of all tokens, defines POS tagging and generates dependency parsing for sentence [24]. Currently, UDPipe solves the problem by inserting a module consisting of a trivial lookup in the dictionary of multiword tokens which is generated from training data [25].

Building Corpora
The building of this corpus built is a preliminary work for intended future research in Arabic NLP. The ADPBC consists of comma-separated values (CSV) file in the CoNLL universal lattices (CoNLL-UL) format that is created using text documents collected from the web, after that some processes have been done as illustrated in Fig.1 for This corpus contains the words and their dependency relation produced by performing some steps. Firstly, segment the text sentence to individual words to produces the word-token, the second stage is performing a number of linguistic processes, which are POS Tagging and Lemmatization. POS tagging is the process of label every word in the text by its grammatical category and is automatically performed using the POS tagger technique [26], [27]. Lemmatization is a process for recovering the lemma from the word-surface form [28]. Secondly, performing the dependency parsing step which facilate the parsing of large scale text which is important to be accurate and fast.
In this stage, we used the Arabic PADT and Farasa model to performed POS Tagging and Lemmatization. The Arabic PADT (Prague Arabic Dependency Treebank) contains a multi-level description comprising functional morphology, analytical dependency syntax, and text grammatical representation of linguistic meaning. The PADT morphology succeeds to determine all of the contextual and lexical parameters [29]. Farasa has a new Arabic segmentation package that is more efficiency and faster than Stanford's Arabic segmenter and is used in this step. Finally, the output has been converted into CoNLL format as illustrated in Fig. 2 [30].
In the next step, manual checks have been performed for the corpus including the POS tagging and morphological annotation. As an example, the corresponding dependency-parsing tree for the sentence "The Russian authorities recommended that they leave the village of Nyonoxa days after an explosion of a nuclear nature" is illustrated in Fig. 2 and Table 1 illustrates the CONLL-U file format for the sentence. Dependency trees (DT) are real word order for representing the relations between head and dependent in the sentence. DT outputs common tag set that used for various languages, It starts from the verb that is the root of the sentence that connected with three nouns and these nouns connected by other adjectives and adverbial. The good dependency tree is simply a tree with the appropriate nodes, their nodes map, one to one, to the tokens as a result of the morphological analysis and tokenization, and their roots collect the nodes according to the division into sentences or paragraphs. The edges comply with the labels and valency limitations determined by the lexical entries. The task of conversion for these trees was easy as the linguistic representation was already what we want [31].

Copyright © 2021 MECS
I.J. Information Technology and Computer Science, 2021, 1, 54-61 As shown in Table 1, the output contains sent_id: the sentence identifier, the text of the sentence (text) for which this token is part of and eight features; These features are: 1) word index (Id), integer starting at 1 for each new sentence; maybe a range for multiword, 2) Word form or punctuation symbol (Form), 3) stem or lemma of word form (Lemma), 4) Universal part-of-speech tag (UPos Tag), 5) particular language-part-of-speech tag (XPos Tag); which is underscore if not longer available, 6) morphological features list of the universal feature inventory (Feats), language-specific extension; which is underscore if not available, 7) the current word identification (Head) that takes zero valus or a value of word index (Id) and 8) Dependency Relation to the Head (DepRel), which is root if Head = 0 or defined depending on the treebank [32]. Universal Dependency Relation has several types, as shown in Table 2:   The Universal Dependencies framework has been chosen because it provide a comparable format and help in matching the different types of dependency relations in various languages. There are seventeen types of dependency relation have been provided by parsers trained on Arabic-padt-ud-2.4-data. For example; the subject and object, adnominal clauses, conjunction, relative, auxiliary, adverbial, and parataxis [26].

Confusion Matrix Evaluation
This section presents an evaluation of conducted corpus. UDPipe can produces errors like any parser, and it was essential to assess the output to configure and estimate these mistakes [26]. The evaluation is performed manually, and a confusion matrix has been conducted for the 17 types of dependency parser annotation. The confusion matrix compares between the data was yielded from the UDPipe model and manually corrected data. The results show the mismatching accrue between the root and Nmod, nsubj and OBJ and Amod and case. The results in Table 3 illustrates the enhancement in the corrected data. This corpus contains seventeenth morphological annotations, as illustrates in Fig. 3 where identify the textual forms of a discourse lexically and recognize their grammatical properties. The analytical syntactical process describes the superficial dependency structures within the discourse, whereas the text grammatical illustration and detects the underlying dependency structures and restores the meaning of linguistic. Annotations on the analytical level are described and represented by dependency trees. The most frequent relation errors are mismatched between different dependency relation annotations, for example, the root and Nmod, root and Nsbj, root and obj, Parataxis and root, Parataxis and Adv, Obj and Nmod.
The ADPBC dataset is viable to start simple large-scale experiments with syntax-aware models while it is not required for long and resource-intensive preprocessing. This data set is uploaded online and its public to modified and improvement. The confusion matrix result indicates that the dataset is suitable for the future researcher in Arabic, especially in information extraction. Various domains and other opinion mining topics are covered into our corpus. The extra meta-data information of a document can be used in various Arabic natural language processes such as machine translation. The system tested on five categories of documents that performed from them, and the average number of sentences per document is 25 sentences. The average number of words per sentence is 15 words.

Conclusion and Future Work
This paper presented ADPBC corpus, which is automatically annotated using UDPipe model then manually corrected. The text has been extracted from the web. After Spell checking is performed for the text, the parsing process is applyed. The non syntax errors will not affect the syntactic complexity evaluation. The paper contains features used in the evaluation of automatic dependency parsing. The corpus will be utilized in numerous contexts starting from information extraction as well as the core of all Arabic natural language process to unsupervised induction of word senses and frame structures.
Additionally, it has several characteristics as well as morphological annotation depends on. That main valuable property is dependency parsing that makes availability to improving the Late in performance in overall ANLP. Finally, the results indicated that the annotation and the parsing processes will be improving. In the future work and the conducted corpora will use mainly in intended future research interested on the information extraction.