Classification of Arabic Information Extraction methods Abd El Salam AL HAJJAR Institute University of Technology Lebanese University Lebanon Paragraph Laboratory University of Paris 8- Vincennes- Saint-Denis France abdsalamhajjar@hotmail.com Mohammad HAJJAR Institute University of Technology Lebanese University Lebanon m_hajjar@ul.edu.lb Khaldoun ZREIK Paragraph Laboratory University of Paris 8- Vincennes- Saint-Denis France zreik@univ-paris8.fr Abstract The performance of information retrieval in arabic language is very problematic due to the specific morphological and structural changes in the language. To extract information from an arabic document, the involved methods must answer the following question: "How can we find the root of the word we search". To find a word in an arabic dictionary, first you must extract the root of this word and then find this root in the dictionary. This is because the vocabulary of the arabic language is essentially built from the roots derivation. The roots are words composed of three to five consonants letters. To address these problems, several methods have been proposed. The aim of this paper is to propose a preliminary classification of arabic information extraction methods. These methods can be classified into two main categories. The first one is called "Stemmer". This category includes the following subcategories: Stemmer based on affixes, Stemmer based on translation and Stemmer based on pattern and affixes. The second is called "N-gram". This category regroups the subcategories: N-gram based on Dice's similarity coefficient and N-gram based on “Manhattan distance” dissimilarity coefficient. However, we find methods which implement the two approaches "Stemmer" and "N-gram". This work contributes to decide on the more appropriate arabic information extraction method. 2005, Khoja & Garside, 1999; Hammo et al., 2002). The second is called "N-gram". It based on statistical approaches to retrieve the information independently of the language complexity (Adamson George & Boreham, 1974; Suleiman Mustafa, 2004; Ahmed & Nürnberger, 2007; M. Sinane et al., 2008; Khreisat, 2006). However, we find methods which implement the two approaches "Stemmer" and "N-gram" (De Roeck & Al-Fares, 2000). Introduction Arabic language is used by more than 330 million arabic speakers that are spread over 22 countries (Ghosn, 2003; Censure of the Internet in the arab countries, 2006). However, the performance of information retrieval in arabic language is very problematic due to the specific morphological and structural changes in the language: polysemy, irregular and inflected derived forms, various spelling of certain words, various writing of certain combination character, short (diacritics) and long vowels, most of the arabic words contain affixes (Table 1, 2). To address these problems, several methods have been proposed. The aim of this paper is to propose a preliminary classification of arabic information extraction methods. These methods can be classified into two main categories. The first one is called "Stemmer" which requires specific knowledge about the language (Al Ameed et al., 2005; Larkey et al., 2002; Larkey, 2005; Darwish, 2002; Chen & Gey, 2002; Kanaan et al., 2004; Thabet, 2004; Kadri & Nie, 2006; Al-Shalabi & Evens, 1998; Taghva et al., Problematic To find a word in an arabic dictionary, first we must extract the root of this word and then find this root in the dictionary (Ibn Manzour, 2008). This is because the vocabulary of the arabic language is essentially built from the roots derivation. The roots are words composed of three to five consonants letters. The arabic language has about ten thousand roots, 85% of them are trilateral. The derivation of words is done by adding affixes (prefix, infix, or suffix) to the root according to several patterns that are around 120 (Al Kharashi, 1999). For example, let us take the root ( ); the words ( , , ) (Table 71 3) are respectively derived from this root according to the patterns ( , , ) (Table 4). To extract information from an arabic document, the involved methods must answer the following question: "How can we find the root of the word we search". To answer this question, we must perform a morphological analysis. In the arabic language, this consists to identify the morphemes of a word (Stem): the affixes (prefix, infix, and suffix) and the root. A stem can be a noun, verb or particle. It can be composed of: One part (a root, for example: ( )); Two parts (a root + a pattern, for example: ( ): root ( ) + a pattern (CuCiC where C is the consonants of the root (the radicals)); Three parts (a root + a pattern + affixes, for example: ( ‫ا‬ ‫)ا‬: root ( ) + a pattern CaCiC + affixes (prefix (al) ( ‫)ا‬, infix (a) (‫ )ا‬and the suffix ( a) ( ))) (Table 2, 3). system. This category includes the following subcategories: Stemmer based on affixes, Stemmer based on translation and Stemmer based on pattern and affixes. Stemmer based on affixes Several Stemmer algorithms use the predefined rules to remove the affixes (prefix, infix, suffix ...) from the word to extract the root. This category allows remarkably good information retrieval without providing correct morphological analysis. Several algorithms have been developed (Al Ameed et al., 2005; Larkey et al., 2002; Larkey, 2005; Chen & Gey, 2002; Kanaan et al., 2004; Thabet, 2004; Kadri & Nie, 2006). The normalization phase is applied before applying these algorithms (Replacing “ ‫”أ‬, “‫ ” إ‬and “ ‫ ”آ‬by alif bar “‫”ا‬, Replacing “ ” by “ ‫ ”ي‬at the end of the words, Replacing “ ” by “ ” at the end of the words, Replacing the sequence “ ‫ ” يء‬by “‫”ئ‬, etc.) (Table 1). Al Ameed, H. et al. (2005) and Larkey, L. et al.(2005) developed a light stemmer which is based on the suppression of “ ” if it is initial at the beginning of the word, of the prefixes ( , , , , ‫ ا‬, ‫)ا‬, and of the suffixes ( , ‫ ي‬, , ‫ ا‬, ‫ا‬, ‫ ي‬, , , ‫ يي‬, ‫)ي‬. A. Chen and F. Gey (2002) identified other sets of prefixes and suffixes. To remove the prefixes and suffixes in the pre-defined sets, each algorithm proposes their own rules. For example, A. Chen and F. Gey (2002) apply the following rules: If the word is at least five-character long, remove the prefixes of three characters: , ‫ اا‬, , , , , , ‫ا‬ ‫ ا‬, . If the word is at least four-character long, remove the first two characters: ‫ ا‬, ‫ا‬, , ,‫ ي‬,‫ ا‬, , , , , , , ,‫ ي‬. If the word is at least four-character long and begins with remove the initial letter . If the word is at least four-character long and begins with either or remove or (Table 2). Kanaan et al. (2004) presented a new stemming algorithm to extract quadrilateral arabic roots. The algorithm starts by excluding the prefixes, and then checks the word characters starting from the last letter backward to the first one. A temporary matrix is used to store the suffix letters of the arabic word, and another matrix is used to store the roots. Algorithm checks the letters of any word, also checking whether the tested letter is included within the general standard arabic word. The large-scale use of diacritics ( , , , , , , , ) (Table 1) representing short vowels are prevalent in the Qur’an. Every word, even every letter is marked with a diacritic. (For example: "reign", “king" ...). N. Thabet (2004) proposes a new stemming approach based on a light stemming technique that uses a transliterated version of the Qur’an in western script (Table 3). Y. Kadri and J. Nie (2006) defined that the arabic words are usually formed as a sequence of antefixes are generally prepositions joined to words at the beginning ( ..., , , , ‫ ا‬, ), prefix are usually represented by only one letter and indicate the conjugation person of verbs in the present tense ( ... ,‫ ي‬, ,‫( )ا‬Table 1), core, suffixes are the conjugation terminations of verbs and they are the dual/plural/female marks for the nouns ( , ‫ ي‬, ... ‫ ا‬, ‫ ا‬, , ‫) ي‬, and postfixes represent pronouns attached to the end of the words(... , , ) ) (Table 2). Results The study that we realized permits to identify several methods which address the problems of information extraction from arabic documents. We have found that these methods can be classified into three categories: "Stemmer", "N-gram", and "Stemmer and N-gram". The first category requires specific knowledge about the language. The second is based on statistical approaches to retrieve the information independently of the language complexity. In the last category we find methods which implement the two approaches "Stemmer" and "N-gram". The diacritics and the variation of the letter forms according to its positions take an important role in the arabic reading and writing complexity and reduce the Arabic Information Extraction methods performance. To resolve these problems, the normalization phase is applied before applying these methods, the text normalization takes a character string as input and tries to remove or replace some characters under the predefined rules to converts it into a string of letters (Figure 1). Every method has the specific rules, in general a text is normalized by removing (the tatweel character “-“, the diacritics and the shedda “ ”, the punctuations, the non letters, the stop words, the specials characters, and the numbers) and replacing (“‫”أ‬, “‫ ”إ‬and “‫ ”آ‬by alif bar “‫”ا‬, “ ” by “ ‫ ”ي‬at the end of the words, “ ” by “ ” at the end of the words, “ ‫ ” يء‬by “‫”ئ‬, ‫ ؤ‬by ‫ ء‬, and ….) (Chen, A. & Gey, 2002; Kadri & Nie, 2006; Khreisat, 2006; Larkey et al., 2002; Larkey, 2005; Douzidia & Lapalme, 2005; Darwish, 2002). Figure 1: Normalization Process Stemmer Category Stemmer is an automatic process used to reduce the different morphological forms of words into common root (Stem) to improve the performance of the extraction 72 N-gram Category Stemmer based on translation The algorithms of english Stemmer have better performance than arabic. For that, several methods use the translation technique to allow any languages (like arabic) use the Stemmer of another language (like english) to extract the root of a word. A. Chen and F. Gey (2002) have built a MT-based arabic stemmer from the arabic words found in the arabic documents, and their english translations are using the online “Ajeeb” machine translation system. They divided the arabic words into clusters based on the english translations of the arabic words. The arabic words whose english translations, after removing english stop words, are conflated to the same english stem that made from one cluster. All the arabic words in the same cluster are conflated to the same arabic word, which is the shortest arabic word in the cluster. For example: ‫( أ‬our children), remove "our" is a word parasite, ‫ أ‬is apparent that in relation to "child". So ‫ أ‬is related to (Table 3). The second category is based on statistical approaches: Ngram. The N-gram can find if two words are semantically similar or dissimilar from the structures of characters of these words. Two words are considered similar if they have in common several substring of N characters, this is done by calculating a coefficient on these two words. The advantages of N-gram are that it does not require a preliminary knowledge of the language, does not require predefined rules, and does not require the construction of a database of vocabulary. This class gives a good result in multiple languages, even in arabic (3-gram and 4-gram). This category regroups the subcategories: N-gram based on Dice's similarity coefficient and N-gram based on “Manhattan distance” dissimilarity coefficient. In this category you can find the following classes: N-gram based on the calculation of similarity coefficient W. Adamson George and J. Boreham (1974) has been developed the first automatic classification technique based on the character structure of words. Dice's Similarity Coefficient is computed from the number of matching bigrams (2-gram) in pairs of character strings, and used to cluster sets of character strings (Figure 2). H. Suleiman Mustafa (2004) assesses the performance of two N-gram matching techniques for arabic root-driven string searching: contiguous N-grams and hybrid Ngrams, combining contiguous and non-contiguous. F. Ahmed and A. Nürnberger (2007) have been presented the n-gram model which can be used to compute the similarity between two strings by counting the number of similar n-grams they share. The more similar n-grams they found between the two strings exist the more similar they are. Based on this idea the similarity coefficient can be derived. The similarity coefficient δ is defined by the following equation: Stemmer based on pattern and affixes Several Stemmer algorithms based on the patterns and affixes have been developed, to find roots with three letters, four and five letters, starting from verbal forms, nouns and adjectives derived from verbs (Khoja & Garside, 1999; Hammo et al., 2002; Al-Shalabi & Evens, 1998; Taghva et al., 2005; Darwish, 2002). S. Khoja and R. Garside (1999) have proposed a method that involves removing diacritics representing vowelization, the stop words, the punctuation, the numbers, the definite article ( ‫)ا‬, the inseparable conjunction ( ), and the longest prefix and suffix. Then, the result is compared to a list of patterns. If a match is found, the characters representing the root in the pattern are extracted (Table 1, 2). The QARAB system is developed by B. Hammo et al. (2002) is based on the Khoja Stemmer (Khoja & Garside, 1999). R. Al-Shalabi and Mr. Evens (1998) proposed an approach of Stemmer where the first step is to remove the longest possible prefix. The three letters of the root must lie somewhere in the first four or five characters of the remainder. They checked all the possible trigrams within the first five letters of the remainder. Then, they check the following six possible trigrams: First, second, and third letters. First, second, and fourth. First, second, and fifth, etc. K. Taghva et al. (2005) implemented a root-extraction stemmer for arabic which has a performance equivalent to the Khoja stemmer (Khoja & Garside, 1999). To implement this algorithm, they have defined several sets of the affixes (D diacritic: ... , , , , P3 prefix of length 3: ‫ ا‬, . P2 prefix of length 2: , ‫ا‬. P1 prefix of length 1: , , . S3 suffix of length 3: , , . S2 suffix of length 2: ‫ ا‬, ‫ ا‬, . S1 suffix of length 1: , , ‫()ي‬Table 1, 2) and several sets of models (PR4 model of length 4: , , . PR53 model of length five and a root of length 3: ... ‫ أ‬, ‫ ا‬, . PR54 model of length five and a root of length four: , ‫ا‬, . PR63 model of length 6 and a root of length 3: ‫ا‬ ... ‫ا‬. PR64 model of length 6 and a root of length 4: ... , ‫( )ا ا‬Table 4). The extraction of the root in this algorithm is based on the length of the normalized stem. Where α and β are the n-gram sets. For Example: shows an example of two arabic words: ‫ا ي‬ ‫ اإ‬and ‫ا‬ ‫( ا‬Table 3): ‫ا ا‬ ‫ي‬ ‫ي‬ ‫ا ا‬ ‫إ‬ ‫إ إ‬ ‫ا‬ Figure 2: Bigram similarity measure between two words ‫ا ي‬ ‫ اإ‬and ‫ا‬ ‫ا‬ M. Sinane et al. (2008) presented an approach that uses Ngram based on the word and characters. Four basic types have been explored, sometimes separately and sometimes in combination: Word, lexical root, root, and N-gram. In general, N-grams based on the stems are better than those based on words, because the N-grams based on words could have prefixes and suffixes which make more mistakes in the Similarity between the document and query. 73 N-Gram based on the Frequency Statistics technique L. Khreisat (2006) presented the N-Gram Frequency Statistics technique for classifying arabic text documents. The technique employs a dissimilarity measure called the “Manhattan Distance”, and “Dice’s measure”. A corpus of arabic text documents was collected from online arabic newspapers, 40% of the corpus was used as training classes and the remaining 60% of the corpus was used for classification. All documents, whether training documents or documents to be classified went through a preprocessing normalization phase that remove the punctuation marks, the stop words, the diacritics, and the non letters. For the training documents, the N-gram (N=3) (the trigrams of the word ‫ي‬ ‫ ا‬are: ‫ ي‬,‫ ي‬, , , ‫)ا‬ (Table 2) was generated for each document and saved in text files. Then for each document to be classified, the Ngram frequency profile was generated and compared against the N-gram frequency profiles of all the training classes. This comparison is done by calculating Manhattan distance and Dice’s measure. A. N. De Roeck and W. Al-Fares (2000) presented a method for arabic words sharing the same root. To implement this method "Clustering Algorithm", depends on two stages which is called "Two-Stage". In first step, they applied the Light Stemming to remove affixes, the second step is based on the Adamson algorithm with some modifications. Each bi-gram assigned a weight (0.25 for bi-gram containing low letter, 0.5 for bi-gram containing the non-low letter, 1 for all other bi-gram). Conclusion In this article we have proposed a first classification of arabic information extraction methods into three categories: Stemmer, N-gram, and "Stemmer" and "Ngram". In the stemmer category we find the following subcategories: Stemmer based on affixes, Stemmer based on translation, and Stemmer based on pattern and affixes. In the N-gram category we find the following subcategories: N-gram based on Dice's similarity coefficient and N-gram based on “Manhattan distance” dissimilarity coefficient. However, we find a method which implements the two approaches "Stemmer" and "Ngram". The next step will be the making of a detailed comparative study of the early described categories. This study will cover mainly the following topics: performances, stabilities, usability, advantages, and disadvantages. Another possible extension of the present work is to test these categories in similar conditions. These studies and tests will permit to designate the more appropriate arabic information extraction method or to propose a new one. Category: Stemmer and N-gram Each of the two approaches has advantages and disadvantages, as long as the Stemmer approach depends on the language, and its morphological complexity, and always does not give the best performance... and the statistical approach N-gram is independent of language but has drawbacks in terms of synonyms. To do this, there are authors trying to merge the two approaches, in order to have a good method. Letter ~ ‫ء‬ ‫ا‬ ‫إ‬ ‫أ‬ ‫آ‬ Transcription Tanween Fatha Tanween Dama Tanween Kasra Fatha Dama Kasra Sokon Shedda Maada Hamza Alef Alef with Hamza on bottom Alef with Hamza on top Alef with Maada Baa Taa Marbouta Taa Tha Jeem H'a Khaa Dal Zain At Begin ‫ـ‬ X ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ Writing In Middle ‫ــ‬ X ‫ــ‬ ‫ــ‬ ‫ـ ـ‬ ‫ـ ـ‬ ‫ـ ـ‬ ‫ـ‬ ‫ـ‬ At End ‫ـ‬ X ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ Letter ‫ؤ‬ ‫ي‬ ‫ئ‬ Transcription Raa Thal Seen Sheen Saad Daad T'aa Zha Ain Jain Faa Qaf Kaf Lam Meem Noon Haa Waw Hamza on waw Alif Makzora Yaa Hamza on yaa At Begin ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ X X ‫يـ‬ ‫ئـ‬ Writing In Middle ‫ـ‬ ‫ـ‬ ‫ـ ـ‬ ‫ـ ـ‬ ‫ـ ـ‬ ‫ـ ـ‬ ‫ـ ـ‬ ‫ـ ـ‬ ‫ــ‬ ‫ــ‬ ‫ــ‬ ‫ــ‬ ‫ــ‬ ‫ــ‬ ‫ــ‬ ‫ــ‬ ‫ــ‬ ‫ـ‬ ‫ـ‬ X ‫ـيـ‬ ‫ــ‬ At End ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ ‫ـ‬ Table1: Arabic diacritics and letters transcription. Empty case means no writing change in the corresponding letter and position. X-case means no existing of the corresponding letter 74 ‫ـي‬ ‫ـئ‬ Affix ‫اا‬ ‫ا‬ ‫ا‬ ‫ا‬ ‫ي‬ ‫ي‬ ‫ا‬ ‫ا‬ ‫إ‬ ‫ا‬ ‫ي‬ ‫ي‬ Transcription Alef Alef Lam Alef Taa Alef Lam Alef Noon Baa Alef Lam Taa Meem Dal Ain Yaa Saa Sokon Saa Alef Meem Ain Yaa Noon Faa Alef Lam Kaf Alef Lam Kaf Noon Lam Alef Lam Alef Lam Lam Alef with Hamza on bottom Lam Lam Meem Alef Lam Meem Waw Dal Haa Alef Haa Meem Alef Haa Meem Lam Waw Alef Lam Waw Baa Waw Taa Waw Dal Ain Waw Seen Waw Lam Lam Waw Meem Waw Noon Waw Yaa Yaa Noon Affix ‫ي‬ ‫ا‬ ‫ا‬ ‫ا‬ ‫ي‬ ‫ي‬ ‫ا‬ ‫يي‬ ‫ا‬ ‫إ‬ ‫ا‬ ‫ي‬ ‫ا‬ ‫ي‬ ‫ي‬ Transcription Yaa Waw Noon Alef Taa Alef Lam Meem Alef Noon Baa Alef Baa Alef Lam Taa Alef Noon Taa Meem Alef Taa Yaa Noon Seen with Fatha Seen Yaa Faa Alef Kaf Alef Lam Kaf Meem Alef Waw Alef Lam Waw Baa Alef Lam Yaa Yaa Taa Marbouta Alef Raa Alef with Hamza on bottom Seen Taa Meem Taa Meem Lam Raa Alef Raa Yaa Seen with Dama Seem Taa Kaf Alef Lam Meem Raa Waw Alef Waw Lam Waw Noon Yaa Taa Marbouta Yaa Haa Table2: Arabic Affix Transcription cited in this paper and their transcription Word ‫ا‬ ‫ا‬ ‫ا‬ ‫ا‬ ‫ا‬ ‫ا‬ ‫ا ي‬ ‫ي‬ ‫أ‬ ‫اإ‬ ‫ا‬ ‫ا‬ Transcription Kateb Aleidwan Alharb Almaaraka Kataba Estemrar Tofol Kateba Malek Maktob Estemrareya Al modeoon Atfalona Muluk Alkateboun Translation Writer Attack War Battle Write Continuity Child Writer King Written Continuities Depositors Our children Had Writers Table3: Arabic words cited in this paper, their transcription, and their translation 75 Pattern ‫ا‬ ‫ا‬ ‫أ‬ Transcription Eftaala Afaalal Afaal Faaela Faaol Mafool Efteaal Moftaeel Mostafeel Mafaala Mafaalal Iestafaal Afaalal Tafaool Tafaalal Faeel Faaela ‫ا‬ ‫ا‬ ‫ا ا‬ Table4: A sample of arabic pattern cited in this paper and their transcription identifying Arabic roots". 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