Towards an automatic extraction of synonyms for Quranic Arabic WordNet

In this paper, we developed an automatic extraction model of synonyms, which is used to construct our Quranic Arabic WordNet (QAWN) that depends on traditional Arabic dictionaries. In this work, we rely on three resources. First, the Boundary Annotated Quran Corpus that contains Quran words, Part-of-Speech, root and other related information. Second, the lexicon resources that was used to collect a set of derived words for Quranic words. Third, traditional Arabic dictionaries, which were used to extract the meaning of words with distinction of different senses. The objective of this work is to link the Quranic words of similar meanings in order to generate synonym sets (synsets). To accomplish that, we used term frequency and inverse document frequency in vector space model, and we then computed cosine similarities between Quranic words based on textual definitions that are extracted from traditional Arabic dictionaries. Words of highest similarity were grouped together to form a synset. Our QAWN consists of 6918 synsets that were constructed from about 8400 unique word senses, on average of 5 senses for each word. Based on our experimental evaluation, the average recall of the baseline system was 7.01 %, whereas the average recall of the QAWN was 34.13 % which improved the recall of semantic search for Quran concepts by 27 %.     

Supervised collaboration for syntactic annotation of Quranic Arabic

The Quranic Arabic Corpus (http://corpus.quran.com) is a collaboratively constructed linguistic resource initiated at the University of Leeds, with multiple layers of annotation including part-of-speech tagging, morphological segmentation (Dukes and Habash 2010) and syntactic analysis using dependency grammar (Dukes and Buckwalter 2010). The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400 year-old central religious text of Islam. This project contrasts with other Arabic treebanks by providing a deep linguistic model based on the historical traditional grammar known as i′rāb (?????). By adapting this well-known canon of Quranic grammar into a familiar tagset, it is possible to encourage online annotation by Arabic linguists and Quranic experts. This article presents a new approach to linguistic annotation of an Arabic corpus: online supervised collaboration using a multi-stage approach. The different stages include automatic rule-based tagging, initial manual verification, and online supervised collaborative proofreading. A popular website attracting thousands of visitors per day, the Quranic Arabic Corpus has approximately 100 unpaid volunteer annotators each suggesting corrections to existing linguistic tagging. To ensure a high-quality resource, a small number of expert annotators are promoted to a supervisory role, allowing them to review or veto suggestions made by other collaborators. The Quran also benefits from a large body of existing historical grammatical analysis, which may be leveraged during this review. In this paper we evaluate and report on the effectiveness of the chosen annotation methodology. We also discuss the unique challenges of annotating Quranic Arabic online and describe the custom linguistic software used to aid collaborative annotation.     

Arabic question answering system: a survey

Question answering is a subfield of information retrieval. It is a task of answering a question posted in a natural language. A question answering system (QAS) may be considered a good alternative to search engines that return a set of related documents. The QAS system is composed of three main modules; question analysis, passage retrieval, and answer extraction. Over the years, numerous QASs have been presented for use in different languages. However, the the development of Arabic QASs has been slowed by linguistic challenges and the lack of resources and tools available to researchers. In this survey, we start with the challenges due to the language and how these challenges make the development of new Arabic QAS more difficult. Next, we do a detailed review of several Arabic QASs. This is followed by an in-depth analysis of the techniques and approaches in the three modules of a QAS. We present an overview of important and recent tools that were developed to help the researchers in this field. We also cover the available Arabic and multilingual datasets, and a look at the different measures used to assess QASs. Finally, the survey delves into the future direction of Arabic QAS systems based on the current state-of-the-art techniques developed for question answering in other languages.

     

Sentence alignment using P-NNT and GMM

Parallel corpora have become an essential resource for work in multilingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross-language information retrieval and machine translation applications. In this paper, we present two new approaches to align English–Arabic sentences in bilingual parallel corpora based on probabilistic neural network (P-NNT) and Gaussian mixture model (GMM) classifiers. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the probabilistic neural network and Gaussian mixture model. Another set of data was used for testing. Using the probabilistic neural network and Gaussian mixture model approaches, we could achieve error reduction of 27% and 50%, respectively, over the length based approach when applied on a set of parallel English–Arabic documents. In addition, the results of (P-NNT) and (GMM) outperform the results of the combined model which exploits length, punctuation and cognates in a dynamic framework. The GMM approach outperforms Melamed and Moore’s approaches too. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in Japanese–Chinese texts, than the ones used in the current research.     

Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches

Sentiment polarity detection is one of the most popular tasks related to Opinion Mining. Many papers have been presented describing one of the two main approaches used to solve this problem. On the one hand, a supervised methodology uses machine learning algorithms when training data exist. On the other hand, an unsupervised method based on a semantic orientation is applied when linguistic resources are available. However, few studies combine the two approaches. In this paper we propose the use of meta-classifiers that combine supervised and unsupervised learning in order to develop a polarity classification system. We have used a Spanish corpus of film reviews along with its parallel corpus translated into English. Firstly, we generate two individual models using these two corpora and applying machine learning algorithms. Secondly, we integrate SentiWordNet into the English corpus, generating a new unsupervised model. Finally, the three systems are combined using a meta-classifier that allows us to apply several combination algorithms such as voting system or stacking. The results obtained outperform those obtained using the systems individually and show that this approach could be considered a good strategy for polarity classification when we work with parallel corpora.     

Automatic Prosodic Event Detection Using Acoustic, Lexical, and Syntactic Evidence

With the advent of prosody annotation standards such as tones and break indices (ToBI), speech technologists and linguists alike have been interested in automatically detecting prosodic events in speech. This is because the prosodic tier provides an additional layer of information over the short-term segment-level features and lexical representation of an utterance. As the prosody of an utterance is closely tied to its syntactic and semantic content in addition to its lexical content, knowledge of the prosodic events within and across utterances can assist spoken language applications such as automatic speech recognition and translation. On the other hand, corpora annotated with prosodic events are useful for building natural-sounding speech synthesizers. In this paper, we build an automatic detector and classifier for prosodic events in American English, based on their acoustic, lexical, and syntactic correlates. Following previous work in this area, we focus on accent (prominence, or ldquostressrdquo) and prosodic phrase boundary detection at the syllable level. Our experiments achieved a performance rate of 86.75% agreement on the accent detection task, and 91.61% agreement on the phrase boundary detection task on the Boston University Radio News Corpus.     

IESK-ArDB: a database for handwritten Arabic and an optimized topological segmentation approach

Even though a lot of researches have been conducted in order to solve the problem of unconstrained handwriting recognition, an effective solution is still a serious challenge. In this article, we address two Arabic handwriting recognition-related issues. Firstly, we present IESK-arDB, a new multi-propose off-line Arabic handwritten database. It is publicly available and contains more than 4,000 word images, each equipped with binary version, thinned version as well as a ground truth information stored in separate XML file. Additionally, it contains around 6,000 character images segmented from the database. A letter frequency analysis showed that the database exhibits letter frequencies similar to that of large corpora of digital text, which proof the database usefulness. Secondly, we proposed a multi-phase segmentation approach that starts by detecting and resolving sub-word overlaps, then hypothesizing a large number of segmentation points that are later reduced by a set of heuristic rules. The proposed approach has been successfully tested on IESK-arDB. The results were very promising, indicating the efficiency of the suggested approach.     

Exploring and exploiting a historical corpus for Arabic

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.     

The WaCky wide web: a collection of very large linguistically processed web-crawled corpora

This article introduces ukWaC, deWaC and itWaC, three very large corpora of English, German, and Italian built by web crawling, and describes the methodology and tools used in their construction. The corpora contain more than a billion words each, and are thus among the largest resources for the respective languages. The paper also provides an evaluation of their suitability for linguistic research, focusing on ukWaC and itWaC. A comparison in terms of lexical coverage with existing resources for the languages of interest produces encouraging results. Qualitative evaluation of ukWaC versus the British National Corpus was also conducted, so as to highlight differences in corpus composition (text types and subject matters). The article concludes with practical information about format and availability of corpora and tools.     

An omnifont open-vocabulary OCR system for English and Arabic

We present an omnifont, unlimited-vocabulary OCR system for English and Arabic. The system is based on hidden Markov models (HMM), an approach that has proven to be very successful in the area of automatic speech recognition. We focus on two aspects of the OCR system. First, we address the issue of how to perform OCR on omnifont and multi-style data, such as plain and italic, without the need to have a separate model for each style. The amount of training data from each style, which is used to train a single model, becomes an important issue in the face of the conditional independence assumption inherent in the use of HMMs. We demonstrate mathematically and empirically how to allocate training data among the different styles to alleviate this problem. Second, we show how to use a word-based HMM system to perform character recognition with unlimited vocabulary. The method includes the use of a trigram language model on character sequences. Using all these techniques, we have achieved character error rates of 1.1 percent on data from the University of Washington English Document Image Database and 3.3 percent on data from the DARPA Arabic OCR Corpus     

Building a multi-modal Arabic corpus (MMAC)

Traditionally, a corpus is a large structured set of text, electronically stored and processed. Corpora have become very important in the study of languages. They have opened new areas of linguistic research, which were unknown until recently. Corpora are also key to the development of optical character recognition (OCR) applications. Access to a corpus of both language and images is essential during OCR development, particularly while training and testing a recognition application. Excellent corpora have been developed for Latin-based languages, but few relate to the Arabic language. This limits the penetration of both corpus linguistics and OCR in Arabic-speaking countries. This paper describes the construction and provides a comprehensive study and analysis of a multi-modal Arabic corpus (MMAC) that is suitable for use in both OCR development and linguistics. MMAC currently contains six million Arabic words and, unlike previous corpora, also includes connected segments or pieces of Arabic words (PAWs) as well as naked pieces of Arabic words (NPAWs) and naked words (NWords); PAWs and Words without diacritical marks. Multi-modal data is generated from both text, gathered from a wide variety of sources, and images of existing documents. Text-based data is complemented by a set of artificially generated images showing each of the Words, NWords, PAWs and NPAWs involved. Applications are provided to generate a natural-looking degradation to the generated images. A ground truth annotation is offered for each such image, while natural images showing small paragraphs and full pages are augmented with representations of the text they depict. A statistical analysis and verification of the dataset has been carried out and is presented. MMAC was also tested using commercial OCR software and is publicly and freely available.     

Progress in stylistics: Theory, statistics, computers

This paper attempts to assess the progress made in computational stylistics dyring the course of the past twenty-five years. First, we discuss some theoretical notions of style, and then we sketch certain trends that emerge from relevant articles appearing in a variety of publications including conference proceedings and academic journals (other than CHum). The conclusion is that progress has been mixed.Louis T. Milic is professor emeritus of English at Cleveland State University and secretary-treasurer of the Dictionary Society of North America. He has been active in quantitative stylistics since the 1960s and has recently completed work on the second of two period corpora, the Century of Prose Corpus.     

Integrating linguistic knowledge into a conditional random fieldframework to identify biomedical named entities

As new high-throughput technologies have created an explosion of biomedical literature, there arises a pressing need for automatic information extraction from the literature bank. To this end, biomedical named entity recognition (NER) from natural language text is indispensable. Current NER approaches include: dictionary based, rule based, or machine learning based. Since, there is no consolidated nomenclature for most biomedical NEs, any NER system relying on limited dictionaries or rules does not seem to perform satisfactorily. In this paper, we consider a machine learning model, CRF, for the construction of our NER framework. CRF is a well-known model for solving other sequence tagging problems. In our framework, we do our best to utilize available resources including dictionaries, web corpora, and lexical analyzers, and represent them as linguistic features in the CRF model. In the experiment on the JNLPBA 2004 data, with minimal post-processing, our system achieves an F-score of 70.2%, which is better than most state-of-the-art systems. On the GENIA 3.02 corpus, our system achieves an F-score of 78.4% for protein names, which is 2.8% higher than the next-best system. In addition, we also examine the usefulness of each feature in our CRF model. Our experience could be valuable to other researchers working on machine learning based NER.     

Comparative evaluation of tools for Arabic corpora search and analysis

As the number of Arabic corpora is constantly increasing, there is an obvious and growing need for concordancing software for corpus search and analysis that supports as many features as possible of the Arabic language, and provides users with a greater number of functions. This paper evaluates six existing corpus search and analysis tools based on eight criteria which seem to be the most essential for searching and analysing Arabic corpora, such as displaying Arabic text in its right-to-left direction, normalising diacritics and Hamza, and providing an Arabic user interface. The results of the evaluation revealed that three tools: Khawas, Sketch Engine, and aConCorde, have met most of the evaluation criteria and achieved the highest benchmark scores. The paper concluded that developers’ conscious consideration of the linguistic features of Arabic when designing these three tools was the most significant factor behind their superiority.     

ICAME-Quo Vadis? Reflections on the use of computer corpora in linguistics

The focus of the paper is on the use of computer corpora in language research. The historical background is touched on, with special reference to work within the International Computer Archive of Modern English (ICAME). Developments in the use of corpora are surveyed. Issues taken up include the representativeness and structure of corpora. Special attention is paid to pitfalls in the use of corpora. Corpus compilers must provide adequate documentation on the texts. Corpus users must know the corpus in order to evaluate whether it is appropriate for their research problem and in order to evaluate the results of their studies. Stig Johansson is professor of Modern English Language at the University of Oslo. He is co-ordinating secretary of the International Computer Archive of Modern English (ICAME) and editor of the ICAME Journal(with Anna-Brita Stenström). Research interests include modern English grammar, contrastive linguistics, computing in language research. Publications include: Frequency Analysis of English Vocabulary and Grammar(with Knut Hofland, Oxford: Clarendon Press, 1989); English Computer Corpora: Selected Papers and Research Guide(with Anna-Brita Stenström, Berlin: Mouton de Gruyter, 1991).     

A corpus-based approach to the multimodal analysis of specialized knowledge

Modern communication environments have changed the cognitive patterns of individuals, who are now used to the interaction of information encoded in different semiotic modalities, especially visual and linguistic. Despite this, the main premise of Corpus Linguistics is still ruling: our perception of and experience with the world is conveyed in texts, which nowadays need to be studied from a multimodal perspective. Therefore, multimodal corpora are becoming extremely useful to extract specialized knowledge and explore the insights of specialized language and its relation to non-language-specific representations of knowledge. It is our assertion that the analysis of the image-text interface can help us understand the way visual and linguistic information converge in subject-field texts. In this article, we use Frame-based terminology to sketch a novel proposal to study images in a corpus rich in pictorial representations for their inclusion in a terminological resource on the environment. Our corpus-based approach provides the methodological underpinnings to create meaning within terminographic entries, thus facilitating specialized knowledge transfer and acquisition through images.     

Semantic indexing of Arabic texts for information retrieval system

As part of information retrieval systems (IRS) and in the context of the use of ontologies for documents and queries indexing, we propose and evaluate in this paper the contribution of this approach applied to Arabic texts. To do this we indexed a corpus of Arabic text using Arabic WordNet. The disambiguation of words was performed by applying the Lesk algorithm. The results obtained by our experiment allowed us to deduct the contribution of this approach in IRS for Arabic texts.     

Building language resources for a Multi-Engine English-Filipino machine translation system

In this paper, we present the building of various language resources for a multi-engine bi-directional English-Filipino Machine Translation (MT) system. Since linguistics information on Philippine languages are available, but as of yet, the focus has been on theoretical linguistics and little is done on the computational aspects of these languages, attempts are reported here on the manual construction of these language resources such as the grammar, lexicon, morphological information, and the corpora which were literally built from almost non-existent digital forms. Due to the inherent difficulties of manual construction, we also discuss our experiments on various technologies for automatic extraction of these resources to handle the intricacies of the Filipino language, designed with the intention of using them for the MT system. To implement the different MT engines and to ensure the improvement of translation quality, other language tools (such as the morphological analyzer and generator, and the part of speech tagger) were developed.     

Multilingual and cross-domain temporal tagging

Extraction and normalization of temporal expressions from documents are important steps towards deep text understanding and a prerequisite for many NLP tasks such as information extraction, question answering, and document summarization. There are different ways to express (the same) temporal information in documents. However, after identifying temporal expressions, they can be normalized according to some standard format. This allows the usage of temporal information in a term- and language-independent way. In this paper, we describe the challenges of temporal tagging in different domains, give an overview of existing annotated corpora, and survey existing approaches for temporal tagging. Finally, we present our publicly available temporal tagger HeidelTime, which is easily extensible to further languages due to its strict separation of source code and language resources like patterns and rules. We present a broad evaluation on multiple languages and domains on existing corpora as well as on a newly created corpus for a language/domain combination for which no annotated corpus has been available so far.