Ahmed Rafea

TextOntoEx: Automatic ontology construction from natural English text

Most of existing ontologies construction tools support construction of ontological relations (e.g., taxonomy, equivalence, etc.) but they do not support construction of domain relations, non-taxonomic conceptual relationships (e.g., causes, caused by, treat, treated by, has-member, contain, material-of, operated-by, controls, etc.). Domain relations are found mainly in text sources. TextOntoEx constructs ontology from natural domain text using semantic pattern-based approach. TextOntoEx is a chain between linguistic analysis and ontology engineering. TextOntoEx analyses natural domain text to extract candidate relations and then maps them into meaning representation to facilitate constructing ontology. The paper explains this approach in more details and discusses some experiments on deriving ontology from natural text.

KP-Miner: A keyphrase extraction system for English and Arabic documents

Automatic keyphrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents the KP-Miner system, and demonstrates through experimentation and comparison with widely used systems that it is effective and efficient in extracting keyphrases from both English and Arabic documents of varied length. Unlike other existing keyphrase extraction systems, the KP-Miner system does not need to be trained on a particular document set in order to achieve its task. It also has the advantage of being configurable as the rules and heuristics adopted by the system are related to the general nature of documents and keyphrases. This implies that the users of this system can use their understanding of the document(s) being input into the system to fine-tune it to their particular needs.

Sentence-level Arabic sentiment analysis

Arabic sentiment analysis research existing currently is very limited. While sentiment analysis has many applications in English, the Arabic language is still recognizing its early steps in this field. In this paper, we show an application on Arabic sentiment analysis by implementing a sentiment classification for Arabic tweets. The retrieved tweets are analyzed to provide their sentiments polarity (positive, or negative). Since, this data is collected from the social network Twitter; it has its importance for the Middle East region, which mostly speaks Arabic.

A Survey of Ontology Learning Approaches

The problem that ontology learning deals with is the knowledge acquisition bottleneck, that is to say the difficulty to actually model the knowledge relevant to the domain of interest. Ontologies are the vehicle by which we can model and share the knowledge among various applications in a specific domain. So many research developed several ontology learning approaches and systems. In this paper, we present a survey for the different approaches in ontology learning from semi-structured and unstructured date

Machine Translation of English Noun Phrases into Arabic

The present work reports our attempt in automating the translation of English noun phrase (NP) into Arabic. Translating NP is a very important task toward sentence translation since NPs form the majority of textual content of the scientific and technical documents. The system is implemented in Prolog and the parser is written in DCG formalism. The paper also describes our experience with the developed MT system and reports results of its application on real titles of theses from the computer science domain.

A Stochastic Arabic Diacritizer Based on a Hybrid of Factorized and Unfactorized Textual Features

This paper introduces a large-scale dual-mode stochastic system to automatically diacritize raw Arabic text. The first of these modes determines the most likely diacritics by choosing the sequence of full-form Arabic word diacritizations with maximum marginal probability via A^ lattice search and long-horizon n-grams probability estimation. When full-form words are OOV, the system switches to the second mode which factorizes each Arabic word into all its possible morphological constituents, then uses also the same techniques used by the first mode to get the most likely sequence of morphemes, hence the most likely diacritization. While the second mode achieves a far better coverage of the highly derivative and inflective Arabic language, the first mode is faster to learn, i.e., yields better disambiguation results for the same size of training corpora, especially for inferring syntactical (case-ending) diacritics. Our presented hybrid system that benefits from the advantages of both modes has experimentally been found superior to the best performing reported systems of Habash and Rambow, and of Zitouni, using the same training and test corpus for the sake of fair comparison. The word error rates of (morphological diacritization, overall diacritization including the case endings) for the three systems are, respectively, as follows (3.1%, 12.5%), (5.5%, 14.9%), and (7.9%, 18%). The hybrid architecture of language factorizing and unfactorizing components may be inspiring to other NLP/HLT problems in analogous situations.

Ontology learning from domain specific web documents

Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.

A multiagent approach for diagnostic expert systems via the internet

In recent years there has been considerable interest in the possibility of building complex problem solving systems as groups of co-operating experts. This has led us to develop a multiagent expert systems capable to run on servers that can support a large group of users (clients) who communicate with the system over the network. The system provides an architecture to coordinate the behavior of several specific agent types. Two types of agents are involved. One type works on the server computer and the other type works on the client computers. The society of agents in our system consists of expert systems agents (diagnosis agents, and a treatment agent) working on the server side, each of which contains an autonomous knowledge-based system. Typically, agents will have expertise in distinct but related domains. The whole system is capable of solving problems, which require the cumulative expertise of the agent community. Besides to the user interface agent who employs an intelligent data collector, so-called communication model in KADS, working on the client sides. We took the advantage of a successful pre-existing expert systems-developed at CLAES (Central Laboratory for Agricultural Expert Systems, Egypt)-for constructing an architecture of a community of cooperating agents. This paper describes our experience with decomposing the diagnosis expert systems into a multi-agent system. Experiments on a set of test cases from real agricultural expert systems were preformed. The expert systems agents are implemented in Knowledge Representation Object Language (KROL) and JAVA languages using KADS knowledge engineering methodology on the WWW platform.

An accuracy-enhanced light stemmer for arabic text

Stemming is a key step in most text mining and information retrieval applications. Information extraction, semantic annotation, as well as ontology learning are but a few examples where using a stemmer is a must. While the use of light stemmers in Arabic texts has proven highly effective for the task of information retrieval, this class of stemmers falls short of providing the accuracy required by many text mining applications. This can be attributed to the fact that light stemmers employ a set of rules that they apply indiscriminately and that they do not address stemming of broken plurals at all, even though this class of plurals is very commonly used in Arabic texts. The goal of this work is to overcome these limitations. The evaluation of the work shows that it significantly improves stemming accuracy. It also shows that by improving stemming accuracy, tasks such as automatic annotation and keyphrase extraction can also be significantly improved.

Generating Arabic text in multilingual speech-to-speech machine translation framework

The interlingual approach to machine translation (MT) is used successfully in multilingual translation. It aims to achieve the translation task in two independent steps. First, meanings of the source-language sentences are represented in an intermediate language-independent (Interlingua) representation. Then, sentences of the target language are generated from those meaning representations. Arabic natural language processing in general is still underdeveloped and Arabic natural language generation (NLG) is even less developed. In particular, Arabic NLG from Interlinguas was only investigated using template-based approaches. Moreover, tools used for other languages are not easily adaptable to Arabic due to the language complexity at both the morphological and syntactic levels. In this paper, we describe a rule-based generation approach for task-oriented Interlingua-based spoken dialogue that transforms a relatively shallow semantic interlingual representation, called interchange format (IF), into Arabic text that corresponds to the intentions underlying the speaker’s utterances. This approach addresses the handling of the problems of Arabic syntactic structure determination, and Arabic morphological and syntactic generation within the Interlingual MT approach. The generation approach is developed primarily within the framework of the NESPOLE! (NEgotiating through SPOken Language in E-commerce) multilingual speech-to-speech MT project. The IF-to-Arabic generator is implemented in SICStus Prolog. We conducted evaluation experiments using the input and output from the English analyzer that was developed by the NESPOLE! team at Carnegie Mellon University. The results of these experiments were promising and confirmed the ability of the rule-based approach in generating Arabic translation from the Interlingua taken from the travel and tourism domain.