Anis Zouaghi

Combination of information retrieval methods with LESK algorithm for Arabic word sense disambiguation

In this paper, we propose to use Harman, Croft and Okapi measures with Lesk algorithm to develop a system for Arabic word sense disambiguation, that combines unsupervised and knowledge based methods. This system must solve the lexical semantic ambiguity in Arabic language. The information retrieval measures are used to estimate the most relevant sense of the ambiguous word, by returning a semantic coherence score corresponding to the context that is semantically closest to the original sentence containing the ambiguous word. The Lesk algorithm is used to assign and select the adequate sense from those proposed by the information retrieval measures mentioned above. This selection is based on a comparison between the glosses of the word to be disambiguated, and its different contexts of use extracted from a corpus. Our experimental study proves that using of Lesk algorithm with Harman, Croft, and Okapi measures allows us to obtain an accuracy rate of 73%.

Ambiguous Arabic Words Disambiguation

In this paper we put forward an unsupervised system WSD-AL for Arabic word disambiguation. We apply some pre-processing steps to texts containing the ambiguous word in the corpus and we extract the most relevant words. Then, we put to use the Context-Matching algorithm that returns a semantic coherence score corresponding to the context of use that is semantically closest to the original sentence. These Contexts are generated using the glosses of the ambiguous word and the corpus. The results found by the proposed ‎system are satisfactory, as the rate of disambiguation obtained ‎equals 78.

Lexical Disambiguation of Arabic Language: An Experimental Study

In this paper we test some supervised algorithms that most of the existing related works of word sen se disambiguation have cited. Due to the lack of lingu istic data for the Arabic language, we work on non-annotated corpus and with the help of four annotators; we were able to annota te the different samples containing the ambiguous words. Since that, we test the Naive Bayes algorithm, the decision lists and the exemplar based algorithm. During the experimental study, we test the influence of the window size on the disamb iguation quality, the derivation and the technique of smooth ing for the (2n+1)-grams. For these tests the exemplar based al gorithm achieves the best rate of precision. Index Terms —Supervised algorithms, training data, Naive Bayes, decision list, exemplar based algorithm, win dow size.

A Hybrid Approach for Arabic Word Sense Disambiguation

In this paper, we present a hybrid approach for Word Sense Disambiguation of Arabic Language (called WSD-AL), that combines unsupervised and knowledge-based methods. Some pre-processing steps are applied to texts containing the ambiguous words in the corpus (1500 texts extracted from the web), and the salient words that affect the meaning of these words are extracted. After that a Context Matching algorithm is used, it returns a semantic coherence score corresponding to the context of use that is semantically closest to the original sentence. The contexts of use are generated using the glosses of the ambiguous word and the corpus. The results found by the proposed system are satisfactory; we have achieved a precision of 79%.

Contribution to semantic analysis of Arabic language

We propose a new approach for determining the adequate sense of Arabic words. For that, we propose an algorithm based on information retrieval measures to identify the context of use that is the closest to the sentence containing the word to be disambiguated. The contexts of use represent a set of sentences that indicates a particular sense of the ambiguous word. These contexts are generated using the words that define the senses of the ambiguous words, the exact string-matching algorithm, and the corpus. We use the measures employed in the domain of information retrieval, Harman, Croft, and Okapi combined to the Lesk algorithm, to assign the correct sense of those proposed.

An experimental study for some supervised lexical disambiguation methods of arabic language

In this paper we propose an experimental study for some supervised algorithms to disambiguate arabic words. Due to the lack of linguistic data for the Arabic language, we work on non-annotated corpus and with the help of four annotators; we were able to annotate the different samples containing the ambiguous words. Since that, we test the naïve Bayes algorithm, the decision lists and the exemplar based algorithm. During the experimental study, we test the influence of the window size on the disambiguation quality, the derivation and the technique of smoothing for the (2n+1)-grams. We find that the exemplar based algorithm achieves the best rate of precision.

A Rule-based Semantic Frame Annotation of Arabic Speech Turns for Automatic Dialogue Analysis

Abstract In this article, and after sketching the state of the art on semantic representations, we have deduced that an important technological lock that hinders the H/M Spoken Dialogue System (SDS) is the adoption of the attribute/value schemes as a semantic representation. Besides, current systems, using such a representation (form of a formula), constrain too much their users to behave according to the model set by the designer. Thus, in order to overcome all the limitations of the modeling by a pre-established form, we decided to choose the use of semantic frames as a representation of the meanings of Arabic user statements. In this perspective, the choice of the FrameNet paradigm as a high level semantic representation seems appropriate. In addition to the robustness of the paradigm, FrameNet has the advantage of producing standard annotations that are easy to share and compare within the scientific community. To represent more complex user rounds, we propose to use, in the understanding module, a semantic model that is more flexible than that of a predefined form.

Knowledge Extraction with NooJ Using a Syntactico-Semantic Approach for the Arabic Utterances Understanding.

Regarding the amelioration of NLP field, knowledge extraction has become an interesting research topic. Indeed, the need to an improvement through the NLP techniques has become also necessary and advantageous. Hence, in a general context of the construction of an Arabic touristic corpus equivalent to those of European projects MEDIA and LUNA, and due to the lack of Arabic electronic resources, we had the opportunity to expand the EL-DicAr of [11] by knowledge hinging on Touristic Information and Hotel Reservations (TIHR). Thus, in the same manner of [11], we have developed local grammars for the recognition of essential knowledge in our field of study. This task facilitates greatly the subsequent work of understanding user utterances interacting with a dialogue system.

N-scheme model: An approach towards reducing Arabic language sparseness

In addition to traditional characteristics of natural languages like implicitly or ambiguity or imprecision, Arabic is known by its sparseness which explains the difficulty of its automatic processing. But on the other hand, Arabic language is characterized by an interesting property; lemmas are generated by derivation based on roots and schemes. Schemes are kinds of molds allowing changing the form of root by actions involving elongation, or repetition, or even adding characters. Schemes can also give meaning to generated word. In this work we have studied the statistical characteristics of the Arabic language at the level of schemes; we have emphasized the attenuation of the sparseness at this level. Then we explored the possibility of building natural language processing tools for Arabic by relying on schemes. We discovered that schemes have great potential in building accurate natural language processing tools for Arabic. Based entirely or partially on schemes we built an n-scheme statistical model and a text classification system.

Automatic translation of Arabic queries for bilingual information retrieval

In this paper, we present an automatic query translation in Arabic for information retrieval. This system, implements a method for lexical disambiguation of terms in a query. To choose the best sense, each term of the query is projected on the French EuroWordNet and we extract his related concepts in order to form a semantic network. In a second time using the same type of network, but by integrating LSI (Latent Semantic Index), and extraction of contextual hidden links between the concepts of the list (listSRF). This list extracted by the automatic alignment by Mkalign tools, from the knowledge base defined by the Monde Diplomatic parallel corpus. This tool will allow us to map between the relevant sentences in Arabic identified in our previous work with French sentences to build a listSRF. This list forms the basis of the lexical disambiguation method. Finally, we propose a mechanism for selecting the best sense of the ambiguous term of the query, based on the matching between each network corresponds to a word in the query with the network listSRF to extract an adequate sense with the highest degree of similarity. An evaluation and comparison are conducted to measure the quality of our translation system.