Akram Alkouz

An Interests Discovery Approach in Social Networks Based on Semantically Enriched Graphs

Studying the text messages of a user such as his posts in Facebook or his tweets in Twitter can help in detecting his topics of interests. User in Social Network Systems (SNS) posts text messages about a wide diverse of topics. Posts usually written in a non-standard language, which make it not applicable to the standard Natural Language Processing (NLP) techniques used to catch the relations between words in text. In many cases there are semantic relations between the contained entities of posts that can infer the interest of the user. Bag-Of-Words (BOW) based text classification techniques classify this kind of messages to a wide diverse of topics, but they fail in catching the implicit semantic relation between the contained entities. In this paper we propose a technique to discover the implicit semantic relations between entities in text messages, which can infer the interests of a user. The proposed technique based on a semantically enriched graph representation of entities contained in text messages generated by a user, a new algorithm (Root-Path-Degree) is invented and used to find the most representative sub-graph that reflects the semantic implicit interests of the user. An evaluation was done using manually annotated posts of 687 Facebook users. Precision and Recall results showed our technique performs better than the standard BOW technique.

Time-sensitive Arabic multiword expressions extraction from social networks

In this paper, we present a comprehensive approach for extracting and relating Arabic multiword expressions (MWE) from Social Networks. 15 million tweets were collected and processed to form our data set. Due to the complexity of processing Arabic and the lack of resources, we built an experimental system to extract and relate similar MWE using statistical methods. We introduce a new metrics for measuring valid MWE in Social Networks. We compare results obtained from our experimental system against semantic graph obtained from web knowledgebase.

Extracting Arabic semantic graph from Aljazeera.net

Collaborative knowledge bases such as Wikipedia and Wiktionary became valuable lexical semantic resources with a high influence in diverse Natural Language Processing tasks. Although Arabic collaborative knowledge bases are in general poorly informatized and significantly different from traditional linguistic knowledge bases in various respects. Aljazeera.net is professionally edited and has rich semantic structure. It constitutes an asset, an impediment and a challenge for research in Arabic Natural Language Processing. This paper addresses one such major impediment, namely the lack of suitable tools to access the knowledge stored in these large semantic knowledge bases. We present a framework designed for mining the explicit and implicit lexical semantic information impeded in the structure and the content of Aljazeera.net. Furthermore, it provides an efficient and structured access to the resulted semantic graph. This framework will be freely available for research purposes to meet the needs of Arabic Natural Language Processing community.