Mohammad Daoud

Constructing Multilingual Preterminological Graphs using various online-community resources

We are describe the concept of dedicated Multilingual Preterminological Graphs MPGs, and some automatic approaches for constructing them by analyzing the behavior of online community users. A Multilingual Preterminological Graph is a special lexical resource that contains massive amount of terms related to a special domain, and can be used as raw material to later build a standardized terminological repository. Building such a graph is difficult using traditional approaches, as it needs huge efforts by domain specialists and terminologists. In our approach, we build such a graph by analyzing the access log files of the website of the community, and by finding the important terms that have been used to search in that website, and their association with each other. We aim at making this graph as a seed repository so multilingual volunteers can contribute. We are experimenting this approach with the Digital Silk Road Project. We have used its access log files since its beginning in 2003, and obtained an initial graph of around 116000 terms. As an application, we used this graph to obtain a preterminological multilingual database that is serving a CLIR system for the DSR project.

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 Terminological Relationships from Historical Patterns of Social Media Terms

In this article we propose and evaluate a method to extract terminological relationships from microblogs. The idea is to analyze archived microblogs (tweets for example) and then to trace the history of each term. Similar history indicates a relationship between terms. This indication can be validated using further processing. For example, if the term t1 and t2 were frequently used in Twitter at certain days, and there is a match in the frequency patterns over a period of time, then t1 and t2 can be related. Extracting standard terminological relationships can be difficult; especially in a dynamic context such as social media, where millions of microblogs (short textual messages) are published, and thousands of new terms are coined every day. So we are proposing to compile nonstandard raw repository of lexical units with unconfirmed relationships. This paper shows a method to draw relationships between time-sensitive Arabic terms by matching similar timelines of these terms. We use dynamic time warping to align the timelines. To evaluate our approach we elected 430 terms and we matched the similarity between the frequency patterns of these terms over a period of 30 days. Around 250 correct relationships were extracted with a precision of 0.65. These relationships were drawn without using any parallel text, nor analyzing the textual context of the term. Taking into consideration that the studied terms can be newly coined by microbloggers and their availability in standard repositories is limited.