Ibrahim Uysal

I developed an automated drop-shipping web application for n11.com (the largest marketplace in Turkey) using the Django framework. I have been generating passive income with this project for more than four years.

I individually authored this research paper with the following abstract:

The increasing volume of streaming data on microblogs has reintroduced the necessity of effective filtering mechanisms for such media. Microblog users are overwhelmed with mostly uninteresting pieces of text in order to access information of value.

In this paper, we propose a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward. In addition, we also investigate how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets. Finally, conducting a pilot user study, we analyze how a retweet likelihood correlates with the interestingness of the tweets.

As a team, authored this research paper with the following abstract:

Text reuse detection aims to identify duplicates, reformulations or partial rewrites of a given text. Some previous research has focused on determining text reuse instances accurately on local corpora. However, the practical usage of finding text reuse on the web has remained largely untested.

In this work, we 1) introduce a novel text reuse searching interface for the web, based on a previously proposed architecture, 2) evaluate its feasibility, and 3) investigate techniques to improve both effectiveness and efficiency. Our results show that exhaustive query submission using n-grams can dramatically reduce the execution time with only small losses in accuracy.

As a team, authored this research paper with the following abstract:

Semantic role labeling (SRL) aims to identify the constituents of a sentence, together with their roles with respect to the sentence predicates.

In this paper, we introduce and assess the idea of using SRL on generic multi-document summarization (MDS). We score sentences according to their inclusion of frequent semantic phrases and form the summary using the top-scored sentences. We compare this method with a term-based sentence scoring approach to investigate the effects of using semantic units instead of single words for sentence scoring. We also integrate our scoring metric as an auxiliary feature to a cutting edge summarizer with the intention of examining its effects on the performance. The experiments using datasets from the Document Understanding Conference (DUC) 2004 show that the SRL-based summarization outperforms the term-based approach as well as most of the DUC participants.