Dublin City University at QA@CLEF 2008

We describe our participation in Multilingual Question Answering at CLEF 2008 using German and English as our source and target languages respectively. The system was built using UIMA (Unstructured Information Management Architecture) as underlying framework

Finding Relevant Answers in Software Forums

Abstract—Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containin

From Query to Usable Code: An Analysis of Stack Overflow Code Snippets

Enriched by natural language texts, Stack Overflow code snippets are an invaluable code-centric knowledge base of small units of source code. Besides being useful for software developers, these annotated snippets can potentially serve as the basis for automated tools that provide working code solutions to specific natural language queries. With the goal of developing automated tools with the Stack Overflow snippets and surrounding text, this paper investigates the following questions: (1) How usable are the Stack Overflow code snippets? and (2) When using text search engines for matching on the natural language questions and answers around the snippets, what percentage of the top results contain usable code snippets? A total of 3M code snippets are analyzed across four languages: C\#, Java, JavaScript, and Python. Python and JavaScript proved to be the languages for which the most code snippets are usable. Conversely, Java and C\# proved to be the languages with the lowest usability rate. Further qualitative analysis on usable Python snippets shows the characteristics of the answers that solve the original question. Finally, we use Google search to investigate the alignment of usability and the natural language annotations around code snippets, and explore how to make snippets in Stack Overflow an adequate base for future automatic program generation.Comment: 13th IEEE/ACM International Conference on Mining Software Repositories, 11 page

Normalized Information Distance

The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wide Web can be used. These practical realizations of the normalized information distance can then be applied to machine learning tasks, expecially clustering, to perform feature-free and parameter-free data mining. This chapter discusses the theoretical foundations of the normalized information distance and both practical realizations. It presents numerous examples of successful real-world applications based on these distance measures, ranging from bioinformatics to music clustering to machine translation.Comment: 33 pages, 12 figures, pdf, in: Normalized information distance, in: Information Theory and Statistical Learning, Eds. M. Dehmer, F. Emmert-Streib, Springer-Verlag, New-York, To appea

Fully Automated Fact Checking Using External Sources

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.Comment: RANLP-201