Nicolai Erbs

Link Discovery: A Comprehensive Analysis

We present a comprehensive analysis of link discovery approaches. We classify them with regard to the type of knowledge being used, and identify three commonly used sources of knowledge: The text of a document, the document title, and already existing links. We analyze the influence of the knowledge source as well as of the amount of training data used. Results show that the link-based approach performs best if the amount of training data is huge. In a more realistic setting with fewer training data, the text-based approach yields better results.

Bringing Order to Digital Libraries: From Keyphrase Extraction to Index Term Assignment

Collections of topically related documents held by digital libraries are valuable resources for users; however, as collections grow, it becomes more difficult to search them for specific information. Structure needs to be introduced to facilitate searching. Assigning index terms is helpful, but it is a tedious task even for professional indexers, requiring knowledge about the collection in general, and the document in particular. Automatic index term assignment (ITA) is considered to be a great improvement. In this paper we present a hybrid approach to index term assignment, using a combination of keyphrase extraction and multi-label classification. Keyphrase extraction efficiently assigns infrequently used

DKPro Keyphrases: Flexible and Reusable Keyphrase Extraction Experiments

DKPro Keyphrases is a keyphrase extraction framework based on UIMA. It offers a wide range of state-of-the-art keyphrase experiments approaches. At the same time, it is a workbench for developing new extraction approaches and evaluating their impact. DKPro Keyphrases is publicly available under an open-source license. 1

Sense and Similarity: A Study of Sense-level Similarity Measures

In this paper, we investigate the difference between word and sense similarity measures and present means to convert a state-of-the-art word similarity measure into a sense similarity measure. In order to evaluate the new measure, we create a special sense similarity dataset and re-rate an existing word similarity dataset using two different sense inventories from WordNet and Wikipedia. We discover that word-level measures were not able to differentiate between different senses of one word, while sense-level measures actually increase correlation when shifting to sense similarities. Sense-level similarity measures improve when evaluated with a re-rated sense-aware gold standard, while correlation with word-level similarity measures decreases.

Counting What Counts: Decompounding for Keyphrase Extraction

A core assumption of keyphrase extraction is that a concept is more important if it is mentioned more often in a document. Especially in languages like German that form large noun compounds, frequency counts might be misleading as concepts “hidden” in compounds are not counted. We hypothesize that using decompounding before counting term frequencies may lead to better keyphrase extraction. We identified two effects of decompounding: (i) enhanced frequency counts, and (ii) more keyphrase candidates. We created two German evaluation datasets to test our hypothesis and analyzed the effect of additional decompounding for keyphrase extraction.