This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our un-supervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task's organizers.

Automatic keyphrase extraction aims at extracting a compact representation of a single document which can be used for various applications such as indexing, classification or summarization. Existing methods for keyphrase extraction usually define the set of phrases of a document as a crisp set and by scoring the phrases, they select the keyphrases of the document. In this work we define the set of phrases inside a document to be a fuzzy set, and based on the membership values of the phrases, we select the ones with higher membership values as the keyphrases of the document. Moreover we propose a novel evaluation method inspired by the Turing test which can be used for extractive summarization tasks.

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