AbstractMeasuring lexical semantic relatedness is an important task in Natural Language Processing (NLP). It is often a prerequisite to many complex NLP tasks. Despite an extensive amount of work dedicated to this area of research, there is a lack of an up-to-date survey in the field. This paper aims to address this issue with a study that is focused on four perspectives: (i) a comparative analysis of background information resources that are essential for measuring lexical semantic relatedness; (ii) a review of the literature with a focus on recent methods that are not covered in previous surveys; (iii) discussion of the studies in the biomedical domain where novel methods have been introduced but inadequately communicated across the domain boundaries; and (iv) an evaluation of lexical semantic relatedness methods and a discussion of useful lessons for the development and application of such methods. In addition, we discuss a number of issues in this field and suggest future research directions. It is believed that this work will be a valuable reference to researchers of lexical semantic relatedness and substantially support the research activities in this field.

Our study demonstrates that (1) the MTD is epirubicin 90 mg/m2 and paclitaxel 200 mg/m2; (2) no clear relationship exists between pharmacokinetic data of paclitaxel and myelosuppression, while the increase in the dose of paclitaxel is associated with a reduction in epirubicinol plasma levels; and (3) the association is feasible, with low cardiotoxicity, and has a high activity in metastatic breast cancer.

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This work explores the usage of Linked Data for Web scale Information Extraction and shows encouraging results on the task of Wrapper Induction. We propose a simple knowledge based method which is (i) highly flexible with respect to different domains and (ii) does not require any training material, but exploits Linked Data as background knowledge source to build essential learning resources. The major contribution of this work is a study of how Linked Data -an imprecise, redundant and large-scale knowledge resourcecan be used to support Web scale Information Extraction in an effective and efficient way and identify the challenges involved. We show that, for domains that are covered, Linked Data serve as a powerful knowledge resource for Information Extraction. Experiments on a publicly available dataset demonstrate that, under certain conditions, this simple unsupervised approach can achieve competitive results against some complex state of the art that always depends on training data.

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