This paper presents and compares WordNetbased and distributional similarity approaches. The strengths and weaknesses of each approach regarding similarity and relatedness tasks are discussed, and a combination is presented. Each of our methods independently provide the best results in their class on the RG and WordSim353 datasets, and a supervised combination of them yields the best published results on all datasets. Finally, we pioneer cross-lingual similarity, showing that our methods are easily adapted for a cross-lingual task with minor losses. 1

In opinion mining of product reviews, one often wants to produce a summary of opinions based on product features/attributes. However, for the same feature, people can express it with different words and phrases. To produce a meaningful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. This paper proposes a constrained semisupervised learning method to solve the problem. Experimental results using reviews from five different domains show that the proposed method is competent for the task. It outperforms the original EM and the state-of-the-art

In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product features/attributes (also called aspects). However, for the same feature, people can express it with many different words or phrases. To produce a useful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. Although several methods have been proposed to extract product features from reviews, limited work has been done on clustering or grouping of synonym features. This paper focuses on this task. Classic methods for solving this problem are based on unsupervised learning using some forms of distributional similarity. However, we found that these methods do not do well. We then model it as a semi-supervised learning problem. Lexical characteristics of the problem are exploited to automatically identify some labeled examples. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods by a large margin.

The ability to model, share and re-use value sets across multiple medical information systems is an important requirement. However, generating value sets semiautomatically from a terminology service is still an unresolved issue, in part due to the lack of linkage to clinical context patterns that provide the constraints in defining a concept domain and invocation of value sets extraction. Towards this goal, we develop and evaluate an approach for context-driven automatic value sets extraction based on a formal terminology model. The crux of the technique is to identify and define the context patterns from various domains of discourse and leverage them for value set extraction using two complementary ideas based on (i) local terms provided by the Subject Matter Experts (extensional) and (ii) semantic definition of the concepts in coding schemes (intensional). A prototype was implemented based on SNOMED CT rendered in the LexGrid terminology model and a preliminary evaluation is presented.

The ability to model, share and re-use value sets across multiple medical information systems is an important requirement. However, generating value sets semi-automatically from a terminology service is still an unresolved issue, in part due to the lack of linkage to clinical context patterns that provide the constraints in defining a concept domain and invocation of value sets extraction. Towards this goal, we develop and evaluate an approach for context-driven automatic value sets extraction based on a formal terminology model. The crux of the technique is to identify and define the context patterns from various domains of discourse and leverage them for value set extraction using two complementary ideas based on (i) local terms provided by the subject matter experts (extensional) and (ii) semantic definition of the concepts in coding schemes (intensional). We develop algorithms based on wellstudied graph traversal and ontology segmentation techniques for both the approaches and implement a prototype demonstrating their applicability on use cases from SNOMED CT rendered in the LexGrid terminology model. We also present preliminary evaluation of our approach and report investigation results done by subject matter experts at the Mayo Clinic. 1