This paper explores the problem of computing text similarity between verb phrases describing skilled human behavior for the purpose of finding approximate matches . Four parsers are evaluated on a large corpus of skill statements extracted from an enterprise-wide expertise taxonomy . A similarity measure utilizing common semantic role features extracted from parse trees was found superior to an information-theoretic measure of similarity and comparable to the level of human agreement . Knowledge-intensive industries need to become more efficient at deploying the right expertise as quickly and smoothly as possible , thus it is desired to have systems that can quickly match and deploy skilled individuals to meet customer needs . The searches in most of the current matching systems are based on exact matches between skill statements . However , exact matching is very likely to miss individuals who are very good matches to the job but did n't select the exact skills that appeared in the open job description . It is always hard for individuals to find the perfect skills to describe their skill sets . For example , an individual might not know whether to choose a skill stating that refers to " maintaining " a given product or " supporting " it or whether to choose a skill about maintaining a " database " or about maintaining " DB2 " . Thus , it is desirable for the job search system to be able to find approximate matches , instead of only exact matches , between available individuals and open job positions . More specifically , a skill similarity computation is needed to allow searches to be expanded to related skills , and return more potential matches . In this paper , we present our work on developing a skill similarity computation based upon semantic commonalities between skill statements . Although there has been much work on text similarity metrics ( Lin , 1998a ; Corley and Mihalcea , 2005 ) , most approaches treat texts as a bag of words and try to find shared words with certain statistical properties based on corpus frequencies . As a result , the structural information in the text is ignored in these approaches . We will describe a new semantic approach that takes the structural information of the text into consideration and matches skill statements on corresponding semantic roles . We will demonstrate that it can outperform standard statistical text similarity techniques , and reach the level of human agreement . In Section 2 , we first describe the skill statements we extracted from an enterprise-wide expertise taxonomy . In Section 3 , we describe the performance of a standard statistical approach on this task . This motivates our semantic approach of matching skill statements on corresponding semantic roles . We also compare and evaluate the performance of four natural language parsers ( the Charniak parser , the Stanford parser , the ESG parser , and MINIPAR ) for the purpose of our task . An inter-rater agreement study and evaluation of our approach will be presented in Section 4 . We end with a discussion and conclusion . In this paper , we have presented our work on a semantic similarity computation for skill statements in natural language . We compared and evaluated four different natural language parsers for our task , and matched skills on their corresponding semantic roles extracted from the parse trees generated by one of these parsers . The evaluation results showed that the skill similarity computation based on semantic role matching can outperform a standard statistical approach and reach the level of human agreement . The extracted semantic role information can also be incorporated into the standard statistical approaches as additional features . One way is to give higher weights to those semantic role features deemed most important . This approach has achieved a high performance for a text categorization task when combining extracted keywords with the full text ( Hulth and Megyesi , 2006 ) . We have shown that good results can be achieved for a domain-specific text matching task by performing a simple word-based feature comparison on corresponding structural elements of texts . We have shown that the structural elements of importance can be identified by domain-specific pattern analysis of corresponding parse trees . We believe this approach can generalize to other domains where phrases , sentences , or other short texts need to be compared .

Challenge: Existing systems for skilled expertise matching use exact matching between skill statements resulting in missing good matches and calling for a system with approximate matching.
Approach: They evaluate four different parsers to take structural information into consideration by matching skill statements on corresponding semantic roles from generated parse trees.
Outcome: The proposed similarity measure outperforms a standard statistical information-theoretic measure and is comparable to a human agreement.