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 .
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.
They evaluate four different parsers to take structural information into consideration by matching skill statements on corresponding semantic roles from generated parse trees.
The proposed similarity measure outperforms a standard statistical information-theoretic measure and is comparable to a human agreement.