The goal of this study is to identify the state-of-the-art in Human Language Technology (HLT), pinpointing potential research directions. We performed our analysis by examining conference publications for recent ACL (Association for Computational Linguistics) and NAACL (North American Association for Computational Linguistics) conferences, identifying terms and topics which reflect the state of HLT research, combined with a network analysis of co-authorships.
All analyses suggest that in the field as seen through ACL and NAACL publications, statistical machine translation (SMT) dominates, and seems to fuel the field and associated research trends, yet there is growing interest in applying knowledge-based approaches that integrate some semantic information or concept and relations understanding within the systems. The co-authorship study demonstrates that the same few people and institutions (often focused on SMT research and typically centered in the East Coast of the US and in China) have dominated the field for the last 8 years. The analysis of the collaboration network shows that the ACL/NAACL community exhibits small world network characteristics, with interconnected groups of authors and a few authors with important central roles.