EMNLP versus ACL: Analyzing NLP research over time

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2002–2006, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. EMNLP versus ACL: Analyzing NLP Research Over Time Sujatha Das Gollapalli, Xiao-Li Li Institute for Infocomm Research, A*STAR, Singapore {gollapallis,xlli}@i2r.a-star.edu.sg Abstract The conferences ACL (Association for Computational Linguistics) and EMNLP (Empirical Methods in Natural Language Processing) rank among the premier venues that track the research develop- ments in Natural Language Processing and Computational Linguistics. In this paper, we present a study on the research pa- pers of approximately two decades from these two NLP conferences. We apply keyphrase extraction and corpus analysis tools to the proceedings from these venues and propose probabilistic and vector-based representations to represent the topics published in a venue for a given year. Next, similarity metrics are studied over pairs of venue representations to capture the progress of the two venues with respect to each other and over time. 1 Introduction Scientific findings in a subject-area are typically published in conferences, journals, patents, and books in that domain. These research docu- ments constitute valuable resources from the per- spective of data mining applications. For in- stance, the citation links among research docu- ments are used in computing bibliometric quan- tities for authors (Alonso et al., 2009) whereas topic models on research corpora are used to distinguish between influential and impactful re- searchers (Kataria et al., 2011) and to capture tem- poral topic trends (He et al., 2009). Despite several potential benefits mentioned above and the free availability of most research proceedings in NLP through the ACL Anthology1, the topical and temporal aspects of this corpus are yet to be fully studied in current literature. In this paper, we present our study on research proceed- ings of approximately two decades from two lead- ing NLP conferences, namely ACL and EMNLP, to complement a previous study on this topic by Hall et al (2008). To the best of our knowledge, we are the first to characterize the developments in the NLP domain using a comparative study of two of its leading publication venues. Our contri- butions are summarized below: 1. We represent the NLP research corpus from approximately two decades as a keyphrase- document matrix and apply Latent Dirichlet Allocation (Blei et al., 2003) to extract co- herent topics from it (Newman et al., 2010). 2. We propose two novel representations for summarizing the venue proceedings in a given year. (1) The probabilistic represen- tation expresses each venue as a probability distribution over topics, whereas (2) the TP- ICP representation captures topics that are the major focus in the venue for a particu- lar year via Topic Proportion (TP) as well as topic importance as measured with inverse corpus proportion (ICP). 3. We apply Jensen-Shannon divergence and cosine similarity on our proposed venue rep- resentations to analyze the venues over time. Specifically, we ask the following questions: What are the popular topics in ACL and EMNLP in a particular year? Is the topical focus …

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