{'fraction_non_alphanumeric': 0.021853146853146852, 'fraction_numerical': 0.013986013986013986, 'mean_word_length': 4.963541666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Labeled LDA Approach to Understanding the Dynamics of Collaboration

We propose a topic modeling approach to understand the nature of academic collaborations between individuals. Specifically, we use Labeled LDA (Ramage et al., 2009), a variation of the popular topic model Latent Dirichlet Allocation (Blei et al., 2003), to train a set of author-specific topics over the ACL corpus. The ACL corpus ranges from 1965 to 2009, and we train a separate topic model for each year over the papers published upto the given year. Once we have trained these models, we examine the influence present in a publication from each of its authors. We use a function of the cosine similarity score between the document’s term vector and each author’s topic signature in the year preceding the document’s publication as our metric. We suggest a theory of the different types of academic collaboration prevalent and discuss how our system performs at classifying high impact papers into these types. Finally, we demonstrate how our system can be applied to answer several questions regarding the nature of collaborations in Computational Linguistics research.