Abstract
We present heterogeneous networks as a way to unify lexical networks with relational data. We build a unified ACL Anthology network, tying together the citation, author collaboration, and term-cooccurence networks with affiliation and venue relations. This representation proves to be convenient and allows problems such as name disambiguation, topic modeling, and the measurement of scientific impact to be easily solved using only this network and off-the-shelf graph algorithms.
Highlights
Graph-based methods have been used to great effect in NLP, on problems such as word sense disambiguation (Mihalcea, 2005), summarization (Erkan and Radev, 2004), and dependency parsing (McDonald et al, 2005)
Throughout this paper, we will use the data from the ACL Anthology Network (AAN) (Bird et al, 2008; Radev et al, 2013), which contains additional metadata relationships not found in the ACL Anthology, as a typical heterogeneous network
We present a heterogeneous network treatment of the ACL Anthology Network and demonstrate several applications of it
Summary
Graph-based methods have been used to great effect in NLP, on problems such as word sense disambiguation (Mihalcea, 2005), summarization (Erkan and Radev, 2004), and dependency parsing (McDonald et al, 2005). In this paper we will demonstrate heterogeneous networks, networks with multiple different types of nodes and edges, along with several applications of them. The applications in this paper are not presented so much as robust attempts to out-perform the current state-of-the-art, but rather attempts at being competitive against top methods with little effort beyond the construction of the heterogeneous network. Throughout this paper, we will use the data from the ACL Anthology Network (AAN) (Bird et al, 2008; Radev et al, 2013), which contains additional metadata relationships not found in the ACL Anthology, as a typical heterogeneous network. The results in this paper should be generally applicable to other heterogeneous networks
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