Making Latent SVMstruct Practical for Coreference Resolution
Authors: Iryna Haponchyk, Alessandro Moschitti (Università di Trento, Qatar Computing Research Institute, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The recent work on coreference resolution has shown a renewed interest in the structured perceptron model, which seems to achieve the state of the art in this field. Interestingly, while SVMs are known to generally provide higher accu-racy than a perceptron, according to pre-vious work and theoretical findings, no re-cent paper currently describes the use of SVMstruct for coreference resolution. In this paper, we address this question by solving some technical problems at both theoretical and algorithmic level enabling the use of SVMs for coreference resolu-tion and other similar structured output tasks (e.g., based on clustering).

Introduction
Coreference resolution (CR) is a complex task, in which document phrases (mentions) are partitioned into equivalence sets. It has recently been approached by applying learning algorithms operating in structured output spaces (Tsochantaridis et al., 2004). Considering the nature of the problem, i.e., the NP-hardness of finding optimal mention clusters, the task has been reformulated as a spanning graph problem. First, Yu and Joachims (2009) proposed to (i) represent all possible mention clusters with fully connected undirected graphs and (ii) infer document mention cluster sets by applying Kruskal’s spanning algorithm (Kruskal, 1956). Since the same clustering can be obtained from multiple spanning forests (there is no one-to-one correspondence), these latter are treated as hidden or latent variables. Therefore, an extension of the structural SVM – Latent SVMstruct (LSVM) – was designed to include these structures in the learning procedure. Later, Fernandes et al. (2012) presented their CR system having a resembling architecture. They do inference on a directed candidate graph using the algorithm of Edmonds (1967). This modeling coupled with the latent structured perceptron delivered state-of-the-art results in the CoNLL-2012 Shared Task (Pradhan et al., 2012). To the best of our knowledge, there is no previous work on a comparison of the two methods, and the LSVM approach of Yu and Joachims has not been applied to the CoNLL data. In our work, we aim, firstly, at evaluating LSVM with respect to the recent benchmark standards (corpus and evaluation metrics defined by the CoNLL-shared task) and, secondly, at understanding the differences and advantages of the two structured learning models. In a closer look at the LSVM implementation1, we found out that it is restricted to inference on a fully-connected graph. Thus, we provide an extension of the algorithm enabling to operate on an arbitrary graph: this is very important as all the best CR models exploit heuristics to prefilter edges of the CR graph. Therefore our modification of LSVM allows us to use it with powerful heuristics, which greatly contribute to the achievement of the state of the art. Regarding the comparison with the latent perceptron of Fernandes et al. (2012), the results of our experiments provide evidence that the latent trees derived by Edmonds’ spanning tree algorithm better capture the nature of CR. Therefore, we speculate that the use of this spanning tree algorithm within LSVM may produce higher results than those of the current perceptron algorithm.

Conclusion
We have performed a comparative analysis of the structured prediction frameworks for coref-erence resolution. Our experiments reveal that the graph modelling of Fernandes et al. and Ed-monds’ spanning algorithm seem to tackle the task more specifically. As a short-term future work, we intend to verify if LSVM benefits from us-ing Edmonds’ algorithm. We have also enabled the LSVM implementation to operate on partial graphs, which allows the framework to be com-bined with different filtering strategies and facili-tates its comparison with other systems.