Accurately segmenting a citation string into fields for authors, titles, etc. is a challenging task because the output typically obeys various global constraints. Previous work has shown that modeling soft constraints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance. On the other hand, for imposing hard constraints, dual decomposition is a popular technique for efficient prediction given existing algorithms for unconstrained inference. We extend the technique to perform prediction subject to soft constraints. Moreover, with a technique for performing inference given soft constraints, it is easy to automatically generate large families of constraints and learn their costs with a simple convex optimization problem during training. This allows us to obtain substantial gains in accuracy on a new, challenging citation extraction dataset.
Initial work in machine learning for citation extraction used Markov models with no global constraints. Hidden Markov models (HMMs), were originally employed for automatically extracting information from research papers on the CORA dataset @cite_20 @cite_12 . Later, CRFs were shown to perform better on CORA, improving the results from the Hmm's token-level F1 of 86.6 to 91.5 with a CRF @cite_14 .
@cite_14 @cite_12 @cite_20
1534730506 1995577199 1568339100
With the increasing use of research paper search engines, such as CiteSeer, for both literature search and hiring decisions, the accuracy of such systems is of paramount importance. This paper employs Conditional Random Fields (CRFs) for the task of extracting various common fields from the headers and citation of research papers. The basic theory of CRFs is becoming well-understood, but best-practices for applying them to real-world data requires additional exploration. This paper makes an empirical exploration of several factors, including variations on Gaussian, exponential and hyperbolic-L1 priors for improved regularization, and several classes of features and Markov order. On a standard benchmark data set, we achieve new state-of-the-art performance, reducing error in average F1 by 36 , and word error rate by 78 in comparison with the previous best SVM results. Accuracy compares even more favorably against HMMs. This paper describes a simple method for extracting metadata fields from citations using hidden Markov models. The method is easy to implement and can achieve levels of precision and recall for heterogeneous citations comparable to or greater than other HMM-based methods. The method consists largely of string manipulation and otherwise depends only on an implementation of the Viterbi algorithm, which is widely available, and so can be implemented by diverse digital library systems. Statistical machine learning techniques, while well proven in fields such as speech recognition, are just beginning to be applied to the information extraction domain. We explore the use of hidden Markov models for information extraction tasks, specifically focusing on how to learn model structure from data and how to make the best use of labeled and unlabeled data. We show that a manually-constructed model that contains multiple states per extraction field outperforms a model with one state per field, and discuss strategies for learning the model structure automatically from data. We also demonstrate that the use of distantly-labeled data to set model parameters provides a significant improvement in extraction accuracy. Our models are applied to the task of extracting important fields from the headers of computer science research papers, and achieve an extraction accuracy of 92.9 .