This work has been funded in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI 12 RC 2289 (INSIGHT) and by Enterprise Ireland and the IDA under the Technology Centre Programme [Grant TC-2012-009]
Most work on taxonomy generation in the legal domain has involved manual construction of concept hierarchies by legal experts @cite_2 . This task, besides being both tedious and costly in terms of time and qualified human resources, is also not easily adaptable to changes. Systems for automatic legal-domain taxonomy creation have on the contrary received very low attention so far. Only @cite_4 worked on a similar task, and developed a machine learning-based system for scalable document classification. They constructed a hierarchical topic schemes of areas of laws and used proprietary methods of scoring and ranking to classify documents. However, this work has been deposited as a patent and is not freely available.
@cite_4 @cite_2
2243633279 2144108169 2098269849 2100132811
Recently, taxonomy has attracted much attention. Both automatic construction solutions and human-based computation approaches have been proposed. The automatic methods suffer from the problem of either low precision or low recall and human computation, on the other hand, is not suitable for large scale tasks. Motivated by the shortcomings of both approaches, we present a hybrid framework, which combines the power of machine-based approaches and human computation (the crowd) to construct a more complete and accurate taxonomy. Specifically, our framework consists of two steps: we first construct a complete but noisy taxonomy automatically, then crowd is introducedto adjust the entity positions in the constructed taxonomy. However, the adjustment is challenging as the budget (money) for asking the crowd is often limited. In our work, we formulatethe problem of finding the optimal adjustment as an entityselection optimization (ESO) problem, which is proved to beNP-hard. We then propose an exact algorithm and a moreefficient approximation algorithm with an approximation ratioof 1 2(1-1 e). We conduct extensive experiments on real datasets, the results show that our hybrid approach largely improves the recall of the taxonomy with little impairment for precision. We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word's coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10,000 novel synsets to WordNet 2.1 at 84 precision, a relative error reduction of 70 over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23 relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs. An economic, scalable machine learning system and process perform document (concept) classification with high accuracy using large topic schemes, including large hierarchical topic schemes. One or more highly relevant classification topics is suggested for a-given document (concept) to be classified. The invention includes training and concept classification processes. The invention also provides methods that may be used as part of the training and or concept classification processes, including: a method of scoring the relevance of features in training concepts, a method of ranking concepts based on relevance score, and a method of voting on topics associated with an input concept. In a preferred embodiment, the invention is applied to the legal (case law) domain, classifying legal concepts (rules of law) according to a proprietary legal topic classification scheme (a hierarchical scheme of areas of law). We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation. Our model incorporates heterogeneous relational evidence about both hypernymy and siblinghood, captured by semantic features based on patterns and statistics from Web n-grams and Wikipedia abstracts. For efficient inference over taxonomy structures, we use loopy belief propagation along with a directed spanning tree algorithm for the core hypernymy factor. To train the system, we extract sub-structures of WordNet and discriminatively learn to reproduce them, using adaptive subgradient stochastic optimization. On the task of reproducing sub-hierarchies of WordNet, our approach achieves a 51 error reduction over a chance baseline, including a 15 error reduction due to the non-hypernym-factored sibling features. On a comparison setup, we find up to 29 relative error reduction over previous work on ancestor F1.