Deriving Concept Hierarchies From Text

Sanderson, Mark; Croft, Bruce

doi:10.21236/ada439413

Cited by 111 publications

(192 citation statements)

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“…Knowledge acquisition, a field at the intersection of psychology and computer science, studies the protocols individuals use to acquire, store, and analyze information (Iria 2009;Schnotz and Kürschner 2008;Ferstl and Von Cramon 2007;Cimiano et al 2005;Sanderson and Croft 1999;Kintsch and Van Dijk 1978). Using ideas developed in this field, we develop and apply an acquisition protocol for the Code.…”

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confidence: 99%

Measuring the complexity of the law: the United States Code

Katz

Bommarito

2014

Artif Intell Law

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Einstein's razor, a corollary of Ockham's razor, is often paraphrased as follows: make everything as simple as possible, but not simpler. This rule of thumb describes the challenge that designers of a legal system face-to craft simple laws that produce desired ends, but not to pursue simplicity so far as to undermine those ends. Complexity, simplicity's inverse, taxes cognition and increases the likelihood of suboptimal decisions. In addition, unnecessary legal complexity can drive a misallocation of human capital toward comprehending and complying with legal rules and away from other productive ends. While many scholars have offered descriptive accounts or theoretical models of legal complexity, most empirical research to date has been limited to simple measures of size, such as the number of pages in a bill. No extant research rigorously applies a meaningful model to real data. As a consequence, we have no reliable means to determine whether a new bill, regulation, order, or precedent substantially effects legal complexity. In this paper, we begin to address this need by developing a proposed empirical framework for measuring relative legal complexity. This framework is based on ''knowledge acquisition'', an approach at the intersection of psychology and computer science, which can take into account the structure, language, and interdependence of law. We then demonstrate the descriptive value of this framework by applying it to the U.S. Code's Titles, scoring and ranking them by their relative complexity. We measure various features of a title including its structural size, the net flow of its intra-title citations and its linguistic entropy. Our framework is flexible, intuitive, and transparent, and we offer this approach as a first step in developing a practical methodology for assessing legal complexity.

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confidence: 99%

Measuring the complexity of the law: the United States Code

Katz

Bommarito

2014

Artif Intell Law

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“…Thus we regard statistics-based approaches to be more suitable to our problem than database-based approaches that mainly consider general word relations. Here we consider a coverage-based measure which is similar to the one used in (Sanderson & Croft, 1999). In (Sanderson & Croft, 1999), the association of two words is defined by two conditions: P(a|b) P 0.8 and P(b|a) < P(a|b).…”

Section: Identifying Word Relationsmentioning

confidence: 99%

“…Here we consider a coverage-based measure which is similar to the one used in (Sanderson & Croft, 1999). In (Sanderson & Croft, 1999), the association of two words is defined by two conditions: P(a|b) P 0.8 and P(b|a) < P(a|b). Word a subsumes word b if the documents in which b occurs are a subset, or nearly a subset, of the documents in which a occurs.…”

Section: Identifying Word Relationsmentioning

confidence: 99%

“…In document summarization, sometimes a document set just consists of only a few documents (for example, 10 documents per set in the DUC 2004 data). Therefore, to get more available information, we intend to study the sentence-level co-occurrence statistics in our study instead of the document-level statistics that are used in previous studies (Kummamuru, Lotlikar, Roy, Singal, & Krishnapuram, 2004;Sanderson & Croft, 1999).…”

Section: Identifying Word Relationsmentioning

confidence: 99%

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A progressive sentence selection strategy for document summarization

Ouyang¹,

Li²,

Zhang³

et al. 2013

Information Processing & Management

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“…In a hierarchy, the concept node may contain very little information, e.g., only the concept's name; hence, the conventional document-based hierarchy classification method [4,25] is not very effective in this situation due to the lack of features. Unsupervised approaches, such as similarity-based methods, may be able to solve the problem of insufficient information by inserting a new term based on its similarity (e.g., content-based point-wise mutual information) to the other concept terms.…”

Section: Introductionmentioning

confidence: 99%