An Information Extraction Approach to Reorganizing and Summarizing Specifications

Materials and Process Specifications are complex semi-structured documents containing numeric data, text, and images. This article describes a coarse-grain extraction technique to automatically reorganize and summarize spec content. Specifically, a strategy for semantic-markup, to capture content within a semantic ontology, relevant to semi-automatic extraction, has been developed and experimented with. The working prototypes were built in the context of Cohesia\u27s existing software infrastructure, and use techniques from Information Extraction, XML technology, etc

Equality in the Streets: Using Proportionality Analysis to Regulate Street Policing

The racially disparate impact and individual and collective costs of stop and frisk, misdemeanor arrests, and pretextual traffic stops have been well documented. Less widely noticed is the contrast between Supreme Court case law permitting these practices and the Court\u27s recent tendency to strictly regulate technologically enhanced searches that occur outside the street policing setting and that--coincidentally or not--happen to be more likely to affect the middle class. If, as the Court has indicated, electronic tracking and searches of digital records require probable cause that evidence of crime will be found, stops and frisks should also require probable cause that a crime has been committed (in the case of stops) or that evidence of crime will be found (in the case of post-detention searches). This equalization of regulatory regimes not only fits general notions of fairness. It is also mandated by the Fourth Amendment\u27s Reasonableness Clause and the Court\u27s cases construing it, which endorse a proportionality principle that requires that the justification for a search or seizure be roughly proportionate to its intrusiveness

Plausible Cause : Explanatory Standards in the Age of Powerful Machines

The Fourth Amendment\u27s probable cause requirement is not about numbers or statistics. It is about requiring the police to account for their decisions. For a theory of wrongdoing to satisfy probable cause-and warrant a search or seizure-it must be plausible. The police must be able to explain why the observed facts invite an inference of wrongdoing, and judges must have an opportunity to scrutinize that explanation. Until recently, the explanatory aspect of Fourth Amendment suspicion- plausible cause -has been uncontroversial, and central to the Supreme Court\u27s jurisprudence, for a simple reason: explanations have served, in practice, as a guarantor of statistical likelihood. In other words, forcing police to articulate theories of wrongdoing is the means by which courts have traditionally ensured that (roughly) the right persons, houses, papers, and effects are targeted for intrusion. Going forward, however, technological change promises to disrupt the harmony between explanatory standards and statistical accuracy. Powerful machines enable a previously impossible combination: accurate predictions unaccompanied by explanations. As that change takes hold, we will need to think carefully about why explanation-giving matters. When judges assess the sufficiency of explanations offered by police (and other officials), what are they doing? If the answer comes back to error­ reduction-if the point of judicial oversight is simply to maximize the overall number of accurate decisions-machines could theoretically do the job as well as, if not better than, humans. But if the answer involves normative goals beyond error-reduction, automated tools-no matter their power-will remain, at best, partial substitutes for judicial scrutiny. This Article defends the latter view. I argue that statistical accuracy, though important, is not the crux of explanation-giving. Rather, explanatory standards-like probable cause-hold officials accountable to a plurality of sometimes-conflicting constitutional and rule-of-law values that, in our legal system, bound the scope of legitimate authority. Error-reduction is one such value. But there are many others, and sometimes the values work at cross purposes. When judges assess explanations, they navigate a space of value­pluralism: they identify which values are at stake in a given decisional environment and ask, where necessary, if those values have been properly balanced. Unexplained decisions render this process impossible and, in so doing, hobble the judicial role. Ultimately, that role has less to do with analytic power than practiced wisdom. A common argument against replacing judges, and other human experts, with intelligent machines is that machines are not (yet) intelligent enough to take up the mantle. In the age of powerful algorithms, however, this turns out to be a weak-and temporally limited-claim. The better argument, I suggest in closing, is that judging is not solely, or even primarily, about intelligence. It is about prudence

Plausible Cause : Explanatory Standards in the Age of Powerful Machines

Much scholarship in law and political science has long understood the U.S. Supreme Court to be the apex court in the federal judicial system, and so to relate hierarchically to lower federal courts. On that top-down view, exemplified by the work of Alexander Bickel and many subsequent scholars, the Court is the principal, and lower federal courts are its faithful agents. Other scholarship takes a bottom-up approach, viewing lower federal courts as faithless agents or analyzing the percolation of issues in those courts before the Court decides. This Article identifies circumstances in which the relationship between the Court and other federal courts is best viewed as neither top-down nor bottom-up, but side-by-side. When the Court intervenes in fierce political conflicts, it may proceed in stages, interacting with other federal courts in a way that is aimed at enhancing its public legitimacy. First, the Court renders a decision that is interpreted as encouraging, but not requiring, other federal courts to expand the scope of its initial ruling. Then, most federal courts do expand the scope of the ruling, relying upon the Court\u27s initial decision as authority for doing so. Finally, the Court responds by invoking those district and circuit court decisions as authority for its own more definitive resolution. That dialectical process, which this Article calls reciprocal legitimation, was present along the path from Brown v. Board of Education to the unreasoned per curiams, from Baker v. Carr to Reynolds v. Sims, and from United States v. Windsor to Obergefell v. Hodges-as partially captured by Appendix A to the Court\u27s opinion in Obergefell and the opinion\u27s several references to it. This Article identifies the phenomenon of reciprocal legitimation, explains that it may initially be intentional or unintentional, and examines its implications for theories of constitutional change and scholarship in federal courts and judicial politics. Although the Article\u27s primary contribution is descriptive and analytical, it also normatively assesses reciprocal legitimation given the sacrifice of judicial candor that may accompany it. A Coda examines the likelihood and desirability of reciprocal legitimation in response to President Donald Trump\u27s derision of the federal courts as political and so illegitimate

New Mexico Daily Lobo, Volume 088, No 69, 11/28/1983

New Mexico Daily Lobo, Volume 088, No 69, 11/28/1983https://digitalrepository.unm.edu/daily_lobo_1983/1150/thumbnail.jp

Near-duplicate news detection using named entities

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 60-65.The number of web documents has been increasing in an exponential manner for more than a decade. In a similar way, partially or completely duplicate documents appear frequently on the Web. Advances in the Internet technologies have increased the number of news agencies. People tend to read news from news portals that aggregate documents from different sources. The existence of duplicate or near-duplicate news in these portals is a common problem. Duplicate documents create redundancy and only a few users may want to read news containing identical information. Duplicate documents decrease the efficiency and effectiveness of search engines. In this thesis, we propose and evaluate a new near-duplicate news detection algorithm: Tweezer. In this algorithm, named entities and the words that appear before and after them are used to create document signatures. Documents sharing the same signatures are considered as a nearduplicate. For named entity detection, we introduce a method called Turkish Named Entity Recognizer, TuNER. For the evaluation of Tweezer, a document collection is created using news articles obtained from Bilkent News Portal. In the experiments, Tweezer is compared with I-Match, which is a state-of-the-art near-duplicate detection algorithm that creates document signatures using Inverse Document Frequency, IDF, values of terms. It is experimentally shown that the effectiveness of Tweezer is statistically significantly better than that of I-Match by using a cost function that combines false alarm and miss rate probabilities, and the F-measure that combines precision and recall. Furthermore, Tweezer is at least 7% faster than I-Match.Uyar, ErkanM.S

A Study of Chinese Named Entity and Relation Identification in a Specific Domain

This thesis aims at investigating automatic identification of Chinese named entities (NEs) and their relations (NERs) in a specific domain. We have proposed a three-stage pipeline computational model for the error correction of word segmentation and POS tagging, NE recognition and NER identification. In this model, an error repair module utilizing machine learning techniques is developed in the first stage. At the second stage, a new algorithm that can automatically construct Finite State Cascades (FSC) from given sets of rules is designed. As a supplement, the recognition strategy without NE trigger words can identify the special linguistic phenomena. In the third stage, a novel approach - positive and negative case-based learning and identification (PNCBL&I) is implemented. It pursues the improvement of the identification performance for NERs through simultaneously learning two opposite cases and automatically selecting effective multi-level linguistic features for NERs and non-NERs. Further, two other strategies, resolving relation conflicts and inferring missing relations, are also integrated in the identification procedure.Diese Dissertation ist der Forschung zur automatischen Erkennung von chinesischen Begriffen (named entities, NE) und ihrer Relationen (NER) in einer spezifischen Domäne gewidmet. Wir haben ein Pipelinemodell mit drei aufeinanderfolgenden Verarbeitungsschritten für die Korrektur der Fehler der Wortsegmentation und Wortartmarkierung, NE-Erkennung, und NER-Identifizierung vorgeschlagen. In diesem Modell wird eine Komponente zur Fehlerreparatur im ersten Verarbeitungsschritt verwirklicht, die ein machinelles Lernverfahren einsetzt. Im zweiten Stadium wird ein neuer Algorithmus, der die Kaskaden endlicher Transduktoren aus den Mengen der Regeln automatisch konstruieren kann, entworfen. Zusätzlich kann eine Strategie für die Erkennung von NE, die nicht durch das Vorkommen bestimmer lexikalischer Trigger markiert sind, die spezielle linguistische Phänomene identifizieren. Im dritten Verarbeitungsschritt wird ein neues Verfahren, das auf dem Lernen und der Identifizierung positiver und negativer Fälle beruht, implementiert. Es verfolgt die Verbesserung der NER-Erkennungsleistung durch das gleichzeitige Lernen zweier gegenüberliegenden Fälle und die automatische Auswahl der wirkungsvollen linguistischen Merkmale auf mehreren Ebenen für die NER und Nicht-NER. Weiter werden zwei andere Strategien, die Lösung von Konflikten in der Relationenerkennung und die Inferenz von fehlenden Relationen, auch in den Erkennungsprozeß integriert