Recognizing cited facts and principles in legal judgements

In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, κ=0.65 and κ=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall κ of 0.72 with the human-annotated gold standard

Classification of the Stance in Online Debates Using the Dependency Relations Feature

Online discussion forums offer Internet users a medium for discussions about current political debates. The debate is a system of claims regarding interactivity and representation. Users make claims in an online discussion with superior content to support their position. Factual accuracy and emotional appeal are critical attributes used to convince readers. A key challenge in debate forums is to identify the participants’ stance, each of which is inter-dependent and inter-connected. This research work aims to construct a classifier that takes the linguistic features of the posts as input and outputs predictions for the stance label of each post. Three types of features which include Lexical, Dependency, and Morphology are used to detect the stance of the posts. Lexical features such as cue words are employed as surface features, and deep features include dependency and morphology features. Multinomial Naïve Bayes classifier is used to build a model for classifying stance and the Chi-Square method is used to select the good feature set. The performance of the stance classification system is evaluated in terms of accuracy. The result of stance labels for this proposed research represents as for and against by analyzing the surface and deep features that capture the content of a post

Against Conventional Wisdom

Conventional wisdom has it that truth is always evaluated using our actual linguistic conventions, even when considering counterfactual scenarios in which different conventions are adopted. This principle has been invoked in a number of philosophical arguments, including Kripke’s defense of the necessity of identity and Lewy’s objection to modal conventionalism. But it is false. It fails in the presence of what Einheuser (2006) calls c-monsters, or convention-shifting expressions (on analogy with Kaplan’s monsters, or context-shifting expressions). We show that c-monsters naturally arise in contexts, such as metalinguistic negotiations, where speakers entertain alternative conventions. We develop an expressivist theory—inspired by Barker (2002) and MacFarlane (2016) on vague predications and Einheuser (2006) on counterconventionals—to model these shifts in convention. Using this framework, we reassess the philosophical arguments that invoked the conventional wisdom

What is the Point of Persistent Disputes? The meta-analytic answer

Many philosophers regard the persistence of philosophical disputes as symptomatic of overly ambitious, ill-founded intellectual projects. There are indeed strong reasons to believe that persistent disputes in philosophy (and more generally in the discourse at large) are pointless. We call this the pessimistic view of the nature of philosophical disputes. In order to respond to the pessimistic view, we articulate the supporting reasons and provide a precise formulation in terms of the idea that the best explanation of persistent disputes entails that they are pointless. We then show how to answer the pessimistic argument. Taking a well-known mathematical controversy as our paradigm example, we argue that some persistent disputes reflect substantive disagreements at the “meta-analytic” level, i.e., disagreements about the best way, among quite different candidates, to understand the topic at issue, and the best associated cluster of analytic truths one should accept concerning it. Moreover, our concrete example shows that such meta-analytic disagreements can in principle be settled and yield a genuine theoretical (as opposed to merely pragmatic) breakthrough. We conclude optimistically that persistent disputes can be an important means of fostering epistemic progress

Visible relations in online communities : modeling and using social networks

The Internet represents a unique opportunity for people to interact with each other across time and space, and online communities have existed long before the Internet's solidification in everyday living. There are two inherent challenges that online communities continue to contend with: motivating participation and organizing information. An online community's success or failure rests on the content generated by its users. Specifically, users need to continually participate by contributing new content and organizing existing content for others to be attracted and retained. I propose both participation and organization can be enhanced if users have an explicit awareness of the implicit social network which results from their online interactions. My approach makes this normally ``hidden" social network visible and shows users that these intangible relations have an impact on satisfying their information needs and vice versa. That is, users can more readily situate their information needs within social processes, understanding that the value of information they receive and give is influenced and has influence on the mostly incidental relations they have formed with others. First, I describe how to model a social network within an online discussion forum and visualize the subsequent relationships in a way that motivates participation. Second, I show that social networks can also be modeled to generate recommendations of information items and that, through an interactive visualization, users can make direct adjustments to the model in order to improve their personal recommendations. I conclude that these modeling and visualization techniques are beneficial to online communities as their social capital is enhanced by "weaving" users more tightly together