399 research outputs found
Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery
Opinionated text often involves attributes such as authorship and location
that influence the sentiments expressed for different aspects. We posit that
structural and semantic correspondence is both prevalent in opinionated text,
especially when associated with attributes, and crucial in accurately revealing
its latent aspect and sentiment structure. However, it is not recognized by
existing approaches.
We propose Trait, an unsupervised probabilistic model that discovers aspects
and sentiments from text and associates them with different attributes. To this
end, Trait infers and leverages structural and semantic correspondence using a
Markov Random Field. We show empirically that by incorporating attributes
explicitly Trait significantly outperforms state-of-the-art baselines both by
generating attribute profiles that accord with our intuitions, as shown via
visualization, and yielding topics of greater semantic cohesion.Comment: EMNLP 201
Moral Judgments in Narratives on Reddit: Investigating Moral Sparks via Social Commonsense and Linguistic Signals
Given the increasing realism of social interactions online, social media
offers an unprecedented avenue to evaluate real-life moral scenarios. We
examine posts from Reddit, where authors and commenters share their moral
judgments on who is blameworthy. We employ computational techniques to
investigate factors influencing moral judgments, including (1) events
activating social commonsense and (2) linguistic signals. To this end, we focus
on excerpt-which we term moral sparks-from original posts that commenters
include to indicate what motivates their moral judgments. By examining over
24,672 posts and 175,988 comments, we find that event-related negative personal
traits (e.g., immature and rude) attract attention and stimulate blame,
implying a dependent relationship between moral sparks and blameworthiness.
Moreover, language that impacts commenters' cognitive processes to depict
events and characters enhances the probability of an excerpt become a moral
spark, while factual and concrete descriptions tend to inhibit this effect
Ontologies for Agents
An ontology is a computational model of some portion of the world. It is often captured in some form of a semantic network-a graph whose nodes are concepts or individual objects and whose arcs represent relationships or associations among the concepts. This network is augmented by properties and attributes, constraints, functions, and rules that govern the behavior of the concepts. Formally, an ontology is an agreement about a shared conceptualization, which includes frameworks for modeling domain knowledge and agreements about the representation of particular domain theories. Definitions associate the names of entities in a universe of discourse (for example, classes, relations, functions, or other objects) with human readable text describing what the names mean, and formal axioms that constrain the interpretation and well formed use of these names. For information systems, or for the Internet, ontologies can be used to organize keywords and database concepts by capturing the semantic relationships among the keywords or among the tables and fields in a database. The semantic relationships give users an abstract view of an information space for their domain of interest
Cupid:commitments in relational algebra
We propose Cupid, a language for specifying commitments that supports their information-centric aspects, and offers crucial benefits. One, Cupid is first-order, enabling a systematic treatment of commitment instances. Two, Cupid supports features needed for real-world scenarios such as deadlines, nested commitments, and complex event expressions for capturing the lifecycle of commitment instances. Three, Cupid maps to relational database queries and thus provides a set-based semantics for retrieving commitment instances in states such as being violated, discharged, and so on. We prove that Cupid queries are safe. Four, to aid commitment modelers, we propose the notion of well-identified commitments, and finitely violable and finitely expirable commitments. We give syntactic restrictions for obtaining such commitments
Internet-Based Agents: Applications and Infrastructure
Software agents are mitigating the complexity of modern information systems—technically by providing a locus for managing information subsets, and psychologically by providing an abstraction for human interaction with them
Generalized commitment alignment
The interoperability of interacting components means that their expectations of each other remain in agreement. A commitment captures what one agent (its creditor) may expect from another agent (its debtor). Chopra and Singh (C&S) motivate commitment alignment as a meaning-based form of interoperation and show how to ensure alignment among agents despite asynchrony. Although C&S’s approach demonstrates the key strengths of relying on commitment semantics, it suffers from key shortcomings, which limit its applicability in practice. One, C&S do not model commitments properly, causing unacceptable interference between commitments in different transactions. Two, they require that the communication infrastructure guarantee first-in first-out (FIFO) delivery of messages for every agent-agent channel. Three, C&S guarantee alignment only in quiescent states (where no messages are in transit); however, such states may never obtain in enactments of real systems. Our approach retains and enhances C&S’s key strengths and avoids their shortcomings by providing a declarative semantics-based generalized treatment of alignment. Specifically, we (1) motivate a declarative notion of alignment relevant system states termed completeness; (2) prove that it coincides with alignment; and (3) provide the computations by which a system of agents provably progresses toward alignment assuming eventual delivery of messages
Cognitive Agents
Several researchers have proposed using cognitive concepts as a semantic basis for agent communications (M.N. Huhns and M.P. Singh, 1997). One of the leading candidates for such a semantics is based on Arcol, the communication language used within Artimis. Interestingly, this application (not only of Arcol, but also in general) appears extremely misguided. The intentional concepts are well suited to designing agents, but are not suited to giving a basis to a public, standardizable view of communication. A challenge for using the cognitive concepts is that although they are natural in several respects and can guide implementations, full blown implementations that try to be faithful to every aspect of the model can end up being computationally demanding. As the cognitive concepts are put to use in real applications, the principles for simplifying the implementations will emerge. In any case, because of their naturalness to humans, the cognitive concepts are here to stay, and we will do well to consider them in the design of our agents
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