Using Readers to Identify Lexical Cohesive Structures in Texts

This paper describes a reader-based experiment on lexical cohesion, detailing the task given to readers and the analysis of the experimental data. We conclude with discussion of the usefulness of the data in future research on lexical cohesion

Using People and WordNet to Measure Semantic Relatedness

This technical report describes in some detail (1) the creation of a dataset for testing the degree of relatedness between concepts out of the data from Beigman Klebanov and Shamir's lexical cohesion experiment [3, 5, 6], and (2) a new measure of semantic relatedness based on WordNet. We welcome comments on this manuscript; however, please refrain from citing it, but rather the concise published version [4]. This report is intended to accompany the published paper with more thorough technical detail to enable replication of the method

Measuring Semantic Relatedness Using People and WordNet

In this paper, we (1) propose a new dataset for testing the degree of relatedness between pairs of words; (2) propose a new WordNet-based measure of relatedness, and evaluate it on the new dataset

Annotating Concept Mention Patterns

This paper presents an annotation project aimed at elicitation of concept interconnections within people's common knowledge. Motivation, annotation scheme and relevant previous work are discussed and some potential applications are suggested

Analyzing disagreements

We address the problem of distinguishing between two sources of disagreement in annotations: genuine subjectivity and slip of attention. The latter is especially likely when the classification task has a default class, as in tasks where annotators need to find instances of the phenomenon of interest, such as in a metaphor detection task discussed here. We apply and extend a data analysis technique proposed by Beigman Klebanov and Shamir (2006) to first distill reliably deliberate (non-chance) annotations and then to estimate the amount of attention slips vs genuine disagreement in the reliably deliberate annotations.