Marc Tomlinson


Introducing the LCC Metaphor Datasets
Michael Mohler | Mary Brunson | Bryan Rink | Marc Tomlinson
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this work, we present the Language Computer Corporation (LCC) annotated metaphor datasets, which represent the largest and most comprehensive resource for metaphor research to date. These datasets were produced over the course of three years by a staff of nine annotators working in four languages (English, Spanish, Russian, and Farsi). As part of these datasets, we provide (1) metaphoricity ratings for within-sentence word pairs on a four-point scale, (2) scored links to our repository of 114 source concept domains and 32 target concept domains, and (3) ratings for the affective polarity and intensity of each pair. Altogether, we provide 188,741 annotations in English (for 80,100 pairs), 159,915 annotations in Spanish (for 63,188 pairs), 99,740 annotations in Russian (for 44,632 pairs), and 137,186 annotations in Farsi (for 57,239 pairs). In addition, we are providing a large set of likely metaphors which have been independently extracted by our two state-of-the-art metaphor detection systems but which have not been analyzed by our team of annotators.


A Corpus of Rich Metaphor Annotation
Jonathan Gordon | Jerry Hobbs | Jonathan May | Michael Mohler | Fabrizio Morbini | Bryan Rink | Marc Tomlinson | Suzanne Wertheim
Proceedings of the Third Workshop on Metaphor in NLP


Capturing Cultural Differences in Expressions of Intentions
Marc Tomlinson | David Bracewell | Wayne Krug
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

A Novel Distributional Approach to Multilingual Conceptual Metaphor Recognition
Michael Mohler | Bryan Rink | David Bracewell | Marc Tomlinson
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

Semi-supervised methods for expanding psycholinguistics norms by integrating distributional similarity with the structure of WordNet
Michael Mohler | Marc Tomlinson | David Bracewell | Bryan Rink
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this work, we present two complementary methods for the expansion of psycholinguistics norms. The first method is a random-traversal spreading activation approach which transfers existing norms onto semantically related terms using notions of synonymy, hypernymy, and pertainymy to approach full coverage of the English language. The second method makes use of recent advances in distributional similarity representation to transfer existing norms to their closest neighbors in a high-dimensional vector space. These two methods (along with a naive hybrid approach combining the two) have been shown to significantly outperform a state-of-the-art resource expansion system at our pilot task of imageability expansion. We have evaluated these systems in a cross-validation experiment using 8,188 norms found in existing pscholinguistics literature. We have also validated the quality of these combined norms by performing a small study using Amazon Mechanical Turk (AMT).

#mygoal: Finding Motivations on Twitter
Marc Tomlinson | David Bracewell | Wayne Krug | David Hinote
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Our everyday language reflects our psychological and cognitive state and effects the states of other individuals. In this contribution we look at the intersection between motivational state and language. We create a set of hashtags, which are annotated for the degree to which they are used by individuals to mark-up language that is indicative of a collection of factors that interact with an individual’s motivational state. We look for tags that reflect a goal mention, reward, or a perception of control. Finally, we present results for a language-model based classifier which is able to predict the presence of one of these factors in a tweet with between 69\% and 80\% accuracy on a balanced testing set. Our approach suggests that hashtags can be used to understand, not just the language of topics, but the deeper psychological and social meaning of a tweet.


Semantic Signatures for Example-Based Linguistic Metaphor Detection
Michael Mohler | David Bracewell | Marc Tomlinson | David Hinote
Proceedings of the First Workshop on Metaphor in NLP


Pursing power in Arabic on-line discussion forums
Marc Tomlinson | David Bracewell | Mary Draper | Zewar Almissour | Ying Shi | Jeremy Bensley
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We present a novel corpus for identifying individuals within a group setting that are attempting to gain power within the group. The corpus is entirely in Arabic and is derived from the on-line WikiTalk discussion forums. Entries on the forums were annotated at multiple levels, top-level annotations identified whether an individual was pursuing power on the forum, and low level annotations identified linguistic indicators that signaled an individuals social intentions. An analysis of our annotations reflects a high-degree of overlap between current theories on power and conflict within a group and the behavior of individuals within the transcripts. The described datasource provides an appropriate means for modeling an individual's pursuit of power within an on-line discussion group and also allows for enumeration and validation of current theories on the ways in which individuals strive for power.

Annotation of Adversarial and Collegial Social Actions in Discourse
David Bracewell | Marc Tomlinson | Mary Brunson | Jesse Plymale | Jiajun Bracewell | Daniel Boerger
Proceedings of the Sixth Linguistic Annotation Workshop

Identification of Social Acts in Dialogue
David Bracewell | Marc Tomlinson | Hui Wang
Proceedings of COLING 2012

The Language of Power and its Cultural Influence
David Bracewell | Marc Tomlinson
Proceedings of COLING 2012: Posters