Michael Flor

Different Texts, Same Metaphors: Unigrams and Beyond

Current approaches to supervised learning of metaphor tend to use sophisticated features and restrict their attention to constructions and contexts where these features apply. In this paper, we describe the development of a supervised learning system to classify all content words in a running text as either being used metaphorically or not. We start by examining the performance of a simple unigram baseline that achieves surprisingly good results for some of the datasets. We then show how the recall of the system can be improved over this strong baseline.

Supervised Word-Level Metaphor Detection: Experiments with Concreteness and Reweighting of Examples

We present a supervised machine learning system for word-level classification of all content words in a running text as being metaphorical or non-metaphorical. The system provides a substantial improvement upon a previously published baseline, using re-weighting of the training examples and using features derived from a concreteness database. We observe that while the first manipulation was very effective, the second was only slightly so. Possible reasons for these observations are discussed.

Automated classification of collaborative problem solving interactions in simulated science tasks

We present a novel situational task that integrates collaborative problem solving behavior with testing in a science domain. Participants engage in discourse, which is used to evaluate their collaborative skills. We present initial experiments for automatic classification of such discourse, using a novel classification schema. Considerable accuracy is achieved with just lexical features. A speech-act classifier, trained on out-of-domain data, can also be helpful.

Semantic classifications for detection of verb metaphors

We investigate the effectiveness of semantic generalizations/classifications for capturing the regularities of the behavior of verbs in terms of their metaphoricity. Starting from orthographic word unigrams, we experiment with various ways of defining semantic classes for verbs (grammatical, resource-based, distributional) and measure the effectiveness of these classes for classifying all verbs in a running text as metaphor or non metaphor.

Topicality-Based Indices for Essay Scoring

In this paper, we address the problem of quantifying the overall extent to which a testtaker’s essay deals with the topic it is assigned (prompt). We experiment with a number of models for word topicality, and a number of approaches for aggregating word-level indices into text-level ones. All models are evaluated for their ability to predict the holistic quality of essays. We show that the best texttopicality model provides a significant improvement in a state-of-art essay scoring system. We also show that the findings of the relative merits of different models generalize well across three different datasets.

ETS Lexical Associations System for the COGALEX-4 Shared Task

We present an automated system that computes multi-cue associations and generates associated-word suggestions, using lexical co-occurrence data from a large corpus of English texts. The system performs expansion of cue words to their inflectional variants, retrieves candidate words from corpus data, finds maximal associations between candidates and cues, computes an aggregate score for each candidate, and outputs an n-best list of candidates. We present experiments using several measures of statistical association, two methods of score aggregation, ablation of resources and applying additional filters on retrieved candidates. The system achieves 18.6% precision on the COGALEX-4 shared task data. Results with additional evaluation methods are presented. We also describe an annotation experiment which suggests that the shared task may underestimate the appropriateness of candidate words produced by the corpus-based system.