We investigate the effectiveness of semantic generalizations/classifications for capturing the regularities of the behavior of verbs in terms of their metaphoric-ity. Starting from orthographic word unigrams, we experiment with various... more
We investigate the effectiveness of semantic generalizations/classifications for capturing the regularities of the behavior of verbs in terms of their metaphoric-ity. Starting from orthographic word unigrams, we experiment with various ways of defining semantic classes for verbs (grammatical, resource-based, dis-tributional) and measure the effectiveness of these classes for classifying all verbs in a running text as metaphor or non metaphor.
Research Interests:
Research Interests:
Page 1. UNT at ImageCLEF 2011: Relevance Models and Salient Semantic Analysis for Image Retrieval Miguel E. Ruiz1, Chee Wee Leong2 and Samer Hassan12 University of North Texas, 1 Department of Library and Information ...
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ABSTRACT
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... more
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.
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.
Research Interests:
"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... more
"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."
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."
Research Interests:
Research Interests:
Research Interests:
Traditional approaches to semantic relatedness are often restricted to text-based methods, which typically disregard other multimodal knowledge sources. In this paper, we propose a novel image-based metric to estimate the relatedness of... more
Traditional approaches to semantic relatedness are often restricted to text-based methods, which typically disregard other multimodal knowledge sources. In this paper, we propose a novel image-based metric to estimate the relatedness of words, and demonstrate the promise of this method through comparative evaluations on three standard datasets. We also show that a hybrid image-text approach can lead to improvements in word relatedness, confirming the applicability of visual cues as a possible orthogonal information source.