Thursday, January 27, 2011

Summary of week 2 commentary

As the focus paper this week was a survey, everyone chose to read papers detailing one of the methods discussed in the focus paper. With one exception, the papers people chose to read either focused on metaphor detection or metaphor interpretation.

Dani, Alan, Dhananjay and I read papers on metaphor detection.
Dani's paper, Metaphor Identification Using Verb and Noun Clustering, combines a small amount of seed knowledge in the form of source-target domain mappings with word clustering in order to generalize those mappings. The clustering is done using parse information and a spectral clustering algorithm. To evaluate, they sampled randomly from the output of their system, and had human annotators judge the sampled sentences, obtaining a precision of .79.

Alan's paper, Comparing Semantic Role Labeling with Typed Dependency Parsing in Computational Metaphor Identification, focused on a slightly different task: finding patterns in text that commonly indicate the use of metaphor. The paper found that semantic role labels are slightly more useful than typed dependency arcs for extracting semantic relations from text, but overall, the paper mostly discussed the problem instead of their solution.
Dhananjay read the paper Catching Metaphors, which used a maximum entropy classifier to detect the metaphorical usage of verbs. The features used in the model were the prior belief of each verb being used metaphorically, and the type of the verb's arguments. The paper used WSJ data that they annotated. A high accuracy of 96.98% accuracy is reported, although this weakened by the fact that over 90% of the verbs that were annotated were marked as metaphorical.
I read the paper Hunting Elusive Metaphors Using Lexical Resources. This paper looked at a wider range of phenomena than other metaphor detection papers, nouns, verbs, and adjectives, but used relatively simplistic techniques. In order to discover IsA metaphors, they simply checked to see if the first noun was a hyponym of the second, using WordNet. For verb and adjective metaphors, they used a method based on computing the frequency of a noun's hyponyms occurring as arguments of the predicate. Their evaluation was not well explained, and they had unimpressive results.

Next, Matt, Michael, Dong, and Weisi read papers that looked at metaphor interpretation.
Matt and Michael read the paper

A Fluid Knowledge Representation for Understanding and Generating Creative Metaphors. This paper automatically creates a knowledge base from WordNet and uses that knowledge to find semantic links between seemingly unrelated nouns. They find facts of the forms is_ADJ:NOUN and VERBs:NOUN by looking at parsed dictionary entries. They then create a graph of nouns, with links between nouns that have very similar facts. Connected nouns in the graph are then considered to be semantically related. The paper does not address in detail the problem of using this information for the task of metaphor interpretation.
Dong and Weisi's paper,
Automatic Metaphor Interpretation as a Paraphrasing Task, addressed the problem of finding literal paraphrases of metaphorical verbs. They use a variety of methods to obtain and filter a list of possible paraphrases using WordNet similarity, likelihood given the context, and a selectional preference measure. They hand-annotated a set of sentences with a ranked list of possible verb paraphrases, and evaluated their system on 1st choice accuracy and mean reciprocal rank, getting an accuracy of .81. Weisi makes a comparison of this task to word sense disambiguation, noting that is much easier, since the problem of detecting metaphor is already taken care of.

Finally, Brendan read a paper about analogical reasoning, A Logical Approach to Reasoning by Analogy. This paper is not about metaphor, but rather about giving a precise account of reasoning by analogy. The paper gives a formal definition of reasoning by analogy, then generalizes it and discusses an implementation of it in a logical programming language. Brendan discusses how this might be useful in metaphor interpretation, and how the task of metaphor detection is not particularly interesting by itself.

Overall, the topic of metaphor in NLP seems to suffer from a lack of a good definition, and no standardization of evaluation. None of the papers present results that are comparable to any of the others, so it is hard to say conclusively what sorts of techniques are preferable.

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