It has been argued that analogy is the core of cognition. In AI research, algorithms for analogy are often limited by the need for hand-coded high-level representations as input. An alternative approach is to use high-level perception, in which high-level representations are automatically generated from raw data. Analogy perception is the process of recognizing analogies using high-level perception. We present PairClass, an algorithm for analogy perception that recognizes lexical proportional analogies using representations that are automatically generated from a large corpus of raw textual data. A proportional analogy is an analogy of the form A:B::C:D, meaning "A is to B as C is to D". A lexical proportional analogy is a proportional analogy with words, such as carpenter:wood::mason:stone. PairClass represents the semantic relations between two words using a high-dimensional feature vector, in which the elements are based on frequencies of patterns in the corpus. PairClass recognizes analogies by applying standard supervised machine learning techniques to the feature vectors. We show how seven different tests of word comprehension can be framed as problems of analogy perception and we then apply PairClass to the seven resulting sets of analogy perception problems. We achieve competitive results on all seven tests. This is the first time a uniform approach has handled such a range of tests of word comprehension.
One of the tasks in SemEval 2007 was the classification of semantic relations between nominals @cite_2 . SemEval 2007 was the Fourth International Workshop on Semantic Evaluations. More information on Task 4, the classification of semantic relations between nominals, is available at http: purl.org net semeval -task4. The problem is to classify semantic relations between nominals (nouns and noun compounds) in the context of a sentence. The task attracted 14 teams who created 15 systems, all of which used supervised machine learning with features that were lexicon-based, corpus-based, or both.
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The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of relations between pairs of words in a text. We present an evaluation task designed to provide a framework for comparing different approaches to classifying semantic relations between nominals in a sentence. This is part of SemEval, the 4th edition of the semantic evaluation event previously known as SensEval. We define the task, describe the training test data and their creation, list the participating systems and discuss their results. There were 14 teams who submitted 15 systems.