Corpus-based Learning of Analogies and Semantic Relations

@article{Turney2005CorpusbasedLO,
  title={Corpus-based Learning of Analogies and Semantic Relations},
  author={Peter D. Turney and M. Littman},
  journal={Machine Learning},
  year={2005},
  volume={60},
  pages={251-278}
}
We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. A verbal analogy has the form A:B::C:D, meaning “A is to B as C is to D”; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly… Expand
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References

SHOWING 1-10 OF 68 REFERENCES
A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.
How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. AExpand
Experiments on Linguistically-Based Term Associations
  • G. Ruge
  • Computer Science
  • Inf. Process. Manag.
  • 1992
Abstract A description of the hyperterm system REALIST (REtrieval Aids by LInguistics and STatistics) and in more detail a description of its semantic component is given. We call a hyperterm system aExpand
The Descent of Hierarchy, and Selection in Relational Semantics
TLDR
This paper explores the possibility of using an existing lexical hierarchy for the purpose of placing words from a noun compound into categories, and then using this category membership to determine the relation that holds between the nouns. Expand
A Probabilistic Account of Logical Metonymy
TLDR
This article acquires the meanings of metonymic verbs and adjectives from a large corpus and proposes a probabilistic model that provides a ranking on the set of possible interpretations and identifies the interpretations automatically by exploiting the consistent correspondences between surface syntactic cues and meaning. Expand
Semi-Automatic Recognition of Noun Modifier Relationships
TLDR
This work presents a semi-automatic system that identifies semantic relationships in noun phrases without using precoded noun or adjective semantics, and in experiments on English technical texts the system correctly identified 60--70% of relationships automatically. Expand
Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy
TLDR
It is found that a very simple approach using a machine learning algorithm and a domain-specific lexical hierarchy successfully generalizes from training instances, performing better on previously unseen words than a baseline consisting of training on the words themselves. Expand
Metaphor as an Emergent Property of Machine-Readable Dictionaries
TLDR
It is argued that this approach to metaphor interpretation obviates the need for the traditional "metaphor-handling component" in natural language understanding systems, and will allow these systems to overcome the britdeness of hand-coded approaches. Expand
Word Association Norms, Mutual Information and Lexicography
TLDR
The proposed measure, the association ratio, estimates word association norms directly from computer readable corpora, making it possible to estimate norms for tens of thousands of words. Expand
Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems
TLDR
Three merging rules for combining probability distributions are examined: the well known mixture rule, the logarithmic rule, and a novel product rule that were applied with state-of-the-art results to two problems commonly used to assess human mastery of lexical semantics|synonym questions and analogy questions. Expand
Learning surface text patterns for a Question Answering System
TLDR
This paper has developed a method for learning an optimal set of surface text patterns automatically from a tagged corpus, and calculates the precision of each pattern, and the average precision for each question type. Expand
...
1
2
3
4
5
...