2009 RandomWalksforTextSemanticSimil

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Subject Headings: Random Walk Natural Language Processing (RW-NLP) Algorithm; Random Walk Text Semantic Similarity Algorithm; Semantic Similarity; Random Walk Algorithm.

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Quotes

Abstract

Many tasks in NLP stand to benefit from robust measures of semantic similarity for units above the level of individual words. Rich semantic resources such as WordNet provide local semantic information at the lexical level. However, effectively combining this information to compute scores for phrases or sentences is an open problem. Our algorithm aggregates local relatedness information via a random walk over a graph constructed from an underlying lexical resource. The stationary distribution of the graph walk forms a “semantic signature” that can be compared to another such distribution to get a relatedness score for texts. On a paraphrase recognition task, the algorithm achieves an 18.5% relative reduction in error rate over a vector-space baseline. We also show that the graph walk similarity between texts has complementary value as a feature for recognizing textual entailment, improving on a competitive baseline system.

References

BibTeX

@inproceedings{2009_RandomWalksforTextSemanticSimil,
  author    = {Daniel Ramage and
               Anna N. Rafferty and
               Christopher D. Manning},
  title     = {Random Walks for Text Semantic Similarity},
  booktitle = {Proceedings of the 2009 Workshop on Graph-based Methods for Natural
               Language Processing, August 7, 2009, Singapore},
  pages     = {23--31},
  publisher = {The Association for Computer Linguistics},
  year      = {2009},
  url       = {https://aclanthology.org/W09-3204/},
}


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 RandomWalksforTextSemanticSimilChristopher D. Manning
Daniel Ramage
Anna N. Rafferty
Random Walks for Text Semantic Similarity2009