From Frequency to Meaning: Vector Space Models of Semantics
- Peter D. Turney, P. Pantel
- Computer ScienceJournal of Artificial Intelligence Research
- 4 March 2010
The goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs, and to provide pointers into the literature for those who are less familiar with the field.
Representing Text for Joint Embedding of Text and Knowledge Bases
- Kristina Toutanova, Danqi Chen, P. Pantel, Hoifung Poon, Pallavi Choudhury, Michael Gamon
- Computer ScienceConference on Empirical Methods in Natural…
- 2015
A model is proposed that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations, and significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.
Automatically Assessing Review Helpfulness
- Soo-Min Kim, P. Pantel, Timothy Chklovski, M. Pennacchiotti
- Computer ScienceConference on Empirical Methods in Natural…
- 22 July 2006
This paper considers the task of automatically assessing review helpfulness, and finds that the most useful features include the length of the review, its unigrams, and its product rating.
Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations
- P. Pantel, M. Pennacchiotti
- Computer ScienceAnnual Meeting of the Association for…
- 17 July 2006
Experimental results show that the exploitation of generic patterns substantially increases system recall with small effect on overall precision, and a principled measure of pattern and instance reliability enabling the filtering algorithm.
Discovering word senses from text
- P. Pantel, Dekang Lin
- Computer ScienceKnowledge Discovery and Data Mining
- 23 July 2002
A clustering algorithm called CBC (Clustering By Committee) that automatically discovers word senses from text that initially discovers a set of tight clusters called committees that are well scattered in the similarity space.
DIRT @SBT@discovery of inference rules from text
- Dekang Lin, P. Pantel
- Computer ScienceKnowledge Discovery and Data Mining
- 26 August 2001
This paper proposes an unsupervised method for discovering inference rules from text, based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar.
VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations
- Timothy Chklovski, P. Pantel
- Computer ScienceConference on Empirical Methods in Natural…
- 1 July 2004
A semi-automatic method for extracting fine-grained semantic relations between verbs using lexicosyntactic patterns over the Web, which detects similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs.
Discovery of inference rules for question-answering
- Dekang Lin, P. Pantel
- Computer ScienceNatural Language Engineering
- 1 December 2001
This paper presents an unsupervised algorithm for discovering inference rules from text based on an extended version of Harris’ Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar.
DIRT – Discovery of Inference Rules from Text
- Dekang Lin, P. Pantel
- Computer Science
- 2001
This paper proposes an unsupervised method for discovering inference rules from text, based on an extended version of Harris’ Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar.
Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Clustering
- Deepak Ravichandran, P. Pantel, E. Hovy
- Computer Science, MathematicsAnnual Meeting of the Association for…
- 25 June 2005
The power of randomized algorithm is explored to address the challenge of working with very large amounts of data and the running time from quadratic to practically linear in the number of elements to be computed.
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