P. Pantel

Publications
Influence

From Frequency to Meaning: Vector Space Models of Semantics

Peter D. Turney, P. Pantel
Computer Science
J. Artif. Intell. Res.
4 March 2010

We present a survey of VSMs and their relation with the distributional hypothesis as an approach to representing some aspects of natural language semantics. Expand

2,483
165
PDF

Representing Text for Joint Embedding of Text and Knowledge Bases

Kristina Toutanova, Danqi Chen, P. Pantel, Hoifung Poon, Pallavi Choudhury, M. Gamon
Computer Science
EMNLP
2015

We propose a convolutional neural network model that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations. Expand

DIRT @SBT@discovery of inference rules from text

Dekang Lin, P. Pantel
Computer Science
KDD '01
26 August 2001

In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y ≈ X wrote Y", "X solved Y ≋ X found a solution to Y", and "X caused Y≈ Y is triggered by X". Expand

Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations

P. Pantel, M. Pennacchiotti
Computer Science
ACL
17 July 2006

We proposed a weakly-supervised, generalpurpose, and accurate algorithm, called Espresso, for harvesting binary semantic relations from raw text. Expand

Automatically Assessing Review Helpfulness

S. Kim, P. Pantel, Timothy Chklovski, M. Pennacchiotti
Computer Science
EMNLP
22 July 2006

In this paper, we investigate the task of automatically predicting review helpfulness using a machine learning approach. Expand

VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations

Timothy Chklovski, P. Pantel
Computer Science
EMNLP
1 July 2004

We present a semi-automatic method for extracting fine-grained semantic relations between verbs using lexicosyntactic patterns over the Web. Expand

Discovering word senses from text

P. Pantel, Dekang Lin
Computer Science
KDD
23 July 2002

We present a clustering algorithm called CBC (Clustering By Committee) that automatically discovers word senses by clustering words according to their distributional similarity. Expand

DIRT – Discovery of Inference Rules from Text

Dekang Lin, P. Pantel
2001

In this paper, we propose an unsupervised method for discovering inference rules from text, such as “X is author of Y  X wrote Y”, “X solved Y  X found a solution to Y”, and “X caused Y  Y is… Expand

Discovery of inference rules for question-answering

Dekang Lin, P. Pantel
Computer Science
Nat. Lang. Eng.
1 December 2001

In this paper, we present an unsupervised algorithm for discovering inference rules from text based on an extended version of Harris’ Distributional Hypothesis. Expand

Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Clustering

D. Ravichandran, P. Pantel, E. Hovy
Computer Science
ACL
25 June 2005

In this paper, we explore the power of randomized algorithm to address the challenge of working with very large amounts of data. Expand

Recommended Authors