P. Pantel

Publications97
h-index37
Citations11,043
Highly Influential Citations879
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From Frequency to Meaning: Vector Space Models of Semantics
TLDR
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
TLDR
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.
Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations
TLDR
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.
DIRT @SBT@discovery of inference rules from text
TLDR
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
TLDR
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.
Automatically Assessing Review Helpfulness
TLDR
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.
Discovery of inference rules for question-answering
TLDR
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.
Discovering word senses from text
TLDR
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 – Discovery of Inference Rules from Text
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…
Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Clustering
TLDR
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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