Learning Syntactic Categories Using Paradigmatic Representations of Word Context

By Mehmet Ali Yatbaz, Enis Sert, Deniz Yuret. 2012

49 citations recorded by the source corpus.

Subfields: Natural Language Processing, Computational Linguistics & Semantics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Oren Melamud
2. Mohit Bansal
3. Siddharth Patwardhan
4. David McClosky
5. Deniz Yuret
6. Graham Neubig
7. Ido Dagan
8. Kevin Gimpel

In Computational Linguistics & Semantics

1. Oren Melamud
2. Mohit Bansal
3. Siddharth Patwardhan
4. Deniz Yuret

Citations in the corpus

• The Role of Context Types and Dimensionality in Learning Word Embeddings (2016, methodology): “Indirectly, this also results in similar embeddings for target words that co-occur with similar contexts.”
• FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-Gram Language Model (2012, background): “Best published results in unsupervised word sense disambiguation [4], and part of speech induction [5] represent word context as a vector of substitute probabilities.”