📰 Workshop on Vector Space Modeling for Natural Language Processing 2015: all accepted papers

27 papers · the most cited first · ← Workshop on Vector Space Modeling for Natural Language Processing 2015

1. A Deep Architecture for Non-Projective Dependency Parsing ↗Erick Fonseca, Sandra Aluísio
2. A Multi-classifier Approach to support Coreference Resolution in a Vector Space Model ↗Ana Zelaia, Olatz Arregi, Basilio Sierra
3. A Simple Word Embedding Model for Lexical Substitution ↗Oren Melamud, Omer Levy, Ido Dagan
4. A Vector Space Approach for Aspect Based Sentiment Analysis ↗Abdulaziz Alghunaim, Mitra Mohtarami, Scott Cyphers et al.
5. A Word-Embedding-based Sense Index for Regular Polysemy Representation ↗Marco Del Tredici, Núria Bel
6. Bilingual Word Representations with Monolingual Quality in MindThang Luong, Hieu Pham, Christopher D. Manningour summary
7. Combining Distributed Vector Representations for Words ↗Justin Garten, Kenji Sagae, Volkan Ustun et al.
8. DeepNL: a Deep Learning NLP pipeline ↗Giuseppe Attardi
9. Dependency Link Embeddings: Continuous Representations of Syntactic Substructures ↗Mohit Bansal
10. Distributed Word Representations Improve NER for e-Commerce ↗Mahesh Joshi, Ethan Hart, Mirko Vogel et al.
11. Distributional Representations of Words for Short Text Classification ↗Chenglong Ma, Weiqun Xu, Peijia Li et al.
12. Distributional Semantic Concept Models for Entity Relation Discovery ↗Jay Urbain, Glenn Bushee, George Kowalski
13. Estimating User Location in Social Media with Stacked Denoising Auto-encoders ↗Ji Liu, Diana Inkpen
14. Learning Distributed Representations for Multilingual Text Sequences ↗Hieu Pham, Thang Luong, Christopher Manning
15. Morpho-syntactic Regularities in Continuous Word Representations: A multilingual study. ↗Garrett Nicolai, Colin Cherry, Grzegorz Kondrak
16. Named Entity Recognition for Arabic Social MediaAyah Zirikly, Mona Diabour summary
17. Neural context embeddings for automatic discovery of word senses ↗Mikael Kågebäck, Fredrik Johansson, Richard Johansson et al.
18. Neural word embeddings with multiplicative feature interactions for tensor-based compositions ↗Joo-Kyung Kim, Marie-Catherine de Marneffe, Eric Fosler-Lussier
19. Relation Extraction: Perspective from Convolutional Neural NetworksThien Huu Nguyen, Ralph Grishmanour summary
20. Semantic Information Extraction for Improved Word Embeddings ↗Jiaqiang Chen, Gerard de Melo
21. Short Text Clustering via Convolutional Neural Networks ↗Jiaming Xu, Peng Wang, Guanhua Tian et al.
22. Simple Semi-Supervised POS Tagging ↗Karl Stratos, Michael Collins
23. Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction ↗Sameer Singh, Tim Rocktäschel, Sebastian Riedel
24. Unsupervised Text Normalization Using Distributed Representations of Words and Phrases ↗Vivek Kumar Rangarajan Sridhar
25. Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words ↗Vivek Kumar Rangarajan Sridhar
26. Vector Space Models for Scientific Document Summarization ↗John Conroy, Sashka Davis
27. Word Embeddings vs Word Types for Sequence Labeling: the Curious Case of CV Parsing ↗Melanie Tosik, Carsten Lygteskov Hansen, Gerard Goossen et al.

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/venues/workshop-on-vector-space-modeling-for-natural-language-processing/2015/papers/