Search Results for author:
Found 7 papers, 2 papers with code
On the Replicability of Combining Word Embeddings and Retrieval Models
We replicate recent experiments attempting to demonstrate an attractive hypothesis about the use of the Fisher kernel framework and mixture models for aggregating word embeddings towards document representations and the use of these representations in document classification, clustering, and retrieval.
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian
We explore the use of unsupervised methods in Cross-Lingual Word Sense Disambiguation (CL-WSD) with the application of English to Persian.
Toward Incorporation of Relevant Documents in word2vec
As an alternative, explicit word representations propose vectors whose dimensions are easily interpretable, and recent methods show competitive performance to the dense vectors.
Character-based Neural Embeddings for Tweet Clustering
In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks.
Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity
Word embedding, specially with its recent developments, promises a quantification of the similarity between terms.
Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation
In this paper, we address the shortage of evaluation benchmarks on Persian (Farsi) language by creating and making available a new benchmark for English to Persian Cross Lingual Word Sense Disambiguation (CL-WSD).
Applying Random Indexing to Structured Data to Find Contextually Similar Words
Our approach is based on exploiting the structure inherent in an RDF graph and then applying the methods from statistical semantics, and in particular, Random Indexing, in order to discover contextually related terms.
