In this proposal track paper, we have presented a crowdsourcing-based word embedding evaluation technique that will be more reliable and linguistically justified. The method is designed for intrinsic evaluation and extends the approach proposed in (Schnabel et al., 2015). Our improved evaluation technique captures word relatedness based on the word context
Labelling, or annotation, is the process by which we assign labels to an item with regards to a task...
We identified features that drive differential accuracy in word sense disambiguation (WSD) by buildi...
Even though considerable attention has been given to the polarity of words (positive and negative) a...
We present a comprehensive study of eval-uation methods for unsupervised embed-ding techniques that ...
International audienceWord embeddings intervene in a wide range of natural language processing tasks...
© 2017 Dr. Richard James FothergillWords can take on many meanings, and collecting and identifying e...
International audienceWord Embeddings have proven to be effective for many Natural Language Processi...
Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approa...
There have been a multitude of word embedding techniques developed that allow a computer to process ...
Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to t...
Word embeddings typically represent differ- ent meanings of a word in a single conflated vector. Emp...
In this paper, we present our work on developing a vocabulary trainer that uses exercises generated ...
State of the art natural language processing tools are built on context-dependent word embeddings, b...
Word embedding models have been an important contribution to natural language processing; following ...
Word embeddings are already well studied in the general domain, usually trained on large text corpor...
Labelling, or annotation, is the process by which we assign labels to an item with regards to a task...
We identified features that drive differential accuracy in word sense disambiguation (WSD) by buildi...
Even though considerable attention has been given to the polarity of words (positive and negative) a...
We present a comprehensive study of eval-uation methods for unsupervised embed-ding techniques that ...
International audienceWord embeddings intervene in a wide range of natural language processing tasks...
© 2017 Dr. Richard James FothergillWords can take on many meanings, and collecting and identifying e...
International audienceWord Embeddings have proven to be effective for many Natural Language Processi...
Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approa...
There have been a multitude of word embedding techniques developed that allow a computer to process ...
Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to t...
Word embeddings typically represent differ- ent meanings of a word in a single conflated vector. Emp...
In this paper, we present our work on developing a vocabulary trainer that uses exercises generated ...
State of the art natural language processing tools are built on context-dependent word embeddings, b...
Word embedding models have been an important contribution to natural language processing; following ...
Word embeddings are already well studied in the general domain, usually trained on large text corpor...
Labelling, or annotation, is the process by which we assign labels to an item with regards to a task...
We identified features that drive differential accuracy in word sense disambiguation (WSD) by buildi...
Even though considerable attention has been given to the polarity of words (positive and negative) a...