Papers with embedding technique
Embedding Words as Distributions with a Bayesian Skip-gram Model (C18-1)
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| Challenge: | Rather than assuming that word embeddings are fixed across the entire text collection, we generate them from word-specific prior densities for each word. |
| Approach: | They propose a method for embedding words as probability densities in a low-dimensional space from a word-specific prior density for each occurrence of a given word. |
| Outcome: | The proposed method can encode word as a distribution on a range of benchmarks and is comparable to Gaussian embeddings. |
Word Embeddings for Code-Mixed Language Processing (D18-1)
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| Challenge: | Existing bilingual word embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embeds from code-mixed texts. |
| Approach: | They propose to use bilingual word embedding techniques to train skip-grams on synthetic code-mixed text generated through linguistic models of code- mixing to perform two tasks. |
| Outcome: | The proposed embedding technique performs better on semantic and syntactic tasks than the existing embeddable techniques on sentiment analysis and POS tagging tasks. |