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Second Language Learning From News
WebsitesWord Sense Disambiguation using Word Embeddings
Demo
Workflow1. Identify words on the page for the learner to learn2. Select an contextually appropriate translation for the
words3. Replace those words with the translations on the
article4. User can click on the word to learn more about it
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Motivation• Conducted a pilot study from May-Aug 2015• Biggest issue found was the poor quality of translations
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Workflow1. Identify words on the page for the learner to learn2. Select an contextually appropriate translation for the
words3. Replace those words with the translations on the
article4. User can click on the word to learn more about it
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Word Sense Disambiguation
WordNews: Identifying the correct translation of an English word given the contextWSD: Identifying the correct sense of an English word given the context
More specifically, our task is Cross-Lingual
WSD
Word Sense DisambiguationNavigli (2009) : Computational identification of meaning for words in context• Evaluation using Senseval/Semeval tasks• Open problem• Variations:
• Lexical Sample vs All words• Fine-grained vs coarse-grained
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Existing Approaches• Supervised vs unsupervised• Knowledge-rich vs Knowledge-poor
• Knowledge can be in the form of WordNet, dictionaries
• IMS is a supervised knowledge-poor system
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Features used in IMS• Local Collocations• POS tags• Surrounding Words
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Word Embeddings• Representation of a word as a vector in a low-
dimension space. • Vectors similarity correlate with semantic similarity.• For example, in Word2Vec,
• vector('king') - vector('man') + vector('woman') is close to vector('queen')
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Word Embeddings for WSDTurian et al. (2010) presented a method of using word
embeddings as an unsupervised feature in supervised NLP systems.
• Taghipour and Ng (2015) used Collobert and Weston’s embeddings as a feature type in IMS
Turian, Joseph, Lev Ratinov, and Yoshua Bengio. "Word representations: a simple and general method for semi-supervised learning."
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Progress Made• Use Word Embeddings in IMS• Evaluate using Senseval-2 and Senseval-3 Lexical
Sample task• Integrate IMS with WordNews
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Implementation of feature typeTried to replicate Taghipour and Ng’s (2015) work, but unable to completely replicate results. Used a different approach.
Taghipour and Ng’s (2015) approach:Concatenate surrounding vectors to form d * (w-1) dimensions
My approach:Sum up vectors of surrounding words to form d dimensions
Each dimension is used as a feature15
Implementation of feature typeTaking zinc syrup, tablets or lozenges can lessen the severity and duration of the common cold, experts believe.
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Implementation of feature type• Turian et al. (2010) suggested we should scale the
standard deviation down to a target standard deviation. • This prevents it from getting a much higher influence than
the binary features.
• Implemented a variant of this done by Taghipour and Ng (2015)
• Target standard deviation for each dimension
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Features used in IMS• Local Collocations• POS tags• Surrounding Words• Word Embedding
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Evaluation: Comparison of word embeddings
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Method Senseval-2 Senseval-3
Collobert and Weston, sigma = 0.1
0.672 0.739
Collobert and Weston, sigma = 0.05
0.664 0.735
Word2Vec, sigma=0.1 0.663 0.733
Word2Vec, sigma=0.05 0.676 0.744
GloVe, sigma =0.1 0.678 0.741
GloVe, sigma=0.05 0.674 0.738
Evaluation: Word Embeddings
This validates our use of word embeddings for this task, as both top and worst systems using word embeddings give good results
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Method Senseval-2 Senseval-3
IMS+ Word2Vec, sigma=0.1 0.663 0.733
IMS + GloVe, sigma=0.1 0.678 0.741
IMS 0.653 0.726
Rank 1 System 0.642 0.729
MFS (Most Frequent Sense) 0.476 0.552
Integration of IMS with WordNews
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Future work• Adapt word embeddings for WSD • Evaluate our system on a gold-standard human
annotated dataset• Perform a Longitudinal study
• Extrinsic evaluation of WSD with real users on our system• Usability of our system
• Improving selection of words
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SummaryWSD using word embeddingsUsed word embeddings as a feature type in IMS: sum up
the word vectors of the surrounding wordsEvaluated on Senseval-2 and Senseval-3’s lexical sample
taskFuture work
End
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