1. Second Language Learning From News Websites Word Sense Disambiguation using Word Embeddings

2. Demo

3. Workflow 1.Identify words on the page for the learner to learn 2.Select an contextually appropriate translation for the words 3.Replace those words with the translations on the article 4.User can click on the word to learn more about it 3

4. Motivation Conducted a pilot study from May-Aug 2015 Biggest issue found was the poor quality of translations 4

5. Workflow 1.Identify words on the page for the learner to learn 2.Select an contextually appropriate translation for the words 3.Replace those words with the translations on the article 4.User can click on the word to learn more about it 5

6. Word Sense Disambiguation WordNews: Identifying the correct translation of an English word given the context WSD: Identifying the correct sense of an English word given the context

7. More specifically, our task is Cross-Lingual WSD

8. Word Sense Disambiguation Navigli (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 8

9. 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 9

10. Features used in IMS Local Collocations POS tags Surrounding Words 10

11. 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') 11

12. Taken from http://deeplearning4j.org/word2vec.html http://deeplearning4j.org/ 12

13. Word Embeddings for WSD Turian 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." 13

14. Progress Made Use Word Embeddings in IMS Evaluate using Senseval-2 and Senseval-3 Lexical Sample task Integrate IMS with WordNews 14

15. Implementation of feature type Tried 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 feature 15

16. Implementation of feature type Taking zinc syrup, tablets or lozenges can lessen the severity and duration of the common cold, experts believe. 16

17. 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 17

18. Features used in IMS Local Collocations POS tags Surrounding Words Word Embedding 18

19. Evaluation: Comparison of word embeddings 19 MethodSenseval-2Senseval-3 Collobert and Weston, sigma = 0.1 0.6720.739 Collobert and Weston, sigma = 0.05 0.6640.735 Word2Vec, sigma=0.10.6630.733 Word2Vec, sigma=0.050.6760.744 GloVe, sigma =0.10.6780.741 GloVe, sigma=0.050.6740.738

20. 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 20 MethodSenseval-2Senseval-3 IMS+ Word2Vec, sigma=0.10.6630.733 IMS + GloVe, sigma=0.10.6780.741 IMS0.6530.726 Rank 1 System0.6420.729 MFS (Most Frequent Sense)0.4760.552

21. Integration of IMS with WordNews 21

22. 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 22

23. Summary WSD using word embeddings Used word embeddings as a feature type in IMS: sum up the word vectors of the surrounding words Evaluated on Senseval-2 and Senseval-3’s lexical sample task Future work End 23