As many public kernels have shown, there are many out of pretraining vocabulary words.\nMany of these OOV words are misspellings, and one possible prescription for those words is using edit distance, like adopting the nearest pretrained word for the initial embedding of the misspelling.
\n\nHas anyone tried edit distance for misspellings? Does it improve your score?
", "messages": [ { "id": "454331", "postDate": "01/11/2019 13:05:42", "content": "As many public kernels have shown, there are many out of pretraining vocabulary words.\nMany of these OOV words are misspellings, and one possible prescription for those words is using edit distance, like adopting the nearest pretrained word for the initial embedding of the misspelling.
\n\nHas anyone tried edit distance for misspellings? Does it improve your score?
", "rawMarkdown": "As many public kernels have shown, there are many out of pretraining vocabulary words.\nMany of these OOV words are misspellings, and one possible prescription for those words is using edit distance, like adopting the nearest pretrained word for the initial embedding of the misspelling.\n\nHas anyone tried edit distance for misspellings? Does it improve your score?", "votes": null }, { "id": "455454", "postDate": "01/13/2019 23:54:44", "content": "I have not tried this specifically, although I did try something slightly similar. The most common approach for embeddings is to simply use a randomly generated vector of 300 (using some statistics). Since I am using mean of two embedding files (like most people), I wondered what would happen if instead of using the random embedding, you checked if the other file had an embedding vector for that specific word and if it did have it, use the embedding from that file instead of random embedding. It didn't work. LB was down even though score on a hold-out test set was up by .001. This is probably because the vector spaces are different, but I thought possibly the fact that they are being averaged might account for that factor
\n\nDid you end up trying misspelling fixes? Any improvement?
", "rawMarkdown": "I have not tried this specifically, although I did try something slightly similar. The most common approach for embeddings is to simply use a randomly generated vector of 300 (using some statistics). Since I am using mean of two embedding files (like most people), I wondered what would happen if instead of using the random embedding, you checked if the other file had an embedding vector for that specific word and if it did have it, use the embedding from that file instead of random embedding. It didn't work. LB was down even though score on a hold-out test set was up by .001. This is probably because the vector spaces are different, but I thought possibly the fact that they are being averaged might account for that factor\n\nDid you end up trying misspelling fixes? Any improvement?", "votes": null }, { "id": "455842", "postDate": "01/14/2019 17:18:05", "content": "For now I tried just calculating edit distances with very naive codes.\nNear words looks somewhat plausible, but because of the naive aproach it takes so long that I cannot finish it for all OOV words within 2 hours.\nI may be going to try tuning the codes for the running time in this weekend.
", "rawMarkdown": "For now I tried just calculating edit distances with very naive codes.\nNear words looks somewhat plausible, but because of the naive aproach it takes so long that I cannot finish it for all OOV words within 2 hours.\nI may be going to try tuning the codes for the running time in this weekend.", "votes": null } ], "comments": [ { "id": 455454, "author_name": "julius6", "author_url": "", "post_date": "01/13/2019 23:54:44", "content": "I have not tried this specifically, although I did try something slightly similar. The most common approach for embeddings is to simply use a randomly generated vector of 300 (using some statistics). Since I am using mean of two embedding files (like most people), I wondered what would happen if instead of using the random embedding, you checked if the other file had an embedding vector for that specific word and if it did have it, use the embedding from that file instead of random embedding. It didn't work. LB was down even though score on a hold-out test set was up by .001. This is probably because the vector spaces are different, but I thought possibly the fact that they are being averaged might account for that factor
\n\nDid you end up trying misspelling fixes? Any improvement?
", "votes": null, "replies": [ { "id": 455842, "author_name": "yufuin", "author_url": "", "post_date": "01/14/2019 17:18:05", "content": "For now I tried just calculating edit distances with very naive codes.\nNear words looks somewhat plausible, but because of the naive aproach it takes so long that I cannot finish it for all OOV words within 2 hours.\nI may be going to try tuning the codes for the running time in this weekend.
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "454331": "As many public kernels have shown, there are many out of pretraining vocabulary words.\nMany of these OOV words are misspellings, and one possible prescription for those words is using edit distance, like adopting the nearest pretrained word for the initial embedding of the misspelling.\n\nHas anyone tried edit distance for misspellings? Does it improve your score?", "455454": "I have not tried this specifically, although I did try something slightly similar. The most common approach for embeddings is to simply use a randomly generated vector of 300 (using some statistics). Since I am using mean of two embedding files (like most people), I wondered what would happen if instead of using the random embedding, you checked if the other file had an embedding vector for that specific word and if it did have it, use the embedding from that file instead of random embedding. It didn't work. LB was down even though score on a hold-out test set was up by .001. This is probably because the vector spaces are different, but I thought possibly the fact that they are being averaged might account for that factor\n\nDid you end up trying misspelling fixes? Any improvement?", "455842": "For now I tried just calculating edit distances with very naive codes.\nNear words looks somewhat plausible, but because of the naive aproach it takes so long that I cannot finish it for all OOV words within 2 hours.\nI may be going to try tuning the codes for the running time in this weekend." }, "source": "meta" }