Dataset Preparation

[alishiba14]

Hi, I have a dataset of 16000 docs and I have some queries. For each query there can be more than one relevant document. Can you tell me how can I prepare my data and also the evaluation?

[AdeDZY]

Hi,

Are your labels binary (relevant / non-relevant)?

If so, use a baseline ranker, e.g. BM25, to retrieve top 100 documents for a query. Then a training instance is (query, a relevant doc, a non-relevant doc in top100)

You may want to random sample the non-relevant documents in top100 instead of using all of them.

On Mon, May 13, 2019 at 2:15 AM Alishiba Dsouza notifications@github.com wrote:

Hi, I have a dataset of 16000 docs and I have some queries for each query there can be more than one relevant document. Can you tell me how can I prepare my data and also the evaluation?

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[alishiba14]

Let's say for a query 'Apple' the relevant docs are 100,120,400 and rest all are non-relevant then 'Apple' \t 100,200,400\t remaining docs \t score. Is it the correct representation of the training instance?

[giangnguyen2412]

@alishiba14 I think its true, but how can we represent remaining docs? And score, I think we can get it from BM25, right?

[AdeDZY]

Let's say a query is 'Apple', and its relevant documents: Very relevant doc (score=2): 'iPhone X - apple.com', Somehow relevant doc (score=1): 'apple inc - wikipedia',

and 10 other non-relevant doc retrieved from BM25: Non-rel doc1 (score=0): 'apple juice is healthy'. Non-rel doc2 (score=0): 'apple is red' ... The training instance is: Apple \t iPhone X apple com \t apple juice is healthy \t 2 Apple \t iPhone X apple com \t apple is red \t 2 Apple \t apple inc - wikipedia \t apple juice is healthy \t 1 Apple \t apple inc - wikipedia \t apple is red \t 1

Then we map the words to word ids.

On Mon, May 20, 2019 at 9:41 PM Giang Nguyen notifications@github.com wrote:

@alishiba14 https://github.com/alishiba14 I think its true, but how can we represent remaining docs? And score, I think we can get it from BM25, right?

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[giangnguyen2412]

Could you please tell me where can I get the training data. Should I pull from an available dataset or I need to pass over a traditional IR (BM25), them take the result of BM25 as my training data? Thank you.

[AdeDZY]

Which dataset are you using? Is it your own dataset? I guess you need to pass over a traditional IR and take the results.

On Mon, May 20, 2019 at 10:17 PM Giang Nguyen notifications@github.com wrote:

Could you please tell me where can I get the training data. Should I pull from an available dataset or I need to pass over a traditional IR (BM25), them take the result of BM25 as my training data? Thank you.

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[alishiba14]

I guess it is clear now. I will try and do it and get back to you. Thanks a lot for your reply. :)

[AdeDZY]

: )

On Tue, May 21, 2019 at 2:58 AM Alishiba Dsouza notifications@github.com wrote:

I guess it is clear now. I will try and do it and get back to you. Thanks a lot for your reply. :)

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[giangnguyen2412]

Which dataset are you using? Is it your own dataset? I guess you need to pass over a traditional IR and take the results. … On Mon, May 20, 2019 at 10:17 PM Giang Nguyen @.***> wrote: Could you please tell me where can I get the training data. Should I pull from an available dataset or I need to pass over a traditional IR (BM25), them take the result of BM25 as my training data? Thank you. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#15?email_source=notifications&email_token=ABHQHGCZUQ6KMSTSIIJF7WDPWNLSTA5CNFSM4HMN3GK2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODV2RU6I#issuecomment-494213753>, or mute the thread https://github.com/notifications/unsubscribe-auth/ABHQHGH2GOFUFENUJGTEH6DPWNLSTANCNFSM4HMN3GKQ . -- Zhuyun Dai Language Technologies Institute School of Computer Science 5000 Forbes Avenue Pittsburgh, PA 15213

Its also what I thought, thanks for reply.