Soft cosine similarity 1 between query and a document

[makesk]

I am calculating the similarity between a query: query2 = 'Audit and control, Board structure, Remuneration, Shareholder rights, Transparency and Performance' and a document(in my case it is a company's annual report).

I am using glove vectors and calculating the soft cosine between vectors, however somehow I get the similarity score of 1 with two documents. How is that possible? For sure I know that the document does not contain only these query words. The document is a .txt file with cleaned text. And if the document matches exactly these words, then similarity can be 1 but I know it does not match exactly these words.

25 1.000 2019_q4_en_eur_con_00.txt 14 1.000 2017_q3_en_eur_con_00.txt 16 0.994 2018_ar_en_eur_con_00.txt 21 0.989 2019_ar_en_eur_con_00.txt 28 0.986 2020_q2_en_eur_con_00.txt 1 0.963 2014_ar_en_eur_con_00.txt

[4OH4]

Hi there, hmmm tricky one. Might need a bit more information to help you though - knowing the contents of that high-scoring document would be useful, for example.

To start with, I would have a look at the contents of the document after pre-processing. You'll see two lines in the example notebook that are important:

corpus = [preprocess(document) for document in documents]
dictionary = Dictionary(corpus+[query])

Check the contents of the document after it has been cleaned up, tokenized and filtered for stop words, and make sure it still looks how you expect it to.

[makesk]

Sorry for the long comment. But i think it is better to give you thorough overview everything that i am operating with.

Contents of the document after it has been cleaned up: [['lhv', 'group', 'tartu', 'mnt', 'tallinn', 'info', 'lhv', 'ee', 'lhv', 'ee', 'interim', 'report', 'january', 'december', 'summary', 'results', 'comparison', 'net', 'profit', 'eur', 'eur', 'eur', 'eur', 'attributable', 'owners', 'parent', 'earnings', 'per', 'share', 'eur', 'eur', 'net', 'income', 'eur', 'eur', 'operating', 'expenses', 'eur', 'eur', 'loan', 'provisions', 'eur', 'eur', 'income', 'tax', 'expenses', 'eur', 'eur', 'return', 'equity', 'capital', 'adequacy', 'comparison', 'net', 'profit', 'eur', 'eur', 'eur', 'eur', 'attributable', 'owners', 'parent', 'earnings', 'per', 'share', 'eur', 'eur', 'net', 'income', 'eur', 'eur', 'operating', 'expenses', 'eur', 'eur', 'loan', 'provisions', 'eur', 'eur', 'income', 'tax', 'expenses', 'eur', 'eur', 'return', 'equity', 'capital', 'adequacy', 'earnings', 'per', 'share', 'return', 'equity', 'ratios', 'based', 'profit', 'attributed', 'shareholders', 'equity', 'lhv', 'group', 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'mnt', 'tallinn', 'info', 'lhv', 'ee', 'lhv', 'ee', 'signatures', 'management', 'board', 'condensed', 'consolidated', 'interim', 'report', 'management', 'board', 'prepared', 'summary', 'results', 'january', 'december', 'period', 'condensed', 'consolidated', 'interim', 'financial', 'statements', 'lhv', 'group', 'month', 'period', 'ended', 'december', 'management', 'board', 'confirms', 'according', 'best', 'knowledge', 'interim', 'report', 'presents', 'fair', 'view', 'lhv', 'group', 'ass', 'assets', 'liabilities', 'financial', 'position', 'profit', 'loss', 'issuer', 'entities', 'involved', 'consolidation', 'whole', 'contains', 'description', 'main', 'risks', 'doubts', 'condensed', 'consolidated', 'interim', 'financial', 'statements', 'prepared', 'according', 'principles', 'international', 'accounting', 'standard', 'ias', 'interim', 'financial', 'reporting', 'madis', 'toomsalu']]

Query after it has been cleaned&tokenized: ['audit', 'control', 'board', 'structure', 'remuneration', 'shareholder', 'rights', 'transparency', 'performance']

So everything looks fine.

Results: 0 0.873 2019_q4_en_eur_con_00.pdf.txt : accounting, commission, audited, disclosure, regulatory, reviewed, committee, report, board, assessment, department, preliminary, disclosures, disclosed, compliance, supervisory, supervision, management, guidelines, commissions, advisory, corrections, remuneration, boards

It is strange that when i put only 1 document to the model, i get the similarity 0.873.

When i use 31 documents(annual reports), i get different result..

[makesk]

I assume that when similarity score is 1, the query terms are present 100% in the document?

[4OH4]

Hi there - sorry I've been busy on work projects and unable to look into this. The cosine similarity is looking at the angle between the vectors, so when each document (including the search query) is converted into vector form (using doc2vec) and represented as a point in high dimensional space - the cosine similarity is the cosine of the angle between lines from the origin to each of those points. A similarity score of 1 means the lines basically overlap, and whilst that means the content is very similar I don't think it definitely means the query terms are full contained within a particular document. I wonder if the difference in size of the documents (the target document above being much larger than the query - which isn't unusual of course) if causing issues here.

[makesk]

Just to give you some context, i have been discussing this with the author of the SoftCosine similiary in gensim. You can see this here: https://groups.google.com/g/gensim/c/8wCZp4Ievk4