Questions tagged [semantic-similarity]
The semantic-similarity tag has no usage guidance.
28
questions
2
votes
1answer
25 views
BERT Optimization for Production
I'm using BERT to transform text into 768 dim vector, It's multilingual :
...
0
votes
1answer
40 views
Cosine vs Manhattan for Text Similarity [closed]
I'm storing sentences in Elasticsearch as dense_vector field and used BERT for the embedding so each vector is 768 dim. ...
1
vote
0answers
41 views
Cluster words into groups of similar meaning (synonyms)
How can words be clustered into groups of similar meaning (synonyms)?
I started with pre-trained word embeddings (e.g., Google News), which is great, but not perfect - a limitation arises because the ...
0
votes
0answers
23 views
Cosine Similarity: Works with TF-IDF Vectors OR with Probability Vectors?
Using Cosine Similarity is a common method to calculate Semantic Textual Similarity. And it is particularly useful when comparing Sentence Embeddings provided by the Universal Sentence Encoder.
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1
vote
1answer
24 views
What's the best way to generate similar words?
Hi all I'm fairly up to date with all the NLP tasks out there (nlpprogress.com, paperswithcode.com) and great tools like (nltk, flair, huggingface etc). I want to take a single word, and predict a ...
-1
votes
1answer
20 views
How to group clusters with semantic similarity?
I have a list of job titles. I found the semantic similarity between them by using word2vec in spacy.
Now I want job titles ...
0
votes
0answers
28 views
Return the most relevant columns for a given keyword
Suppose my database has column name and description for each column of each table. Need to design an interface where a user can enter a keyword and the interface will return the most relevance columns....
1
vote
0answers
15 views
Is it possible to perform Semantic Textual Similarity without using NLTK and Genism?
College restricted us to make projects in object oriented programming languages but without using any other libraries except standard ones. We can not use APIs also
1
vote
1answer
25 views
How to determine whether a semantic concept is present in a string
I need to find a way to detect if a large string contains a specific substring.
Imagine that I have a full contract page converted to string in my Python program. What I want to do is to say if a ...
1
vote
1answer
19 views
Similarity Threshold Standards
When using similarity measures (eg. Resnik Information Content, Cosine Similarity, etc.) for any type of data, are there any standard similarity thresholds that are used, or does it all depend on the ...
1
vote
2answers
29 views
Sentence to word similarity
Is there a way to know how much a sentence is related to a word/topic?
For instance the following dataframe and the topics/attributes Romantique, ...
3
votes
1answer
40 views
Evaluation metric for Information retrieval system
I am currently reading Semantic Product Search paper published by Amazon. They are using two evaluation subtasks matching and ranking. In matching, they tune the model hyperparameters to
maximize ...
0
votes
0answers
9 views
Appearance-based hashing for similarity detection for picking the 100 most distinct images out 500 images
I would like to perform appearance-based hashing for similarity detection. I have 500 photos for each of my categories but I only want to maintain the 100 of them that are most distinct. How should I ...
1
vote
1answer
168 views
If i use use BERT embeddings for if cosine(sent1,sent2) > 0.9, then is it fair to assume s1 and s2 are similar
According to BERT author Jacob Devlin: I'm not sure what these vectors are, since BERT does not generate meaningful sentence vectors. It seems that this is doing average pooling over the word tokens ...
1
vote
1answer
110 views
Should one-hot encoded categorical features needs to be scaled when used along with text feature while deriving semantic similarity?
My aim is to derive textual similarity using multiple features. Some of the features are textual for which I am using (Tfhub 2.0) Universal Sentence encoder. There are other categorical features which ...
0
votes
1answer
51 views
How to approach for predicting semantic similarity between two phrases
I need pointers on the latest research, tools, and techniques for predicting semantic similarity between two phrases.
Problem Statement: Given two propositions A ...
0
votes
1answer
591 views
Semantic similarity between two or more sentences
I need to determine how similar sentences (in meaning) are to one another.
In order to do it, I have been considering an algorithm (cosine similarity) to determine the similarity between sentences. I ...
0
votes
1answer
26 views
Semantic networks: word2vec?
I have some doubts on how to represent the relationships between words in texts.
Let’s suppose I have two sentences like these:
...
0
votes
1answer
30 views
Modelling for similarity between two descriptions
I have a dataset of companies and research projects that they were involved in. A subset of the dataset is shown below.
I am trying to find a way to model similarity between the company and the ...
1
vote
0answers
30 views
Semantic networks and conceptual graphs
I would like to use semantic networks to understand changes in texts.
For example when I add/remove some words within a text.
...
3
votes
1answer
85 views
Semantic network using word2vec
I have thousands of headlines and I would like to build a semantic network using word2vec, specifically google news files.
My sentences look like
...
3
votes
1answer
249 views
How to build recommendation model based on resume and job description?
How to build a model which will result in better recommendation of resumes based on the job description given?
I am familiar with bow or tfidf (n-grams) approach and then take a cosine similarity but ...
1
vote
3answers
197 views
NLP: Compare tags semantically with machine learning? (finding synonyms)
Let's say I have multiple tags that I need to compare semantically. For example:
...
0
votes
1answer
71 views
How to Calculate semantic similarity between video captions?
I intend to calculate the accuracy of a caption generated by comparing it to a number of reference sentences.
For example, the captions for one video are as follows:
These captions are for the same ...
1
vote
0answers
58 views
Semantic Search Help
There is a problem we are trying to solve where we want to do semantic search on our set of data,
i.e we have a domain specific data (example: sentences talking about automobiles)
Our data is just a ...
1
vote
0answers
28 views
How we compare two paragraphs of unlabelled dataset semantically (STS)?
Column representation: Unique_id | Text1 | Text2
Unique_id 0
Text1 public show for reynolds suspension of his coaching licence. portrait sir joshua reynolds portrait of omai will get a public airing ...
3
votes
3answers
994 views
where to store embeddings for similarity search?
I've asked on stackoverflow already (here), but I figured that the approach of storing embeddings in an ordinary postgres-Database might be flawed from the very beginning. I will shortly etch out the ...
0
votes
1answer
78 views
Siamese networks vs Semantic similarity (may be gensim)
I am trying to understand the Siamese networks . In this vector is calculated for an object (say an image) and a distance metric is applied (say manhatten) on two vectors produced by the neural ...
