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Questions tagged [semantic-similarity]

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Use text embeddings to map job descriptions to ESCO occupations

I'm trying to build a model to map job descriptions to ESCO occupations which is a taxonomy for job titles. Every ESCO occupations have a title, a description and some essential skills. Ideally I ...
GanaelD's user avatar
  • 21
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0 answers
19 views

Finding accuracy of model that uses different labels than ground truth

I have an nlp model that has ground truth labels and predicted labels (that belong to different group of classes). For example, the ground truth labels are [art, computer science, history] and ...
Vidushi Maheshwari's user avatar
0 votes
1 answer
77 views

How to combine two vector embeddings into one?

I want to use OpenCLIP for generating embeddings for each slide in an array of pptx presentations. To improve the quality of the results, I want to vectorize both slide text content and preview images....
Olek Gornostal's user avatar
0 votes
0 answers
6 views

Semantic grouping and replacement of words to improve topic modelling with LDA

I have a data set of customers with complaints. As a result, I want to perform topic modelling to find topics that customers talk about in their complaints. I use LDA for this. In the results of LDA, ...
methus's user avatar
  • 111
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0 answers
21 views

How can I measure the precision and Recall?

I did semantic search using query and the total relevant documents should be 12 documents but my model retrieve 5 relevant documents only so the irrelevant are 7 documents. how can i calculate the ...
Begnnier's user avatar
0 votes
1 answer
88 views

How does RAG query affect the similarity search?

I have a RAG pipeline where I want to extract a piece of information called "X" In a regular RAG pipeline, there is a query entered by the user. Then, ...
ahmedmoh123's user avatar
0 votes
1 answer
42 views

Visualizing Author Topic Similarities: t-SNE and Cluster Labeling

I am working on a dataframe containing abstracts from various NLP conferences, along with information on information on the respective authors (names) and the keywords they've associated with their ...
Marrluxia's user avatar
0 votes
1 answer
91 views

Semantic Search on numeric data

I have a dataset in csv format. Most columns have numbers in them. The data is mostly number based, with little text. A sample of my dataset: I want to build a semantic search engine that can answer ...
Apoorva's user avatar
  • 297
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0 answers
21 views

Semantic search with pretrained BERT models giving irrelevant results with high similarity

I'm trying to create a Semantic search system and have experimented with multiple pretrained models from the SentenceTransformers library: LaBSE, MS-MARCO etc. The system is working well in returning ...
Aftaab Zia's user avatar
0 votes
0 answers
65 views

Leverage LLMs to classify sentence similarity

This is intended to be mainly a reference request in the vast world of NLP and LLMs. Context A certain protocol is given in the form of text. This can be, for instance, the general description of a ...
Andrea Gagna's user avatar
0 votes
0 answers
42 views

How can I avoid the irrelevant number of sentences in the result?

The nature of the data I have is not arranged; however, I'm trying to extract the appropriate sentences for each query as a sample for ground truth. Also, the most critical problem is that I use the ...
Begnnier's user avatar
3 votes
1 answer
136 views

Why do semantically different words produce similar embeddings?

I am comparing words in HuggingFace web UI using e5-small-v2, one of the best vector embedding models: Assuming that the scores are in the range from 0 to 1, how ...
AlwaysLearning's user avatar
0 votes
0 answers
33 views

How to increase model's validation accuracy?

I'm trying to build a model for text similarity problem using CNN, Bi-directional GRU and Bi-directional LSTM. I've tried changing several parameters but I'm getting the maximum validation accuracy as ...
siddheshk599's user avatar
1 vote
0 answers
105 views

How to do search/cluster over a million points?

I've a practical question in the areas of clustering/semantic search and would like to get some thoughts. Refer the figure for more details on this hypothetical situation. Imagine I've 2 query ...
Namburi Srinath's user avatar
0 votes
0 answers
35 views

Why shouldn't the attention matrices $W^Q$, $W^K$, $W^V$ be the same?

My question is why the equally shaped attention head matrices $W^Q$, $W^K$, $W^V$ should not be the same $W = W^Q =W^K= W^V$. In my understanding of transformer-based language models one attention ...
Hans-Peter Stricker's user avatar
0 votes
1 answer
67 views

What do averaged word vectors represent?

Assume you have high-dimensional word embeddings (d > 100) for a large number of words (|V| > 100,000) calculated over a huge non-specialized natural language corpus. Assume you have taken the ...
Hans-Peter Stricker's user avatar
1 vote
2 answers
292 views

How to find a vector representation for each descriptor?

Cubes data is well known data for extreme classification. Each picture has a set of descriptor along with it. Total data set has 312 descriptor. You will find list of descriptior in this file. My ...
A. H.'s user avatar
  • 61
0 votes
1 answer
462 views

Deduplication using NLP

I have a product catalog. The user can add a new product to the catalog. The user can enter some attributes (such as color, weight, etc.) in the text boxes. The user can also mention the description ...
Shrinidhi M's user avatar
3 votes
2 answers
2k views

Semantic search - combine text and image embedding

I have a question regarding combining text and image embeddings for semantic search. The use case is product search on a (B2B) marketplace, we have image(s) and title&description of the products. ...
Steven's user avatar
  • 31
1 vote
0 answers
30 views

Semantic Text similarity NLP

I'm new to AIML. Right now I'm working with a requirement where I need to see if two sentences are similar semantically. I'm searching for an API which compares given sentance with another array of ...
gubera's user avatar
  • 11
0 votes
1 answer
127 views

Which model to use that can distinguish between names with the same words?

For my task, I need a model that can distinguish between job titles that contain the same words. BERT model "msmarco-MiniLM-L-12-v3" shows high cosine similarity for positions: "Data ...
manabou11's user avatar
  • 101
0 votes
0 answers
107 views

On which texts should TfidfVectorizer be fitted when using TF-IDF cosine for text similarity?

I wonder on which texts should TfidfVectorizer be fitted when using TF-IDF cosine for text similarity. Should TfidfVectorizer be fitted on the texts that are analyzed for text similarity, or some ...
Franck Dernoncourt's user avatar
3 votes
1 answer
42 views

Is there a reference dataset for contextual similarity?

I'm doing some experiments with word embeddings to try to capture context-aware similarity, so that for example the word pair apple - hardware, are very dissimilar in the context of a fruit store, but ...
Jorgemar's user avatar
  • 241
2 votes
1 answer
2k views

How do we evaluate the outputs of text generation models?

Evaluation of a wide variety of natural language generation (NLG) tasks is difficult. For instance, for a question answering model, it is hard for a human to quantify how well the model has answered a ...
Greggs's user avatar
  • 121
3 votes
1 answer
3k views

Threshold determination / prediction for cosine similarity scores

Given a query sentence, we search and find similar sentences in our corpus using transformer-based models for semantic textual similarity. For one query sentence, we might get 200 similar sentences ...
DarknessPlusPlus's user avatar
0 votes
1 answer
562 views

Text cleaning when applying Sentence Similarity / Semantic Search

Do we need to apply text cleaning practices for the task of sentence similarity? Most models are being used with whole sentences that even have punctuation. Here are two example sentences that we wish ...
DarknessPlusPlus's user avatar
0 votes
0 answers
43 views

Mapping of an unseen Field/word to an existing description (in the input data), given Field and their respective descriptions as input/training data

I am working on a NLP problem. Problem Statement Given the input of fields & Labels and the respective descriptions, the goal is to the map a new unseen field to one of the most appropriate ...
Polymath's user avatar
  • 319
0 votes
0 answers
55 views

Parameters for training a sentence-similarity model using Bert?

I have a list of sentences : sentences = ["Missing Plate", "Plate not found"] I am trying to find the most similar sentences in the list by ...
Chintan Mehta's user avatar
1 vote
0 answers
42 views

Which model is better able to understand the difference that two sentences are talking about different things?

I'm currently working on the task of measuring semantic proximity between sentences. I use fasttext train _unsiupervised (skipgram) for this. I extract the sentence embeddings and then measure the ...
Ir8_mind's user avatar
  • 183
1 vote
1 answer
59 views

Document Similarity with User Preference

To measure the similarity between two documents, one can use, e.g. TF-IDF/Cosine Similarity. Supposing that after calculating the similarity scores of Doc A against ...
JoyfulPanda's user avatar
3 votes
1 answer
943 views

How to choose similarity measurement between sentences and paragraphs

Problems 1. How to find appropriate measurement method There are several ways to measure sentence similarities, but I have no idea how to find appropriate method among them for my data (sentences). ...
user avatar
0 votes
1 answer
35 views

Document Content

I have a set of .pdf/.docx documents with content. I need to search for the most suitable document according to a particular sentence. For instance: ...
user3043636's user avatar
1 vote
1 answer
36 views

Is there a way to train Doc2Vec on a corpus of docs and be able to take a novel doc and see how similar it is to the trained corpus?

I have a project idea, where I train a bunch of documents on Doc2Vec and then take a novel, input doc, and ideally be able to be told how similar it is to the docs supplied for training as a whole or ...
sangstar's user avatar
  • 133
1 vote
2 answers
707 views

Train a spaCy model for semantic similarity

I'm attempting to train a spaCy model for the purposes of computing semantic similarity but I'm not getting the results I would anticipate. I have created two text files that contain many sentences ...
motormal's user avatar
1 vote
0 answers
41 views

Comparing the similarity structure of 2 distance matrices (computed from sentence embedding)

I apologize if this question lacks clarity, my mathematical background on the topic is limited and was hoping to find some guidance. I would like to compare 2 distance matrices that contain pair-wise ...
keun's user avatar
  • 11
0 votes
1 answer
22 views

applicability of relative similarity computation

I've computed the cosine similarity between a & b (=x) and ...
Van Peer's user avatar
  • 285
1 vote
1 answer
109 views

BERT Optimization for Production

I'm using BERT to transform text into 768 dim vector, It's multilingual : ...
Mohy Mohamed's user avatar
0 votes
1 answer
407 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. ...
Mohy Mohamed's user avatar
2 votes
1 answer
1k 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 ...
Ben's user avatar
  • 141
2 votes
1 answer
762 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 ...
Julian H's user avatar
  • 123
-1 votes
1 answer
121 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 ...
Nagh's user avatar
  • 129
0 votes
0 answers
34 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....
etang's user avatar
  • 101
1 vote
0 answers
21 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
user110377's user avatar
1 vote
1 answer
95 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 ...
Tiago Bachiega de Almeida's user avatar
1 vote
1 answer
930 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 ...
3michelin's user avatar
1 vote
2 answers
67 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, ...
Revolucion for Monica's user avatar
3 votes
1 answer
435 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 ...
Sayali Sonawane's user avatar
1 vote
0 answers
14 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 ...
Mona Jalal's user avatar
1 vote
1 answer
951 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 ...
user2478236's user avatar
1 vote
1 answer
1k 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 ...
Bruso's user avatar
  • 111