Questions tagged [semantic-similarity]
The semantic-similarity tag has no usage guidance.
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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869
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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. ...
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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 ...
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Distance Metric for a dataset with embeddings and numerical columns
I'm trying to build an Approximate Nearest Neighbours model that can fetch similar records in a dataset, that are contextually similar. For example, for a record of job=Software Engineer and age=25, a ...
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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 ...
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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 ...
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For Q&A NLP system, how to extract the most relevant embedding if it is a combination of top K embeddings?
From my understanding, a typical "AI" Q&A system has a (vector) database of embedded text (from a set of documents). And when a user asks a question, the user's question is embedded and ...
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Multimodal semantic search
Context: I am interested in using the potential of using embeddings to consolidate texts/documents with very different surface forms into one searchable database - in other words, to produce a sort of ...
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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 ...
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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 ...
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How to build a Resume-to-Job Description matcher based on a parsed JSON Resume dataset?
For my capstone project/internship I'm working on an "HR assistant" tool designed to help match, score and rank resumes given a job description and/or requirements. I have inquired about ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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783
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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).
...
user120022
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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:
...
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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 ...
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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 ...
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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 ...
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applicability of relative similarity computation
I've computed the cosine similarity between a & b (=x) and ...
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BERT Optimization for Production
I'm using BERT to transform text into 768 dim vector, It's multilingual :
...
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351
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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. ...
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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 ...
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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 ...
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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 ...
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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....
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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
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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 ...
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641
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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 ...
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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, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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