Questions tagged [nlp]

Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation.

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How to Re-Train TextBlob?

I am conducting a sentiment analysis of historical newspapers. For this, I have been using TextBlob's sentiment.polarity. The results are okay, but I am curious how they would differ if I re-trained ...
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how Can we add extra word embedding to the pytorch funnel transformer?

i was approaching NLP sequence classification problem (3 classes) using huggingface transformers (funnel-transformer/large) and tensorflow. first i created laserembedding like this : ...
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Image captioning using sequence to sequence model

I am trying to build a sequence to sequence model. I have used Vision transformer as the encoder and LSTM with 1 layers as the decoder. The output of the encoder is given as the hidden state for ...
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Input length of Sentence BERT

Can Sentence Bert embed an entire paragraph instead of only a sentence? For example, a description of a movie. If so, what if the word counts exceeded the input limit? I remember Bert can only take up ...
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How to train SentencePiece tokenizer for huggingface transformers?

I am trying to train MarianMT model using Huggingface Trainer API. But first, I need to train MarianTokenizer which needs three parameters to initialize: Processor for source language Processor for ...
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**tokens when tokens is a dictionary

Trying to understand the code from https://www.analyticsvidhya.com/blog/2021/05/measuring-text-similarity-using-bert/ I am looking at understanding the syntax on these two lines: ...
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why there is no preprocessing step for training BERT?

I would like to train a BERT model from scratch. I read the paper as well as a few online material. It seems there is no preprocessing involved. e.g. removing punctuation, stopwords ... I wonder why ...
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Is there any sentiment analysis algorithm to identify sentiment of a sentence towards a certain word in the sentence?

I'll start with some examples. Think about a sentence like "Mazda CX5 is a good car.". NLTK sentiment analysis module "Vader" will give a positive polarity score on the sentence. ...
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Creating class labels for custom DataSets efficiently (HuggingFace)

I have pandas dataframes - test & train,they both have text and label as columns as shown below - ...
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Using relative or absolute frequencies to estimate group differences in texts

My objective is to estimate differences between how five political parties use moral words in their tweets and speeches. To that end, I have a dictionary that I pass to each tweet text / audio ...
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Next-word Generation in Tabular Dataset

I'll build next-word generation using Tensorflow to predict address mapping. But, I saw many tutorial, next-word generation use long-text narration for its training dataset. But, I have dataset ...
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Procedure or term for analyzing transcribed text and returning bulleted output

I am attempting to analyze transcribed text from an audio file to group bullet points based on known key phrases in the text. Example: I have verbally stated the following keywords in the text, which ...
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What Preprocessing is Needed for Semantic Search Using Pre-trained Hugging Face Transformers?

I am building a project for my bachelor thesis and am wondering how to prepare my raw data. The goal is to program some kind of semantic search for job postings. My data set consists of stored web ...
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NLP model for assessing the probability of token given n previous tokens

I am looking for a model with which I can predict the probability of a current word given its n predecessors (or successors) in a sentence. Please note: I do not want to generate text nor do I want to ...
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Do I need training data in multiple languages for a multilingual transformer?

I am attempting to train a transformer which can categorize sentences into one of n categories. This model should be able to work with a number of different languages - English and Arabic in my case. ...
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Creating tables from unstructured texts about stock market

I am trying to extract information such as profits, revenues and others along with their corresponding dates and quarters from an unstructured text about stock market and convert it into a report in ...
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Suggestions for guided NLP online courses - Beginner 101

I would like to know from the data science community here for suggestions on nlp courses. I am new to NLP area and would like to take up a course which covers from basic to advanced concepts such as ...
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How does "A Neural Probabilistic Language Model" learn good word vectors?

I'm a layman making a foray into NLP and I have a question: The landmark paper A Neural Probabilistic Language Model (Bengio et al., 2003) makes an attempt at statistical language modelling by (1) ...
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Slow and Fast tokenizer gives different outputs(sentencepiece tokenizer)

When i use T5TokenizerFast(Tokenizer of T5 arcitecture), the output is expected as follows: ['▁', '</s>', '▁Hello', '▁', '<sep>', '</s>'] But ...
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Usage of Word2Vec

Sorry for the basic doubt, I would like to know if I can use my Word2Vec straight for classification without using LSTM. My assumption is it’s not possible because the ordering of the words will not ...
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Using a supervised model (BiGRU-CRF) with lack of labelled data. How to get labels with no human labeled data?

I'm working with a project supervisor on a deep learning project. The project involves extracting keywords or catchphrases from legal documents so that they can then be used for semantic search of ...
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Transformers vs RNN basic doubt

I have a basic doubt. Kindly clarify this. My doubt is, When we are using LSTM's, We pass the words sequentially and get some hidden representations. Now transformers also does the same thing except ...
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Good starting point for Natural Language Processing thesis

I want to do a masters thesis on Natural Language Processing, where I want to evaluate if given definitions meet certain criterias. Problem is, I'm new to NLP and I don't know where to start. I need a ...
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Which model is best for the generating accurate answers of the Boolean questions?

I am trying to generate the question using T5 transformer answer of the questions but I am getting the error like below. here is the code. ...
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Clustering unknown product names

I have a parser that reads messages that contain product names. I would like to automatically cluster product names in clusters where each cluster would be one product and all the ways it can be ...
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The Right Approach / Method for Address Completion

I have data that formatted like this below: ...
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Supervised recommender system design feedback

I am facing a challenge that I am not quite sure how to solve and would like to hear feedback. Basically, I have to implement a recomendation system for certain courses to be recommended to users of ...
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Can you detect source language of a translation?

Sometimes you read text and you have a strong feeling that it was translated from a certain language. For example, you read Russian text, see «взять автобус» («take bus» instead of Russian «сесть в ...
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Topic modelling or Keyword extraction for a small dataset

I am working on a project where I have a dataset which contains very less data. These are the comments of people. I have only 130 lines with 10 words per line. My goal is to identify the common topics ...
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Word stemming effect on dictionary-based Sentiment analysis

I am currently building a Farsi dictionary-based sentiment analysis model, based on thousands of Farsi tweets. Our team's approach has been as follows: ...
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How can I implement classification for this problem?

I have been thinking about the problem of "predicting" damages awarded in legal cases. For specificity, let us be given a dataset of summaries of cases of a certain flavour (say ...
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Smaller embedding size causes lower loss

When I convert my multilingual transformer model to a single lingual transformer model (got my languages embedding from the multilingual transformer and deleted other embeddings, decreased dimensions ...
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What is the difference between adding words to a tokenizer and training a tokenizer?

The title says it all. I was researching this question but couldn't find something useful. What is the difference between adding words to a tokenizer and training a tokenizer?
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Limitations of NLP BERT model for sentiment analysis

I am reading a paper, where the authors assess online public sentiment in China in response tot the government's policies during Covid-19, using a Chinese BERT model. The author's objective is not ...
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What is the effect of the tokens?

What is the effect of the tokens that the model has if model A has 1B tokens and the other model has 12B tokens? Will that have an effect on the performance?
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Cost Value of the original GloVe model

Does anyone know the parameters that were used to train the original GloVe model (say the 50 dimensional one) and the final cost function value attained? I am trying to use their model from their ...
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How to create product category based on product description

I am currently working on a project that needs product range analysis. It's an ecommerce dataset that has 7 columns: InvoiceNUm, StockNum, Description, Quantity, InvoiceDate, UnitPrice and CustomerID. ...
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LinearSVC training time with CountVectorizer and HashingVectorizer

I am currently trying to build a text classifier and I am experimenting with different settings. Specifically, I am extracting my features with a CountVectorizer ...
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1 answer
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Mixed language OCR

I'm solving a table data recognition task And the huge problem is the recognition of mixed language pictures. I'm using tesseract for OCR, but it fails to recognize both languages simultaneously. Here ...
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How to use batches for training in Sequence-to-Sequence models?

I am working on a seq-to-seq Image Captioning model, with Vision Transformer as the encoder and a LSTM based model as a decoder. The output from the encoder is given as the hidden state and cell state ...
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How to use word embeddings and numerical features together for UMAP visualization?

I'm trying to visualize a large collection of artwork with both numerical and text features (e.g., its physical dimensions and text description), and I want to incorporate all these features together ...
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Removing unique writing style from text

To start off, I'm not a data scientist or machine learning engineer. I'm mostly interested in finding existing solutions that will fit this usecase. I'm trying to build a forum that will protect ...
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Application of eigenvalues, eigenvector, transposed matrix

Can you give me please some application of eigenvectors, eigenvalues and matrix transposition in data science? I guess for eigen-values/vectors it would be linear regression PCA and NLP, alongside ...
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Named Entity Recognition using Spacy V3 with imbalance entities

Will the spacy V3 model get affected by imbalanced entities? I have got a dataset annotated in spacy format and if I look into my custom entities the rations are different for different entities. For ...
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Marginal Probability Distribution of Feature space - meaning

I'm reading some literature on Transfer Learning in NLP, and this is one of the definitions that I came across in Pan & Yang (2010) Here is another definition from Sebastian Ruder which is a ...
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Why are we training Segment Embedding in BERT?

In BERT we have segment embeddings that are used for "Segment Embeddings with shape (1, n, 768) which are vector representations to help BERT distinguish between paired input sequences." Yes,...
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Are there known meanings or topics associated with pretrained word embeddings?

I've been using some pre-trained word embeddings (Glove, FastText, word2vec etc.) in text classification, with the averaged 300 dimensions of tags and sentences as input features. Normally it's useful ...
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How does CLS token having meaning of the sentence in BERT

As my understanding CLS token is a representation of the whole text (sentence1 and sentence2), which means that model got trained in such a way that the CLS token is having the probability of "if ...
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How special tokens in BERT-Transformers work?

I was trying to understand how tokens work and all I understood is that tokens are the representation of the input in a more meaningful way (data preparation for the "encoder of transformer" ...
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emotional ontology for textual emotion detection

I am working on textual emotion detection project , I want to use ontology for that issue but i could not find any ontology that handles the semantic issues like ...
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