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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Using Sentence-Bert with other features in scikit-learn

I have a dataset where one feature is text and 4 more features. Sentence-Bert vectorizer transforms text data into tensors. I can use these sparse matrices directly with a machine learning classifier. ...
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Sentiment Analysis on Software Engineering texts

What are the possible ways to improve sentiment dictionaries to analyse SE texts? There are several SE specific sentiment dictionaries but cannot expect much accuracy when analysing open-source ...
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When to do tokenization and does my output need tokenization after stemming?

I am working on sentiment analysis project , where there are various customer reviews. So I am trying to clean those reviews. So first thing i did is removing special characters, white spaces, ...
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How to identify/recognize that a sentence about talks about future?

Brief Introduction: I have a report/paragraph in which there are sentences with reference to future plans/outlooks/expectations for a particular entity. I want to extract all such sentences for now. ...
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Keyword extraction using NLP methods

I have some 100,000 keywords approx stored in elastic search and to extract keywords from a given text, i write a query to provide all the matching keywords. ...
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What is the difference between batch_encode_plus() and encode_plus()

I am doing a project using T5 Transformer. I have read documentations related to T5 Transformer model. While using T5Tokenizer I am kind of confused with tokenizing my sentences. Can someone please ...
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Topic modelling on long documents: intra document clustering first

I have a collection (around 1000) of very noisy, similar documents, that are each very long (>10 pages - 600 paragraphs) with multiple subsections - I want to perform topic modelling across the ...
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Converting to lowercase while creating dataset for NER using spacy

I am trying to make a custom entity model for an NER application using spacy. In several NLP projects, I have converted all the data to lowercase and applied several ML techniques. For NER also should ...
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1answer
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How should I engineer features for Named Entity Identification task?

I was working on Named Entity Identification (not recognition) task. In this NLP task, given a sentence, model has to predict whether each word (aka token) is named entity or not. The dataset used ...
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Can a reformer model really handle long-range dependency?

I read this article about new attention model called Reformer. Here is the main strength of this model: The Reformer pushes the limit of longe sequence modeling by its ability to process up to half a ...
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Why do we calculate the vector of a document by averaging the vectors of all the words?

I am trying to build a search engine to query a folder of documents. Tutorials online suggest that we should obtain the vector of a document by averaging the vectors of all the words, then compare ...
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Building a tagger model for a specific domain

I am trying to build a tagger model in spaCy v3.1 with .pos_ attributes for a specific domain. The code below manages to compile, however, it is not returning the <...
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What approach should I take for my product classification ML model with user feedback for improving result accuracy?

I'm trying to implement a product categorization ML model on a dataset with the following structure: Data sample I want to my model to be able to predict the correct category that the product should ...
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How to represent source code in NLP for tasks like code retrieval?

How to represent code features in NLP? Can code be treated like a language and pour into the neural network? Are there some work, paper or material around this topic?
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Understanding how transfer learning happens in named entity recognition task

I was going through word embedding video in Andrew Ng's coursera course Sequence modeling. In this video, he gives following two examples: Sally Johnson is an orange farmer. Robert Lin is a durian ...
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How to generate sentences based on words?

I have a dataframe which has columns Role Name, Technical Skills, Soft Skills and average experience. I have to use these words ...
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How to build vocabulary file for NLP embeddings efficiently?

I am currently building various word embeddings for my NLP project, ranging from Word2Vec, ELMo, LINE etc. I am looking to train ELMo using AllenNLP, a Python package for NLP, using the tutorial here. ...
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default estimation method of gensim's word2vec skipgram?

I am now trying to use word2vec by estimating skipgram embeddings via NCE (noise contrastive estimation) rather than conventional negative sampling method, as a recent paper did (https://asistdl....
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30 views

CRFSuite/Wapiti: How to create intermediary data for running a training?

After having asked for and been suggested two pieces of software last week (for training a model to categorize chunks of a string) I'm now struggling to make use of either one of them. It seems that ...
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How can I classify if a line of text is an incomplete natural language fragment or a complete natural language unit?

I would like to return a simple “true” or “false” for a given string which determines whether it is an incomplete sentence, like “which is why they usually”, or a complete entity, such as a chapter ...
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21 views

How to determine a good architecture for multilabel classification

I am working on an university project that requests us to classify Wikipedia abstracts about people by their professions. The output shall be a JSON file that assigns each Wikipedia abstract to a set ...
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Software/Library Suggestion: Is there a usable open-source sequence tagger around?

(Not sure if this is the right community for the question - please do downvote if stats. or whatever else is more appropriate...) I'm looking for a suggestion for ...
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Clustering Strings Based on Similar Word Sequences

In my dataset I have a feature having below data : Input Feature Brain Dementia Routine(Comfortone) Morning Check Dementia Brain-Routine(Comfortone) Brain MRA Routine (Comfortone) Brain-Dementia/...
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Is it possible to fine-tuning BERT by training it on multiple datasets? (Each dataset having it's own purpose)

BERT can be fine-tuned on a dataset for a specific task. Is it possible to fine-tune it on all these datasets for different tasks and then be utilized for these tasks instead of fine-tuning a BERT ...
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Will count vectorizer and one hot encoding provide same result when applied on Pandas series?

I have a list of pincodes in pandas series format. I want to create a sparse matrix with one column for each pincode using this series. Will both Countvectorizer and one hot encoding provide me the ...
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What can I do when my test and validation scores are good, but the submission is terrible?

This is a very broad question, I understand and I'm totally fine if someone believes it's not appropriate to do it. But it's killing me not to understand this... Here's the thing, I'm doing a machine ...
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How long does it take to fine-tune XLNet?

XLNet takes a lot more time than BERT during pre-training. This results in XLNet performing better than BERT in over 20 NLP tasks. How long does XLNet take for fine-tuning (let's assume this is ...
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Classify short sentences using BERT pre trained model with a custom dataset

I have a dataset that has 2 columns, an input and the class it is associated with. I have 6 classes and I am not able to find a way to train the BERT model on my dataset. I tried huggingface but I ...
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Which ML algorithm is best works on text data and the reason behind it? Also, which metrics is used for testing performance of model?

I am working on a project - 'sentiment analysis of tweets.' There are 5 different sentiments - extremely negative, negative, neutral, positive, and extremely positive. So it is basically the NLP ...
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8 views

Where do Q vectors come from in Attention-based Sequence-to-Sequence Transformers?

I'm taking a course on Attention-based NLP but I'm not understanding the calculation and application of Attention, based on the use of Q, K, and V vectors. My understanding is that the K and V ...
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How to handle words not in the dictionary (while finding similar words)?

I am doing a project on Semantic text analysis where my data has column Technical skills (so I have to train data to find similar words) which are words and not sentences. So I wish to find similar ...
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28 views

Does BERT need supervised data only when fine-tuning?

I've read many articles and papers mentioning how unsupervised training is conducted while pre-training a BERT model. I would like to know if it is possible to fine-tune a BERT model in an ...
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NeMo Conformer-CTC Predicts Same Word Repeatedly When Fine-Tuning

I'm using the NeMo Conformer-CTC small on the LibriSpeech dataset (the clean subset, around 29K inputs, using 90% for training and 10% for testing). I use Pytorch Lightning. When I try to train, the ...
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Entity Linking for Receipts [closed]

I am building a model for reading receipts from their mobile snapshots. After the receipt is OCR'd, I plan to use a variation on LayoutLM for entity extraction. Entities are: "quantity", &...
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34 views

TF-IDF for 400,000+ unique words in corpus?

I have a corpus with over 400,000 unique words. I would like to build a TF-IDF matrix for this corpus. I have tried doing this on my laptop (16GB RAM) and Google Colab, but am unable to do so due to ...
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1answer
20 views

Binary document classification using keywords for a very small dataset

I have a set of 150 documents with their assigned binary class. I also have 1000 unlabeled documents. Each document is about the length of a journal paper. Each class has 15 associated keywords. I ...
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2answers
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What exactly are the parameters in GPT-3's 175 billion parameters?

What exactly are the parameters in GPT-3's 175 billion parameters? Are these the words in text on which model is trained?
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1answer
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how to filter out and discard irrelevant tweets in simplest way possible

I have lot of tweets and from which i need to filter out and discard irrelevant tweets. the criteria for a tweet to be irrelevant is very simple. if all that a tweet has is emojis or a single hastag ...
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88 views

Best approach for text classification of phrases with little syntactic difference

So I have the task of classifying sentences based on their level of 'change talk' shown. Change talk is a psychology term used in counseling sessions to express how much the client wants to change ...
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17 views

How to get sentiment score for a word in a given dataset

I have a sentiment analysis dataset that is labeled in three categories: positive, negative, and neutral. I also have a list of words (mostly nouns), for which I want to calculate the sentiment value, ...
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9 views

What is multilingual vs monolingual corpus?

If I have a corpus which has Hindi script and Hindi transliteration with English script. Is it multilingual or monolingual corpus?
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Phrase/Token labeling

Looking for suggestions on how to define the following NLP problem and different ways in which it can be modeled to leverage machine learning. I believe there are multiple ways to model this problem. ...
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33 views

How much data augmentation is required on an imbalanced dataset?

Imagine I have a dataset with positive and negative sentences, and I need to train a transformer (Like BERT) to do the binary classification. The problem is that there are 100 negative sentences and ...
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8 views

How to add new words into word embedding model?

Inspired by the this post, I am curious about how to add new words into trained existing word embedding without retraining the entire embedding? My guess is of the following: there is no such thing as ...
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Comments Moderation/Profanity Filtering

Just a brief background. I am working on a project for a live-streaming app and we want to improve our live comments moderation using machine learning. The problem is that it over filter/block words ...
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2answers
29 views

How to stay up to date in NLP and use the best approaches?

There are many fast advancements in NLP field, BERT, RoBERTa, ALBERT, and XLNe, and no one can check the news or papers daily. Is there any way or site that keeps track of all these new developments ...
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Continuous Bag of Words loss function and training objective

CBOW from what I understand, obtains a probability distribution $P(w|c)$ for all words $w$ in the vocabulary, given context $c$. Th loss function is: $-logP(w|c)$, which means this would be maximised ...
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139 views

How to perform text classification on a dataset with many imbalanced classes

I am completely new to NLP and I have been tasked with performing text classification on a dataset containing 193k records. The number of classes is 107. The class with the highest number of records ...
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How can word2vec or BERT be used for previously unseen words

Is there any way to modify word2vec or BERT to extend finding out embeddings for words that were not in the training data? My data is extremely domain-specific and I don't really expect pre-trained ...
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1answer
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For text classification, would a BoW or Word Embeddings based model ever be better than a Language Model?

I've done a bit of research, with this being the best as far as objectively measuring quality, but wanted to ask from a theoretical perspective if BoW-based models (e.g. using TF-IDF) or word ...

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