Questions tagged [text-classification]

For questions about text classification, the task of assigning predefined categories (or classes) to free-text documents.

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Text Classification with unlimited labels, Text Extraction?

I'm looking to use ML to read in a blob of text, and extract a name from that text blob. (The blob is from an OCR result from an iPhone) The text blob varies in size, but the name is always present in ...
Matthew Knippen's user avatar
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Does Fine Tuning with Custom Label Build Upon the Capability of Zero Shot Classification or Does It Train from Scratch?

The task is to classify email text bodies into exclusive categories like feedback, complaint etc. I have a labelled dataset available having about 350 samples. I have tried the ...
Della's user avatar
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Multilabel Classification - Flat Binary Classifiers vs Hierarchical Binary Classifiers

Was researching on multi label classification to solve the problem of tagging news articles with topics and countries, where tags follow the syntax <topic>-<country>, and would like to ...
curious-24-7's user avatar
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Is this how you would go about this NLP Project?

What do you think of these steps? And where can I find help with this project? I am in a business class and was assigned a data science problem. I was advised to seek out a coder at my school who can ...
Layla M's user avatar
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Will hypermeters tuned on sampled dataset work for the whole dataset?

I'm doing multi-label classification on text data using BERT model. Since the dataset is huge, around 50 thousand rows, I was thinking to use stratify sampling on dataset to reduce it to around 2-4 ...
Shaurya Uniyal's user avatar
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Approach for Multi-class Classification of texts

I'm trying to do a project where I have paragraphs and I need to classify them into multiple labels. The dataset is around 40k rows with labels. I understand there is no one right approach but should ...
Shaurya Uniyal's user avatar
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Is it methodologically correct to use the data to be used for finetuning in the pretrain phase of the BERT model?

Let us assume the training of a BERT model. An initial pre-train is performed with a large data set A. Subsequently a finetuning is performed with a dataset B which is part of A, but now with labels ...
Álvaro Loza's user avatar
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What specific problems in what domains and fields have the need to use rule-based text classification?

I wrote a rule-based keyword detection and classification program specialized in my language (Vietnamese) and would like to know where this app is useful. Here how the program work: First you input ...
Ooker's user avatar
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2 answers
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Improve text classification accuracy

Task: I am building a text classification for salary prediction for data science jobs. I want to achieve at least 70 percent accuracy. Data: Features: Consists of job descriptions of data science, ...
Sendhan's user avatar
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Zero-shot out-of-distribution text classification

I'm building out a pipeline that would allow me to filter out text based on whether or not the text belongs to any of the classes I've defined. I feel like one (albeit naive) approach would simply be ...
mehsheenman's user avatar
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predict if news article belong to specific category or not?

I am still new to machine learning. I am trying to build an ML model to predict if an article belongs to a category or not. for example, I have three categories : [war, politics, and crime]. I choose ...
user158789's user avatar
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Recommendation system NLP ideas

The problem: If we have a clustering problem with lets say x groups. And each group has a document describing it, lets say 3 pages. Then we have n observations each with a smaller piece of text ...
Dylan Dijk's user avatar
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How should I design the CNN for classifying the relation between 2 texts (multiple classes)

So I have a task to classify the relation between 2 texts (4 classes possible) and one of the requirements is to preprocess them with TfidfVectorizer or CountVectorizer. Since every sample has 2 ...
giza2001s's user avatar
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PyTorch input shape for text classification using LSTM

I have three sentiment classes: POSITIVE, NEGATIVE, and NEUTRAL, along with a dataset consisting of 3000 sentences and their corresponding sentiment labels (POSITIVE, NEGATIVE, or NEUTRAL). Each ...
PatelisGM's user avatar
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Explanation : Simpler models beat BERT base

I have been trying to train different models for a multi-class classification task of texts. My data set consists of rows of text and its label. The texts are short sentences. I tried the following ...
eya_bklt's user avatar
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Fine-tune zero-shot classification model multi-label

I started a small project where I am trying to fine-tune a zero-shot classification model on a proprietary dataset. I was thinking to use the NLI approach, building contradiction and entailment ...
lucatrovato's user avatar
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Performing Multi label text classification

I have a text and it's class, so I have performed single text classification, but now I want to train a multi label classifier, so I tried combining sentence to form a multi label dataset, but the ...
Gaurav Joshi's user avatar
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Scikit-Learn classifiers have impressively bad accuracy on test set for binary text classification problem

I'm trying to fit a GaussianNB and a LinearSVC to binary labeled text using scikit-learn. To do that, I'm using a TfidfVectorizer to transform my sentences into a matrix of features. This is ...
Towdo's user avatar
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NLP approach for classifying webscraped data

I have a challenge in a project of mine where I will be provided with a list of scraped datas from a website. Along with the data i will also be provided some parameters like the tag of scraped ...
LEO_007's user avatar
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Segregation of a finance interview based on topic discussed

I have a video interview of 5 people, which i have transcribed to text (example corpus given below). Considering the fact that we know SPEAKER_00 is the interviewer and rest are guests. I want to know ...
Devansh Gupta's user avatar
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Text Classification Taking too long

I have a sample of 135k documents that are preprocessed, and to which I calculated TFIDF. I tried clustering with KMeans, which gave me a memory problem (20GB). Then, i tried with MiniBatch K-Means ...
ayowhatthedogdoin's user avatar
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Classification errors on 'bert-base-uncased' text classifier

Disclaimer : This is a long question, please be patient. Thanks in advance I am using bert-base-uncased for text-classification. I have 11 classes, and the classification is happening alright for most ...
Vinay Varahabhotla's user avatar
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Advice needed on text analytics (keyword/topic extraction, sentiment analysis) with multiple language support

I am looking for a text analytics solution which supports the following at the minimum. Keyword extraction from provided text Multiple language support (At the minimum, English and Chinese) Sentiment ...
yetanotherse's user avatar
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Check if given information is genuine

I want to build an algorithm which can verify a piece of content against ground truth and output whether the piece of content is providing genuine information or is fake. I'm working on a stock ...
nish's user avatar
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Why doesn't loss decrease with each epoch (for IMDB data vs Rotten Tomatoes data)

I am following the Google tutorial on ML for text classification I made this Google Colab notebook which you should be able to run from start to finish to see the issue. When the code trains a ...
Nate Anderson's user avatar
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1 answer
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What is a proven text classification method beyond tfidf+svm?

I'm doing text classification and Tfidf+LinearSVM does pretty well. Do you know a reference which demonstrates another technique that is shown to do better? I'm specifically looking for evidence that ...
Gere's user avatar
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How to implement Ant Lion Optimization (ALO) feature selection for KNN Classification Problems?

I have been assigned for a project related to text data classification, i have preprocess and vectorized the data with TF-IDF. For feature selection i am using pyMetahueristic library to implement ALO ...
Physics69's user avatar
1 vote
1 answer
147 views

What are the preprocessing steps for text classification after removing stopwords?

I am working on an NLP project where I have text that I need to categorize based on topics (The data is 2 columns, text and topic). Something that I am stuck on now is the preprocessing part. What are ...
soup's user avatar
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1 answer
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Why my sentiment analysis model is overfitting?

The task is to predict sentiment from 1 to 10 based on Russian reviews. The training data size is 20000 records, of which 1000 were preserved as a validation set. The preprocessing steps included ...
Renat Abdrakhmanov's user avatar
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Argument classification based on a given claim

I'm working on an nlp school project in which I have to build a model that takes a text and a claim and gives as an output whether the text is supporting or opposing the claim . . the data that I have ...
eya_bklt's user avatar
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NLP and multi-level text classification

I define text commands for controlling home smart devices. Each commands (there are about 6 different commands ) are labeled. I need also and additional label: "out-of-scope". It could be ...
sigidagi's user avatar
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1 answer
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Below text-classification model gives accuracy of 0.77 only on one dataset and 0.99 on spam-ham dataset? What should I do to increase with my dataset?

...
rutvi's user avatar
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88 views

Better results in Document similarity using Word2Vec

I try to cluster similar support-tickets in a technical domain. The support tickets are very domain-specific and are written in various styles, lengths, using abbreviation, etc. I made a training-...
Roland's user avatar
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1 answer
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Aspect-Based Sentiment Analysis with Bert and Pytorch

I have a dataset of online reviews (X) with their corresponding topics (topic1 to topic5) and each topic can have 5 values (fined-grained sentiment score from 1 to 5). So, I have one X and 5 Y columns....
m sh's user avatar
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1 answer
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Is my model overfitted?

I am using a naive bayes classifier to classify 20 newsgroup dataset. My accuracy on the training set is 97 and on the testing set is 89. Is my model overfitted? If it is what steps can I take to ...
Colin Antony's user avatar
1 vote
1 answer
246 views

Does high number of output labels affect the performance of BERT and how to handle the class imbalance issue while doing multi text classification?

I am using BERT to do multiclass text classification. The number of output classes I have to predict from is: 116 and there is high degree of class imbalance that I see. We have the following kind of ...
Swagat Mishra's user avatar
1 vote
1 answer
62 views

Is it valid changing the classification treshold of neural networks for improving the classification performance?

I'm dealing with text classification using BERT pre-trained model with a multiclass imbalanced dataset. When we use a 0.5 default classification threshold we obtain a f1 measure of around 0.7. But we ...
Zaratruta's user avatar
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using a feature that is only available during training

I'm working on a project that aims to classify JIRA issues into their relevant owner group. An issue has the following text features: Summary Description Comments all of which are text based. During ...
Ben's user avatar
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245 views

How do I improve the accuracy of a BERT-based multilabel text classification model?

I have a database table with 79,512 rows, each of which describes a category. Each row has a title and a description, and can even have a supercategory. Often, supercategories have categories. I'm ...
moonman239's user avatar
1 vote
1 answer
556 views

Topic classification on text data with no/few labels

I would like to achieve a classification of a text input into predefined categories. From what I have understand unsupervised approach are unfeasible if my target label is something very rare in ...
Andrea's user avatar
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847 views

How to deal with DataCollator and DataLoaders in Huggingface?

I have issues combining a DataLoader and DataCollator. The following code with DataCollatorWithPadding results in a ...
3r1c's user avatar
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1 answer
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Classify E-commerce URLs into predefined classes

How can I classify an E-commerce URL Page into the following categories, Cart Payment Product Page Checkout How can I achieve this with the url and page title in my hand? I have tried multiple ways ...
Loukik's user avatar
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1 vote
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Boosting the effect of some of the features in SVM

I'm doing text classification with SVM. I'm using Tfidf vectorization. In addition to the text vectors, I have a context data denoting the possible outcomes of the prediction. For example, I have a ...
cuneyttyler's user avatar
3 votes
4 answers
2k views

How to automatically classify a sentence or text based on its context?

I have a database of sentences which are about different topics. I want to automatically classify each sentence with the one or more relevant tags based on the context of the sentence as shown below: ...
SageMaker's user avatar
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Tensorflow text classification with 'int' and 'tf-idf' vectorizer

My question is simple. I tried a text classification with both 'int' and 'tf-idf' vectorizers. I'd expect it would classify better with tf-idf but the scores are 0.65 for 'int' and 0.60 for 'tf-idf'. ...
cuneyttyler's user avatar
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2 answers
97 views

NLP vs Keyword-Search. which one is the best?

I have constructed a natural language processing (NLP) model with the aim of identifying technology keywords within text. The model is trained on a large dataset that contains over 400,000 phrases and ...
Lakshitha Samod's user avatar
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1 answer
462 views

LSTM model is producing really bad results for multiclass text classification for imbalanced small dataset

I am training a LSTM model on my current dataset to predict the multiclass categories - there are 18 mutually exclusive categories and the dataset has ...
Django0602's user avatar
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1 answer
26 views

Sequence labels with prior knowledge about valid labels

I am working on a text classification problem where on inference, there is a known set of valid classifications which is smaller than the set of all possible labels. Example: There are 5 labels: L1, ...
Imran Q's user avatar
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1 vote
1 answer
134 views

What is the right processing order when working with a dataset that already consists of test and train data?

I want to work on the following task: Text Classification using Deep Learning models and a Transfer Learning model. The notebook that I'm creating should include the following steps: Data ...
Elodin's user avatar
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1 vote
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NLP - F1 score for positive class drops to 0 after data augmentation

I'm working on a 3-class text classification problem where my initial class distribution looked like this: positive: 50% negative: 25% and neutral: 25% And training on a model on this slightly ...
AnonymousMe's user avatar

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