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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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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-...
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Updating corpus and then clustering without re-running agglormetative clustering on everything in the corpus
I'm working on an app where I want to cluster many sentences (more specifically hints/insights to math or coding puzzles like on Codeforces). I want to use TF-IDF instead of a deep learning model for ...
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How to create the target column for tweets classification in Orange
I am interested in performance analysis of classification algorithms. For this, I have collected tweets on a specific topic, and saved as .csv file. The .csv file consists of only one column called ...
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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....
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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 ...
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Is there any way to aquire image and video URLs from a large amount of facebook ads for research?
I have aquired a lot of URLs to some political ads from the Meta ad library API. As far as i can tell, there is no way to automate the aquisation of the image and video URLs from these ads. Is this ...
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How can we integrate zero shot classification with a supervised classifier?
We have two sets of labels that are known and unknown to the supervised classifier (SC).
We infer for a test example using the SC and a zero shot classifier (ZC).
Let's assume, our inference datapoint ...
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Benchmark dataset with both images and tabular data
In the medical sector, there are situations in which an image dataset is associated with a tabular dataset containing different features but the same labels as the image dataset. For example, suppose ...
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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 ...
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Which is the best choice for evaluating models on small and unbalanced textual datasets?
We are dealing with a small multilabel dataset (around 15k samples) of texts that is imbalanced. Some classes have more than 4k samples and others have around 700 samples. We are using a classifier ...
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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 ...
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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 ...
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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 ...
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Multi-label classification inference
I am working on a multi-label classification with transformers. I want to assign tags to input text. First, I have trained a model multiclass and with the pipeline function I can retrieve all possible ...
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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 ...
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How to deal with ambiguous classification outputs that exceed the specified threshold but are too close together?
I have a simple classification setup (intent classification). Once an input is received it's parsed using Multinomial Logistic Regression and then a score is predicted for each class. I pick the ...
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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 ...
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Can I use Deep Learning Algorithm between every two columns of excel with text data?
If my data has this format
Now can I use LSTM in between each column? I have to classify keyword into column B first and then Column A. are there any other ways I can look into? But I had larger data ...
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Text Classification Model unable to learn
I am trying to build a text classification model. When I train the model it is unable to improve accuracy and at some point accuracy even decreases and loss increases.
I have researched for possible ...
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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 ...
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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 ...
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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:
...
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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'. ...
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Tensorflow text classification with subject for each text
I want to classify texts with additional input 'text' subject. I acquire these subjects from wikidata 'instance of' properties. I designed a neural net model as below. Network takes texts and subjects ...
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Augmented text classification with Knowledge Graphs
There is a paper 'Learning beyond datasets: Knowledge Graph Augmented Neural
Networks for Natural language Processing'. In this paper a method for classifying texts with additional knowledge graph ...
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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 ...
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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 ...
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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, ...
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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 ...
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Visualizing data distribution of categorized texts
I'm trying to develop an SVM model to classify some texts into positive and negative. For the training phase, I'm going to use a dataset containing 2400 comments from Twitter, classified into positive ...
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Text Annotation
How to annotate aspect-based sentiment analysis using docanno? I want to annotate data like the given piture
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How to find the optimal number of samples for fine-tuning a pre-trained language model for text classification?
I'm trying to fine-tune a pre-trained language model (PLM) for text classification. The dataset that I'm using for fine-tuning includes about 40k samples.
I wonder if I should use the whole dataset or ...
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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 ...
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