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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Multiple text inputs to neural network
How do I best input multiple text fields in a neural network for classification? For example, each data point has a title and an abstract:
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How can I use Artificial Intelligence to compare to paragraphs to see if they share the same meaning?
I am looking for a way to compare two paragraphs and see if both share the same meaning.
For example, these two paragraphs have the same meaning, but they are worded differently.
Paragraph 1:
The ...
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NLP for classifying a YES or NO response to a question
I'm currently working on a project that requires some feature extraction. The data I have is text and comes from an interview. The interviewer asks a question, the client responds, and the interviewer ...
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Noob crafting a simple "Zero-Shot Classifier" Using an API . How can I avoid passing the categories every single request? [closed]
I have a collection of 700 categories, all potential classifications for articles. My current need is to create a system that can dynamically categorize short texts or articles according to these 700 ...
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Difference between using transformer for multi-class classification and clustering using last hidden layer
My data is a collection of URLs where I am interested in categorizing them into multiple groups.
At the moment, I am using a pre-trained transformer model and fine-tuning it according to my data with ...
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My text classifier behaves like regex
I'm trying to train binary classifier that classifies ask to ask programming questions, programming questions that say "I'm getting an error about x/I have problem about x" but don't say the ...
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69
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How does a Machine Learning model predict this classification problem?
Let’s imagine we want to create a simple Sentiment Analysis model using Machine Learning not Deep Learning algorithms, so we need to have a set of handcrafted features for this classification problem.
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Do different ngrams share embedding in Fasttext?
As per Section 3.2 in the original paper on Fasttext, the authors state:
In order to bound the memory requirements of our model, we use a
hashing function that maps n-grams to integers in 1 to K
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Increasing "output_sequence_length" in TextVectorization layer worsens model's performance
When exploring the Twitter Sentiment Analysis dataset on Kaggle, I came up with a model that looks like this:
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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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Giving Specified Data a Larger Value/Weight in a Model
I'm in the process of creating a model to classify an occupational code based on a job title & description. I have a large sample of labelled data to achieve this.
The government has a resource ...
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Pre-Trained Model for Occupational Coding
I've recently embarked on a task to classify an occupation code, given a job title & description. I have come across clustering, a method of grouping data into clusters of which were not ...
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Multilabel text classification with highly imbalanced training data
I'm trying to train a multilabel text classification model using BERT. Each piece of text can belong to 0 or more of a total of 485 classes. My model consists of a dropout layer and a linear layer ...
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How do I use ResNet for text processing?
I need to implement a deep neural network [residual neural network (ResNet)] that takes some text as an input [length M x N] and then processes it. Now as far as my understanding goes, ResNet is used ...
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How do I generate video classification labels using video descriptions/titles?
I've been scanning the internet for ways to generate baseball-based labels for youtube baseball videos using text collected from a YT video's description, title, and top 50 titles, but so far, I have ...
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How to perform domain adaptation if there are only unlabelled data in both source and target domains
Recently I am reading literature regarding domain adaption. However, most of the works consider scenarios when there are some labelled data in the source domain. So I wonder if there is any ...
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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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How to predict the rating of a text review and improve it?
Why is it better to treat the rating prediction of a text review as a regression problem rather than a classification one? Is it because the ratings (1,2,3,4,5) are ordinal variables?
What kind of ...
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Using a pre-trained model to generate labels to data to then train a model on
I'm trying to set up a pipeline for my ML models to automatically re-train themselves whenever concept drift occurs to recalibrate to the new output distributions. However, I can't get ground-truth ...
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Distinguishing text with opposite meanings in SVM (False Information Detection)
I am currently working on a Binary Text Classification Model (False Information Detection) using Support Vector Machine and used TF-IDF as text vectorizer in Python. I have already tried training the ...
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Which pre-processing steps are necessary for Deep Learning models to solve a document classification problem?
I have created a data set with 30.000 text documents (each text file is rather small with respect to its length), which are labelled with 0 and 1. Using this data set, I want to train machine learning ...
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Which AI algorithm to use for identifying API for a specific use from a list of APIs?
We have a legacy code solution in C#. We have to change the code so that it fetches internal data via APIs and not via DB calls.
E.g. if the current code GETS Payment object from DB, we have to ...
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Get the name of a merchant from records
I have a bunch of bank transaction records from which I want to extract merchants' names. In a few subsets of these records, the structure of the string is the same within the subset with only the ...
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Training and Evaluating BERT and XLNET [closed]
I am thinking about a project and have a few questions before I accept it. Would be grateful I anyone experienced of you could give me some advice.
In the project, I have been given a data set with (...
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How to calculate cosine similarity for classification when you have say 10000 samples belonging to two classes have a bunch of samples
Does anyone have experience with using Cosine Similarity for text classification? I see a number of articles on how to find cosine similarity between documents using Doc2Vec, Gensim, etc.
I have a ...
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Is there an AI that can extract proper nouns from free text?
I have some free text (think: blog articles, interview transcripts, chat comments), and would like to explore the text data by analysing the proper nouns it contains.
I know of many ways to simply ...
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Advantages of CNN vs. LSTM for sequence data like text or log-files
When do you tend to use CNN rather than LSTM (or the other way round) in classification or generation tasks of sequential data like text or log-data? What are the reasons for the decision and what ...
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Which algorithm can be used for extracting text patterns in tabular data?
I am working with tabular data that is similar to the below:
Name
Phone Number
ISO3 Country
Amount
Email
...
...
Outcome
Possible Reason
Leona Sunfurry
(555)-555-5555
United States
58.96
leo_sun@...
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What approach to use for selecting one of the category according to short category text?
I need some tool to classify articles based on short category text which consists of two or three words separated by '-'. The RSS/XML tag content is for example:
Foreign - News
Football - Foreign
I ...
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Multi class text classification when having only one sample for classes
I have a dataset of texts, each text was identified with an ID number. I would like to do a prediction by finding the best match ID number for upcoming new texts. To use multi text classification, I ...
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How to go about classifying 1000 classes?
I am trying to find research paper with theory(preferably implementation) that is about classifying 1000 (or more) classes. I have heard of an implementation, that initially clustering needs to be ...
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Is using a LSTM, CNN or any other neural network model on top of a Transformer(using hidden states) overkill?
I have recently come across transformers, I am new to Deep Learning. I have seen a paper using CNN and BiLSTM on top of a transformer, the paper uses a transformer(XLM-R) for sentiment analysis in ...
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Bechmark models for Text Classification / Sentiment Classification
I am currently working on a novel application in NLP where I try to classify empathic and non-empathic texts. I would like to compare the performance of my model to some benchmark models. As I am ...
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NLP Bible verse division problem: Whats the best model/method?
I'm working on a project compiling various versions of the Bible into a dataset. For the most part versions separate verses discreetly. In some versions, however, verses are combined. Instead of verse ...
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Can I use one-hot vectors for text classification?
For an upcoming project I'm trying to write a text classifier for the IMDb sentiment analysis dataset. This needs to vectorize words using an embedding layer and then reduce the dimensions of the ...
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How do RNN's for sentiment classification deal with different sentence lengths?
I have been doing a course which teaches you about Deep Neural Networks, during one of the exercises I was made to make an RNN for sentiment classification which I did, but I did not understand how an ...
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Is it possible that every class has a higher recall than precision for multi-class classification?
I am a student learning machine learning recently, and one thing is keep confusing me, I tried multiple sources and failed to find the related answer.
As following table shows (this is from some paper)...
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Text classification of non-equal length texts, should I pad left or right?
Text classification of equal length texts works without padding, but in reality, practically, texts never have the same length.
For example, spam filtering on blog article:
...
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NLP Identifying important key words in a corpus
I am intrigued with the idea of Zettelkasten but unsatisfied with the current implementations. It seems to me that a machine learning and NLP approach could be productive by helpfully identifying “...
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My LSTM text classification model seems not learn anything in early epochs
I am trying to use LSTM to do text classification and monitor the training process with tensorboard. But it seems that this model doesn't learn anything in early epochs. Is it normal for LSTM networks?...
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Applications of polar decomposition in Machine Learning
Assume there exists a new and very efficient algorithm for calculating the polar decomposition of a matrix $A=UP$, where $U$ is a unitary matrix and $P$ is a positive-semidefinite Hermitian matrix. ...
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How many spectrogram frames per input character does text-to-speech (TTS) system Tacotron-2 generate?
I've been reading on Tacotron-2, a text-to-speech system, that generates speech just-like humans (indistinguishable from humans) using the GitHub https://github.com/Rayhane-mamah/Tacotron-2.
I'm very ...
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Creating Text Features using word2vec
My task is to classify some texts. I have used word2vec to represent text words and I pass them to an LSTM as input. Taking into account that texts do not contain the same number of words, is it a ...
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Top Frequent occurrence word effect in Model Efficiency?
Assume that I have a Dataframe with the text column.
Problem: Classification / Prediction
...
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Can artificial intelligence classify textual records?
I am a records manager and I am being asked if I recommend Office 365. I'm having a hard time making a recommendation because I am missing an essential piece of information: can Office 365 replace ...
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Are bayesian neural networks suited for text (or document) classification?
I've tried to do my research on Bayesian neural networks online, but I find most of them are used for image classification. This is probably due to the nature of Bayesian neural networks, which may be ...
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Is it possible to derive meaning from text by providing multiple ways of saying the same thing to a neural network?
Let's say I feed a neural network with multiple string sentences that mean roughly the same thing but are formulated differently. Will the neural network be able to derive patterns of meaning in the ...
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Language Learning feedback with AI
Is there a program under development that uses AI technology, like Siri, to "hold hands" so to speak with a language learner and coach them on accent, colloqiual expressions, or to let them guide the ...
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When is it time to switch to deep neural networks from simple networks in text classification problems?
I did an out of domain detection task (as a binary classification problem) and tried LR and Naive Bayes and BERT but the deep neural network didn't perform better than LR and NB. For the LR I just ...
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Text detection on English and Chinese language
https://arxiv.org/abs/1910.07954
In this paper, we have a convolutional character neural network where we have object detection by taking a character as a basic unit. First, we do character detection ...
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