Questions tagged [text]
Text is a type of data often used in data science projects involving natural language processing.
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Do any text tagging NLP tools use a relational database or anything other than text files to annotate texts?
I am learning about how production systems implement text tagging. MILA has this XML format, and according to ChatGPT, Brat has a text file format, though haven't found much examples of that other ...
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Why did the Double Metaphone algorithm choose to substitute and merge consonants?
Is there any literature describing the decisions behind why the mappings from input text characters to Metaphone hash consonants were made? Why did they choose to leave out vowels? Why did they merge ...
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Highly unbalnced text data giving very low matrics
I have an unbalanced multi-class banking text data with around 76 classes. Classes are badly distributed such as one class which is combination of 240 other different categories, represents 50% of ...
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How to input or estimate missing text data?
My task requires 32 different columns with 25 beeing independent text data.
Deleting nan values, or cutting columns which has less then 20% (of non NaNs) results in reducing dataset to less then 10% ...
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Clustering Similar Articles Using Mixed Data: Seeking Advice and Validation
Question:
I'm working on a project where I need to cluster a dataset of articles based on various features, including text, numeric values, and categorical data. I've implemented a clustering approach ...
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What dimensional reduction and similarity score work for sentence embeddings created using sentence transformers
I am clustering sentence embeddings for log files, and find anomalies.
So, when I create sentence embeddings for logs using sentence transformers.
It will create vector of fixed length, which somehow ...
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Best practice for fine tuning LLM
I have a dataset that i have collect for specific topic
the dataset is in these format :
raw text (similar to shake spare dataset) where it has no label or input, just text
Question and answer ...
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Trouble Loading Lines from Text File with Various Encodings
I have been facing difficulties while loading specific lines from a text file. The lines contain characters such as
ٹام بیمار ÛÛ’Û”
ٹام بیمار ÛÛ’.
I have tried using different ...
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109
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Text segmentation problem
I am new to ML and trying to solve problem of text segmentation.
I have a transcript of news show and I want to split this transcript into parts by topic. I tried to google and asked chatgpt and found ...
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How do people usually handle creating an embedding vector of longer texts (32000 characters?
I have a set of podcast episode transcriptions in Arabic. I wish to convert these to embedding vectors so I can run a similarity comparison of them. Here's the summary statistics on the episodes:
...
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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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Were any LLMs trained on Google books?
An important limiting factor on the performance of large language models, is the amount of training text available. Of course, using e.g. the Gutenberg archive of public domain books is an obvious ...
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Can this task for phrases be called lemmatization?
I want to 'lemmatize' phrases to dictionary entries. For instance, the following collocates can be standardized to the idiom in the aforementioned link ...
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Creating variations of prompts for ChatGPT
I am developing a fine tune model to emulate a tech support chatbot based on my given information. I am struggling to create a large dataset (aiming for 1000 prompt/completion pairs), does anyone have ...
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Dictionary-based text analysis- dealing with length
I am working on an analysis using a dictionary-based text-as-data approach. I have a dataset of texts (n=1200), and I am applying a dictionary of 50 words (I tokenize the text with each word being one ...
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Get most useful sentences from one text and add to other
Supposed i have 2 text. The are share the same topic. I would like to get the most useful sentences from one text and add to other. By 'useful' we can assume any similarity suitable function.
What are ...
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Identifying and removing repeated text from json files post inference
Good morning: We index and summarize medical records using a proprietary algorithm. Being medical records they repeat the same information almost on every single page, like facility name, medical ...
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how to extract common aspects from text using deep learning?
Can you suggest me some papers to read about deep learning models that find patterns/similarities between different texts?
What I have is a set of reviews with the following categories for each review:...
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Predictive value of short text fields
I am working on a classification model using one of the following three algorithms: RandomForestClassifier, a TensorFlow model and a LogisticRegression model.
The data set I am working with has a ...
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Text analysis - "interpret" bag of words
I wanted to try to create a machine learning tarot card reader. Setting up a simulated draw of tarot cards and their associated meanings is straight-forward enough. However, the listed applications I ...
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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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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 to calculate Pointwise Mutual Information (PMI) when working with multiple ngrams
Pointwise Mutual Information or PMI for short is given as
Which is the same as:
Where BigramOccurrences is number of times bigram appears as feature, 1stWordOccurrences is number of times 1st word ...
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What is the logic/algorithm behind 'did you mean' suggestion by search engines, command suggestion in command prompt like git?
For eg. https://stackoverflow.com/questions/307291/how-does-the-google-did-you-mean-algorithm-work
this is the logic behind google's did you mean algorithm - used for spell correction suggestion.
What ...
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NLP text representation techniques that preserve word order in sentence?
I see people are talking mostly about bag-of-words, td-idf and word embeddings. But these are at word levels. BoW and tf-idf fail to represent word orders, and word embeddings are not meant to ...
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How to down\up sample text?
I have data set of 5566 samples - one column is the text of the recipe description and the other is what tax class is it.
I wish to make a classifier that would classify receipts using ML only.
I have ...
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Find best features for text classification with Countvectorizer Python
I'm facing a text classification problem where the algo is human-made but impacted by keywords. Hence I can't use any ML model but I certainly can take a data-science driven approach to find the best ...
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Partially labelled open-class classification problem with heavy overlap
Let's say we have a corpus of text, including discussions about movies and about sports. Unsupervised clustering would typically cluster into the two topics of discussion. However, we are interested ...
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280
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Matching 2 keywords list using NLP
I have two lists and I want to identify which elements are common (same or similar in meaning or context) in the list. Which NLP algorithm we should use.
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How to set vocabulary size, padding length and embedding dimension in LSTM network?
Usually in a LSTM network, we have certain parameters that need to be set before the model can begin training. I am specifically talking about vocabulary size, padding length and embedding dimension. ...
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250
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Keywords extraction for business rule text classification
I would like to classify texts without using any ML model. My idea is to find a list of keywords that I would assign to each class. Then when I need to classify a new text, I can compare it with my ...
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Why not rule-based semantic role labelling?
I have recently found some interest in automatic semantic role labelling. Most introductory texts (e.g. Jurafsky and Martin, 2008) present approaches based on supervised machine learning, often using ...
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Text classification length
I have a set of text examples I need to learn as class A, and they are of varying lengths, say 10 sentences to 1 sentence long. I have to parse a document to find those strings of text that match one ...
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NLP conversation data - Pre-processing steps
I have text data, so data that has been transcribed from conversations from employees to customers. So each call has a recording that has been transcribed to text.
I am looking to do some analytics ...
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How to evaluate the similarity of two columns containing strings?
I am new to text processing and stuck on a problem to identify the similarity of columns.
To detail the problem, consider we have two columns with string values:
...
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Problem Direction - Text Data - Conversation Classification
The problem
I have a problem where I have text data that has been transcribed from a conversation. These conversations have been marked as a pass or fail in terms of compliance by a person, ie they ...
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How do I work with noisy real world text data for text classification?
I have a topic classification model built upon Bert, when I deploy my model people input strings of a random nature like :
"aaaaaa" "aaa bbb" "ab ab ab"
and so on.
My ...
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Working with three types of data: numeric (integer, floats), images, and text for prediction
So I have three types of data (in title) and am wondering how I can combine the data. The target is numeric (price).
My idea is to perform feature extraction on both the images and text, which would ...
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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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Classifying short strings of text with additional context
I have a list of short strings each identifying a city. Misspellings are very common. The example below shows some of these short strings, along with the correct city they're supposed to match.
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How to impute missing text data?
Lets say I have a dataframe consisting of two text columns. By text, I mean the values in those columns are either sentences/paragraphs. In such a case, how do I handle missing 'NaN' values?
If it ...
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How to find the probability of a word to belong to a dataset of text
I have two text datasets, one with people that have a certain medical condition and another of random patients. I want to figure out what words are more likely to show up in the dataset with that ...
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Performance measurement of an event extraction system
I have developed an event extraction system from text documents. It first clusters the data corpus and extracts answers for what, when and where questions. Final answers are determined by using a ...
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ValueError: Layer model expects 2 input(s), but it received 3 input tensors using generator
I am trying to fit a model using generator function and I get the following error:
...
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encoding of text data in NLP
I'm getting data using web scraping to create a dataset. I have a 'company' column that contains the names of the companies. I would like to encode this column but i don't know how to find the ...
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Cluster words into groups of similar meaning (synonyms)
How can words be clustered into groups of similar meaning (synonyms)?
I started with pre-trained word embeddings (e.g., Google News), which is great, but not perfect - a limitation arises because the ...
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Effective way to find similarity between utterance(short text) and question(long text)
The challenge I have is a bunch of questions(long text) that are closely matching with an user utterance (short text). I have tried cosine similarity & Tf-iDF, BM25,Jaccard similarity, etc., but ...
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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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How to identify the feature that make the model misclassifed in text classification
Hi I am working on social media financial THAI text classification, the problem with this one is the confused classes, the misclassified prediction has a pattern that consistent as a pair.
and I want ...
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