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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14 views

When to use GloVe vocabulary vs. building a vocabulary from the training data?

While studying some (pytorch) examples that use pretrained GloVe vectors I came across two variants: Use the vocabulary of the GloVe vectors and thus initialize the embedding layer with the ...
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12 views

What are the best ways to convert different parts of speech to noun?

I am working on a topic modelling task. I want to convert different parts of speech such as adjectives, verbs to noun. What are the best ways of doing this? I have tried lemmatization using NLTK ...
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19 views

Imbalanced NLP text classification

I'm trying to solve a multi-class text classification task with 3 classes. I have an initial pretty balanced but small dataset. When I start to mine additional data I can't always find a lot of new ...
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21 views

Softmax in Sentiment analysis

I am going to do Sentiment Analysis over some tweet texts. So, in summary we have three classes: Positive, Neutral, Negative. If I apply Softmax in the last layer, I will have the probability for each ...
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1answer
11 views

Questions of understanding - Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation

I'm currently analysing the paper Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation (Post, Vilar 2018): https://arxiv.org/abs/1804.06609 I have ...
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BERT vs GPT architectural, conceptual and implemetational differences

In the BERT paper, I learnt that BERT is encoder-only model, that is it involves only transformer encoder blocks. In the GPT paper, I learnt that GPT is decoder-only model, that is it involves only ...
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12 views

What should I visualize for humor detection model to gain some useful insight?

I was going through bunch (1,2,3) of humor detection paper. But most papers don't include any visualizations, say some graph related to model being trained. I was thinking to train some language ...
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Automatically finding business opportunities in text documents

I am new to machine learning and NLP. I am exploring the possibility of using one of these approaches to automatically examine a large collection of text documents and determine, first of all, if they ...
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3answers
17 views

Creating numeric word representation of input sentences resulting in MemoryError

I am trying to use CountVectorizer to obtain word numerical word representation of data which is essentialy list of 160000 English sentences: ...
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How to learn common sense constants? Look in body for detail

If I wanted to learn constants for example week -> 7 days, chicken -> 2 legs, day -> 24, 1km -> 1000 meters hours, and so on, would it be possible to extract this information from a BERT ...
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Picking the right NLP model to tag words from a dataset

As the title suggests, I am posting here in the hope someone could direct me towards NLP models for tagging words. To be more concrete, here is what I wish to do. I would like to build a flashcard ...
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Difference between the architectures of semantic and instance segmentation

My question is about the difference between the architectures of semantic segmentation and instance segmentation models. So, as far as I understand, a semantic segmentation model is making pixel-wise ...
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24 views

Fake News Detection Classifier approach

I have the dataset related to any domain like sports, entertainment, politics, etc. I just want to know that the approach I am using for fake news detection is valid or not. As I do not want to use ...
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10 views

Keyword Imputation Using Machine Learning and NLP

I am fairly new to machine learning and data imputation so forgive me if I am using wrong words or not feasible ideas. I have a table as follows, with text in the Headline column and a list of ...
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88 views

Train a spaCy model for semantic similarity

I'm attempting to train a spaCy model for the purposes of computing semantic similarity but I'm not getting the results I would anticipate. I have created two text files that contain many sentences ...
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18 views

Model doesn't know German well enough

I have a model that generates questions and answers based on input text. The texts are in German and based on observations it seems like the model doesn't know German well enough. I need to pretrain ...
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Any transformer model (NLP) for code classification?

Does any Transformer (NLP) that is suitable for code classification tasks exist? For example, I have a lot of source codes of various categories (driver, game, email client, etc.). I want to ...
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I'm looking for some scripts or tools that can take some text, understand and generate multichoice questions, cloze deletion, pop quiz [closed]

I'm a fairly entry level coder in python but I was hoping for some guidance on if, or how I could load a block of text to generate random questions to test comprehension. My goal is to conduct a large ...
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44 views

How to extract numerical information from text descriptions

I have an attribute that is the description of an operation (i.e description of a building consent), I need to translate this to a mathematical operation. I need to find out the new number of dwelling ...
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1answer
12 views

Is there a ubiquitous web crawler that can generate a good language-specific dataset for training a transformer?

It seems like a lot of noteworthy AI tools are being trained on datasets generated by web crawlers rather than human-edited, human-compiled corpora (Facebook Translate, GPT-3). In general, it sounds ...
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Is there an AI function that can provide the definition of a word or phase with reasonably good accuracy?

I would like to make use of a software function which can provide the definition of a word or phrase. These words and phrases are in the realm of common knowledge - objects like "DVD player",...
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7 views

Browser history segmentation

I'm trying to segment a browser history into semantically coherent sessions. For example, a user might be working on a school project for 30 minutes, then planning an upcoming vacation for 30 minutes, ...
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1answer
60 views

How does amazon's reviews that mention extracts topics from reviews?

Amazon product page contains a section called Reviews that mention. The section lists the main things that users liked or dislike about the product. For example see ...
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21 views

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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GloVe dot product optimized for non-comutative data whilst the operation itself being commutative

To my current knowledge, GloVe word vectors dot product are optimized to be the w_i ⋅ w_j = log⁡(P(ⅈ|j)) The probability being computed from a cooccurance matrix. However, dot product is a commutative ...
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33 views

Is the Transformer decoder an autoregressive model?

I have been trying to find an answer to these questions, but I only find conflicting information. Is the transformer as a whole autoregressive or not? And what about the decoder? I understand that the ...
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1answer
161 views

Which type of models generalize better, generative or discriminative models?

In NLP, which type of models (generative or discriminative) is more sensitive to the amount of data to generalize better? references? This is related to the way those two types capture the data ...
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1answer
47 views

How to use pre-trained word2vec model generated by Gensim with Convolutional neural networks (CNN)

I have generated a pre-trained word2vec model using the Gensim framework (https://radimrehurek.com/gensim/auto_examples/index.html#documentation). The dataset has 507 sentiments(sentences) which are ...
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Can I get some help on how to train a Word2Vec Model on a dictionary?

I doing a project, where I'm ingesting student resumes with Word2Vec, and then I need to find the best applicant for a project position. So I have a table with a column for the applicant ID and for ...
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12 views

Is PositionalEncoding needed for using Transformer models correctly?

I am trying to make a model that uses a Transformer to see the relationship between several data vectors but the order of the data is not relevant in this case, so ...
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2answers
133 views

How to obtain vector representation of phrases using the embedding layer and do PCA with it

I am trying to understand from both a conceptual and a Python code point of view, how to represent phrases that are present in a corpus (that is used to train a neural network to classify phrases) as ...
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0answers
10 views

How can incrementality be measured in NLP?

As the title suggests, I am searching for a method to numerically evaluate the incrementality of a language model. Is there any way this can be achieved? For example, when a language model receives an ...
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1answer
31 views

How to extract details (educational details, exp details etc.) from a resume?

I am trying to build a resume parser which can extract details such as Name, Address, Education details (degree name, college name, university name, course duration), Experience details (designation, ...
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19 views

Cloud-based visual tool to perform NLP on text corpora

I have some text corpora to share with non-programming clients (~50K documents, ~100M tokens) who would like to perform operations like regex searches, co-locations, Named-entity recognition, and word ...
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21 views

Question generation from a wordcloud using nlp [closed]

Need help in Question generation from Clickable Image, Wordcloud,Upvote - Downvote,Read and Response Type using NLP
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15 views

When performing token-classification over phrases, is it better to predict 1 by 1, or few at once?

Say you fine tune a pre-trained BERT model for token-classification. Which of the following method is best? Set a big FC layer to predict tokens for each token in the input sequence. Set the FC layer ...
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0answers
22 views

Unstructured data not template based to structured data

I am working on a project where my goal is to extract data from a large diversity of pdf that does not follow a template (i.e unstructured data not template based). My ORC part works well and now I am ...
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0answers
54 views

Ways to cluster word senses with word embeddings

I'm trying to semantically cluster polysemous words or word with different meanings in a corpus for my class study and I want to do it by word embeddings but I have no Idea how to reach to the ...
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0answers
7 views

Python implementation for GloVE: Doc2Vec

What is the most used/best python implementation of GloVe? I'm looking to do doc2vec training, not word2vec.
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0answers
22 views

Clustering new data with UMAP

I am trying to preprocess embeddings for a text classification model using 'all-mpnet-base-v2' sentence transformer. Initially, I used UMAP to reduce the dimensionality for visualization. Until I read ...
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0answers
15 views

What does the term "seed lexicon" means?

I am reading a research paper (NLP) and found the phrase "seed lexicon". Could someone please explain it in detail? Edit : A sample paper Leveraging Affective Bidirectional Transformers for ...
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10 views

Why one of the features is dominating all rest of the features in my trained SVM?

I have been given a task to train the SVM model on conll2003 dataset for Named Entity "Identification" (That is I have to tag all tokens in "Statue of Liberty" as named entities ...
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27 views

Using sequences for multilabel classification

I have a sequential dataset of events, which looks like the following: ['some text here', 'more text here'] -> target Each datapoint is a true sequence ...
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17 views

Combine multiple vector fields for approximate nearest neighbor search

I have multiple vector fields in one collection. My use-case is to find similar sentences in similar contexts. The sentences and contexts are encoded to float vectors. Therefore, I have one vector for ...
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0answers
11 views

How can I build my voice speech-to-text model?

I found an instruction to build such kind of custom model on Azure. Prepare data for Custom Speech However, I would like to either fine-tune or transfer learning on Google Colaboratory or docker. In ...
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20 views

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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1answer
21 views

Methods for finding characteristic words for a group of documents in comparison to another group of documents?

I'm working on a problem of anomaly detection, where at the end of the anomaly detection I will have a group of documents consisting of a title of each object that was flagged as anomalous. At the ...
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2answers
33 views

How to consider the effect of exclamation marks in sentiment analysis

For example in the following two tweets, we can see the first one seems to be more negative than the second one: "You are not required to come here now!!!" "You are not required to ...
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12 views

Machine translation transformer output - "unknown" tokens?

Cross post from my original post in Stackoverflow: https://stackoverflow.com/questions/69595863/machine-translation-transformer-output-unknown-tokens Based on the feedback , I have now updated my ...
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1answer
63 views

Machine learning roadmap not for beginners [closed]

To introduce myself: I know what is RL, know some RL algorithms such as PPO, A2C. Know about offline RL, online RL. I have read many papers about RL. Such as MuZero, AplhaZero, Decision Transformer ...

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