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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Where does BERT fit in the Machine Learning Hierarchy?

I am a newbie in the machine learning world and I need guidance from the professionals. I am trying to make a hierarchy starting from machine learning, then to deep learning and to BERT. I have read ...
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22 views

Effect of Stop-Word Removal on Transformers for Text Classification

The domain here is essentially topic classification, so not necessarily a problem where stop-words have an impact on the analysis (as opposed to, say, sentiment analysis where structure can affect ...
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Non-uniform class occurances in input data for classification task - how to tackle it?

So, I gathered political articles for my thesis, now I want to be able to classify given text. Though the classes distribution is actually crazy. Class 1: 964 docs Class 2: 37,020 Class 3: 640 Class ...
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Model to detect specific semantic content without labeled data

I want to build a model that can detect sentences that discuss requests for communication - like 'email me', 'phone us', 'contact us', etc. However, I do not have any labeled data which I can use to ...
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User reviews but in French [closed]

thanks for passing by. So I am working on a sentiment analysis project where I need to gather french reviews about some giving product (iphone XR, PS5 or anything as long as there is enough data), I ...
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Text Extraction from PDF's [closed]

I am working on an entity extraction project for which I am processing PDF documents. Due to the unstructured nature of data, I am trying to extract sub-sections of text from the documents and then ...
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Debugging potential data mismatch NLP

I trained a binary classifier on a sample of 2000 posts with some class imbalance (38% minority class) and obtained an F1 score = 1.0 but when it was tested on a validation set of 470 posts of same ...
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improve NER model accuracy with spaCy dependency tree

I have search at lot, was not able to find a solution for my problem... I am training a NER model, that should detect two types of words: Instructions and Conditions. This is not the standard use-case ...
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1answer
23 views

Python to clean miswritten words with repetitive letters such as “wwwwooorrrrddss” to “words”

When cleaning text-data in Python3 for NLP, are there are any 'common practices' when it comes to addressing repetitive letters in words such as "wwwwooorrds" to "words", or "...
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What is the difference between BERT architecture and vanilla Transformer architecture

I'm doing some research for the summarization task and found out BERT is derived from the Transformer model. In every blog about BERT that I have read, they focus on explaining what is a bidirectional ...
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Using bert (or fitbert) for predicting masked words from word candidates

Fitbert (which is based on Bert) can be used to predict (fill in) a masked word from a list of candidates as below: ...
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1answer
22 views

Why does gpu speed up inference?

I understand that GPU can speed up training because for each batch multiple data records can be fed to the network, which can be parallelized for computation. However, for inference, typically each ...
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18 views

k-means for customer review analysis

I have a dataset of amazon Alexa reviews and want to group negative and positive reviews in separate groups. Is k-means a good approach to it? The dataset is unlabeled so how will my model know which ...
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24 views

BERT data cleaning [duplicate]

I am wondering which data cleaning steps should be performed if you want to re-fine a BERT model on custom text data. Which steps should be performed? Does it make sense to perform a stemming or ...
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In sequence models, is it possible to have training batches with different timesteps each to reduce the required padding per input sequence?

I want to train an LSTM model with variable length inputs. Specifically I want to use as little padding as possible while still using minibatches. As far as I understand each batch requires a fixed ...
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15 views

Word2Vec vs LexVec vs GloVe

I'm on a NLP project and found a resource that has the three word representations mentioned in the question name, and I am struggling to find one place where they all are explained and compared. As I ...
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1answer
31 views

Are LDA clusters identical across different runs?

for a given corpus are the Latent Dirichlet Allocation clusters for it is unique in general? How about the gensim multi-process implementation of LDA? are there ...
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14 views

How do you compute perplexity in a text generation task?

I am using an LSTM in tensorflow to do text generation. I want to compute the perplexity of my model on a test set (is that the right thing to do?). For each individual word I can make a long list ...
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1answer
16 views

Vector representation of documents for text classification

I'm looking for proper method of document embeddings. I know that doc2vec will give me the vector representations for given corpus, but how do I embed new documents? I need to train neural network ...
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32 views

For short sentences(max length 10 ), which Name entity recognition algorithm is good?

My Training data look like this . I have to recognize 4 class for each sentence. Any algorithm , which have some learning parameters Means not rule based approach . So which method is good for my ...
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1answer
25 views

Why this TensorFlow Transformer model has Linear output instead of Softmax?

I am checking this official TensorFlow tutorial on a Transformer model for Portuguese-English translation. I am quite surprised that when the Transformer is created, their final output is a Dense ...
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1answer
13 views

Twitter Data-Analyse: What can I do with the data?

I retrieve data to a specific topic from Twitter and did my sentiment analysis on it. I never did anything in NLP, etc. So what else can I do with that? "Main goal" would be to find out if ...
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2answers
90 views

Differentiate between positive and negative clusters

I have applied k-means clustering on my dataset of Amazon Alexa reviews. ...
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2answers
22 views

How to collect info about unseen bugs given user's comments/feedbacks? [closed]

I have a dataframe which looks like: ...
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1answer
10 views

Why I would use TF-IDF after Bag-of-Words (CountVectorizer)?

In my recent studies over Machine Learning NLP tasks I found this very nice tutorial teaching how to build your first text classifier: https://towardsdatascience.com/machine-learning-nlp-text-...
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1answer
14 views

Difference between Word Embedding and Text Embedding

I am working on a dataset of amazon alexa reviews and wish to cluster them in positive and negative clusters. I am using Word2Vec for vectorization so wanted to know the difference between Text ...
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14 views

LDA-like algorithm but at the character level?

I have a catalog of products and I'd like to find "topics" in their description. The problem is that you might find in the description things like GraphicsCardVendor10.2 ...
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1answer
13 views

How to find appropliate algorithm to bulid a model for natural language based two data [closed]

What I would like to do I would like to create a model to infer nationality from name and created the below data frame combining two dataset from Kaggle. Titanic: Machine Learning from Disaster (input/...
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10 views

Usage of Doc2Vec as feature extractor for text classification of websites with political articles

I have gathered political articles from polish websites for my engineering thesis. The main goal is to try to predict the website that input text belongs to. So for this few websites I want to create ...
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15 views

Training custom NER on OCR text with SpaCy won't train

I want to perform information extraction from documents. I wanted to try Spacy's NER method, so I follow following steps : 1)OCR on text document, using Tesseract. As output I have a list of words ...
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1answer
38 views

How pre-trained BERT model generates word embeddings for out of vocabulary words?

Currently, I am reading BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. I want to understand how pre-trained BERT generates word embeddings for out of vocabulary ...
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10 views

Pearson Product Moment Correlation vs Cosine Similarity For Encoded Text Comparison

I've seen a few different examples of the implementation of Google's Sentence Encoders. Many of these use different methods to find the similarity between sentences. For example, the standard ...
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19 views

Logistic Regression performs better on longer texts

I trained the LogisticRegression model with TF-IDF (both birgams and unigrams) and while predicting class it revealed that in longer texts (up to 3000 symbols)it works better that if I use short (+-...
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1answer
19 views

From where does BERT get the tokens it predicts?

When BERT is used for masked language modeling, it masks a token and then tries to predict it. What are the candidate tokens BERT can choose from? Does it just predict an integer (like a regression ...
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17 views

Math behind word2vec in neural network

I built a neural network for my NLP preblem using GloVe and an embedding layer. Each word is converted to a vector of 100 dimensions and input_length is 300. Word2Vec has 68,546 words. How does the ...
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1answer
30 views

Is applying pre-trained model on a different type of corpus called transfer learning?

I trained my classification model on corpus A and evaluated it on corpus B. I do it, because for corpus A I have a lot more labeled sentences than for B. Nature of sentences used in A is different ...
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12 views

character level n-gram embedded LSTM is overfitting strong

I am working on a character level classification LSTM and I used uni-gram (hello -> h, e, l, l, o). So my vocab size was 28 (alphabet + " " + "-&...
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18 views

NLP - Found input variables with inconsistent numbers samples

I'm trying to train a model to read the greetings from the sample dataset collected from Tripadvisor and I've been getting the following error when I am trying to ...
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1answer
12 views

How to pass input to deep learning models for Multiple choice question answering problem?

I'm currently working on a multiple-choice question answering system. The training set consists of a question, answer and 4 options and I need to predict the correct answer among 4 options. Sometimes ...
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1answer
31 views

Why does an attention layer in a transformer learn context?

I understand the transformer architecture (from "Attention is All You Need"), as well as how the attention is computed in the multi-headed attention layers. What I'm confused on is why the ...
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8 views

NER_Multiple_entities

I am working on a problem of entity extraction which requires me to extract variables of interest from a text document. My challenge is that the text contains multiple entities of a variable, for ex. ...
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1answer
22 views

Mining timelines in a long text

I am trying to detect timeline of brands histories. For my specific case, I believe it is easy because data is already clustered. For each Wikipedia article I can spot sentences surrounding dates. ...
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1answer
28 views

What is considered short and long text in NLP (document similarity)

What is considered short and long text in NLP? I'm working on a dataset that contains documents from 10 to 600 words and I'm asking myself if I should treat them differently. Also, I haven't found a ...
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1answer
18 views

How to find correlated knowledge among different documents? [closed]

Say I have a sequence of documents clicked by a user, how can I mine the identical or semanticly similar word/knowledge/phrases shared among different documents? Maybe someone can give a paper or ...
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10 views

Learning conditional statements from natural language

Natural language text in emails might have conditional statements in them. Are there any technical papers and methods that explore converting unstructured text (eg. emails) into structured conditional ...
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11 views

SKLEARN SGDClassifier prediction accuracy hint?

There is a function predict but is it possible to also hint how much is the predicted category probable? Like prediction of category 1 with 90% confidence, or 2 with 30% confidence etc. Without this I ...
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11 views

What is the structure and dimension of input passed to neural network when training CBOW and SKIP GRAM word embedding

I am confused about input passed to neural network in natural language processing (NLP) when training CBOW word embedding from scratch. I read the paper and have ...
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1answer
16 views

Is smoothing in NLP ngrams done on test data or train data?

Is smoothing in NLP ngram done on test data or train data? Since smoothing is to avoid the language model predicting 0 probability of unseen corpus (test). So I wonder is smoothing done on test data ...
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1answer
14 views

making conclusions after sentiment analysis

After performing some sentiment analysis, I have a dataset that looks like this: For different products, using online reviews, I have obtained some values for positive/negative sentiments. However, ...
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1answer
11 views

Are GPTs close to real intelligence or just another Data In -Data Out -Data Permutation and Combinations?

Use cases and Solutions surrounding GPT's have taken NLP world with storm and started the GPT-Best vs GPT- Not So Best war on the internet. There are solutions been derived from API's provided by HF. ...

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