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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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4 views

BERT Model Evaluation Measure in terms of Syntax Correctness and Semantic Coherence

For example I have an original sentence. The word barking corresponds to the word that is missing. ...
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What is the advantage of positional encoding over one hot encoding in a transformer model?

I'm trying to read and understand the paper Attention is all you need and in it, they used positional encoding with sin for even indices and cos for odd indices. In the paper (Section 3.5), they ...
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1answer
25 views

Training with many CPU cores doesn't improve performance

I ran my job on a computing cluster: first with 1 node / 4 cores, then with 2 nodes / 32 cores. But the training time is pretty much exactly the same for both of them! 67 seconds per step. I am ...
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Absolute Discounting: How are we guaranteed that the n-gram count in training set will differ from the count in held-out corpus by a fixed number

In "August 2019 draft of the 3rd edition of Jurafsky & Martin Speech and Language Processing" book's section 3.5 (Kneser-Ney Smoothing) it is stated that The astute reader may have noticed that ...
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3answers
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How to extract insights from the given data?

Ok, I have this data with 3 columns, unique id, raw text, and review text. My task is play with the dataset and find meaningful insights from it. Raw text is in plain English but review text is in ...
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1answer
69 views

High / low resources language : what does it mean?

In NLP, languages are often referred as low resource or high resource. What these terms mean ?
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17 views

Preprocessing for Text Classification in Transformer Models (BERT variants)

This might be silly to ask, but I am wondering if one should carry out the conventional text preprocessing steps for training one of the transformer models? I remember for training a W2V or Glove, ...
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How can I extract vehicles model from a text using feature extraction? [closed]

I'm working on a project in which I should read thousands of emails and extract features from their bodies, among these features "vehicle model", for example in this text: ...
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Difference in the dimensionality of Entity and Relation embeddings in the RotatE framework

In the RotatE paper, tail embedding is supposed to be the Hadamard product of the Head and Relation embeddings. Quoting from the paper: Given a triplet (h, r, t), we expect that t = h ◦ r, where ...
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TLDR Bot - Sentence Tagging w/ BERT

Currently making a bot that condenses news articles. I'm tagging sentences as important or not important using a simple BERT classifier. The results were... not great. I'm really interested in how I ...
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1answer
25 views

Do repeated sentences impact Word2Vec?

I'm working with domain-oriented documents in order to obtain synonyms using Word2Vec. These documents are usually templates, so sentences are repeated a lot. 1k of the unique sentences represent 83%...
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8 views

Input embeddings to Transformers network

I have been learning about Transformer network and most of it clear because of some of the brilliant explanation provided by the experts in the field. Can someone explain about the input word ...
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7 views

NL2SQL task, if we have enough data, what will the model achieve for hard SQL?

We are afraid that the hard SQL like TABLE JOIN is the limit for industrial application. Addition info: https://yale-lily.github.io/spider Thank you very much.
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Best structure for a LSTM Bert sentence classifier

I'm interested in classifying sentences using BERT. Finetuning on a single sentence had very poor results. I'd like to add a forward and backward LSTM layer to try to improve results. I'm having ...
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1answer
25 views

How to select 500 most pertinents tags among 10000?

Say we have 100,000 documents tagged with 10,000 different tags (Max 5 tag per document). We wish to limit allowed tags to a list of 500 tags. How to select 500 tags in order to cover the largest ...
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23 views

how to use sklearn without feature selection

I am trying to study the effect of using feature selection onmy text classification code . I want to make a rating without any feature selection, but sklearn use document frequency (df) by default ...
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1answer
51 views

How to get sentence embedding using BERT?

How to get sentence embedding using BERT? ...
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1answer
14 views

help finding research discussion on HTS classification

My question is about the theory of this problem, and not necessarily syntax. I'm wondering if anyone here has experience with automating HTS (Harmonized Tax Schedule) classifications, specifically ...
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How to implement Dependency Parse for an column in excel sheet [closed]

I am confused with what Dependency Parser is and why is it used. Next, I need to know how to do Dependency parsing for an entire column in an excel sheet. The excel sheet contains details of positive ...
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is it possible to implement LSTM with input shape (sample,timestep,timestep,feature)?

I'm new to Keras. I am trying to implement this model https://www.aclweb.org/anthology/D15-1167 for document classification, and I want to use LSTM for getting sentence representation. I have trained ...
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1answer
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What is the reason behind having low results using the data augmentation technique in NLP?

I used Data augmentation technique on my dataset, to have more data to train. My data is text so the data augmentation technique is based on random insertion of words, random swaps and synonyms ...
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1answer
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How to group chat messages by topic?

I am a newbie in this field. Developer since 20 years and more but never done anything (except tutorials) with ML, DL, and NLP. Though I've already read a bunch of articles and tutorials about this ...
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1answer
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LSTM fot text classification always returns the same results

Hello fellow Data Scientists, I'm trying to make a classifier that was to classify sequences of text into some predefined classes, but i always get the same output, can anyone help me understand why? ...
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1answer
33 views

NLP techniques to label unlabeled data in a dataset [closed]

So I have a .xls file with negative and neutral reviews of a medicine. However, this dataset does not have labels. I converted this .xls into a dataframe and I am using the Spacy Lib. I have to use ...
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Looking for recommendations on auto-tagging approaches

Basically looking for approaches to solve the stackoverflow question tagging problem. I have seen a few papers already but in case I have missed something - asking here too. For anyone interested, ...
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Information Gain & Gini Index for NLP

I know how Information Gain and Gini Index work in General. I have problem figuring out how to apply these techniques in NLP and text feature extraction. Can someone show me an example of how to ...
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0answers
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What's the meaning of having a UNK token for out of vocabulary words during decoding?

Adding a UNK token to the vocabulary is a conventional way to handle oov works in tasks of NLP. It is totally understandable to have it for encoding, but what's the ...
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Getting error when trying retrieve polarity from russian sentence Polyglot(Python)

I trying to retrieve polarity from Russian sentence, using this code: from polyglot.text import Text as T print(T("ты не понимаешь").polarity) But I get the ...
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2answers
32 views

How to Extract Information from the Image(PNG)

I'm trying to extract some particular information from the image(png). I tried to extract the text using the below code ...
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1answer
18 views

Accessing Flask WS APIs over intranet -

I have 2 scripts - A.py and B.py, and both are Flask apps. A.py renders a web page and acts as my UI taking inputs from user. B.py is hold the main logic and has a web service API being called by A.py....
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1answer
34 views

Is Annoy a machine learning algorithm to find nearest neighbor ? and is it similar to K nearest neighbor algorithm?

I was researching about Google universal sentence encoding and i saw that it uses simple neighbor/Annoy to find the nearest vector for semantic-similarity search engine. This is the first time i'm ...
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0answers
19 views

How to access an embedding table that is too large to fully load into memory?

I'm currently trying to find a way of loading/deserializing a .json file containing Flair word embeddings that is too large to fit in my RAM at once (>60GB .json with 32GB of RAM). My current code for ...
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1answer
23 views

What dataset was Stanford NER trained on?

I would like to re-train the Stanford NER library from scratch as a 1 class model. Only 3,4 and 7 class models are available out of the box. Is it possible to obtain the data that the model was ...
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0answers
8 views

Recognizing emerging topic within ongoing topic

I have been collecting a large amount of tweets from Twitter for a few weeks related to a series of specific keywords, and would like to address a specific problem. Say I collected all tweets ...
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0answers
12 views

Can BERT embeddings be used to reproduce the original content of the text?

From what I understand, BERT provides contextualized embeddings that are not deterministic the way Word2Vec embeddings (i.e. the word "Queen" doesn't always produce the same vector, it'll be different ...
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1answer
33 views

Clause type classification

We would like identify similar text (clauses) on a contract based on a trained corpus. For instance: Contract - small sample ...
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21 views

Wikipedia corpus for NLP - Cleaned sentences

I can see many wikipedia dumps out there. I am looking for a wikipedia-made corpus, in which every line is one sentence, without any wikipedia meta tags.
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1answer
13 views

what is sentence embeding and how to do sentence embedding for a sentence and how to use word embedding to create a sentence embedding?

what is sentence embeding ? How to do sentence embedding for sentence like example ""How old are you" ? how to use word embedding to create a sentence embedding ?
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1answer
120 views

Why is word prediction an obsession in Natural Language Processing?

I have heard how great BERT is at masked word prediction, i.e. predicting a missing word from a sentence. In a Medium post about BERT, it says: The basic task of a language model is to predict ...
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1answer
32 views

can we learn a model to pre-process text? [closed]

I'm in a very dire situation where I have to preprocess the text but the text in the documents is very random. It is in the form of numerical points. I want to remove a certain class of points (...
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1answer
23 views

How to generate abbrevations from shortend words in medical records

I have text files which contains medical history of a patients and would want to extract information out of it. Basically what want is generate english text of abbrevations,semantic category region,...
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2answers
26 views
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1answer
59 views

Can AI (NLP) convert user questions (text) into database SQL queries?

I have been reading about NLP but got confused and not able to figure out - if it is feasible for NLP to convert questions in natural language to transform into SQL queries (so that it can execute on ...
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1answer
21 views

Information Extraction/Semantic Search for long, unstructured documents

I am stuck with a particular task of information extraction. I have a few hundred, long (5-35 pages) pdf, doc and docx project documents from which I seek to extract specific information and store ...
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2answers
18 views

What does Conv1d do in a sentiment analysis?

I am doing some study on https://www.kaggle.com/anshulrai/cudnnlstm-implementation-93-7-accuracy I understand we need LSTM to capture the sequence of words in the sentience, but I am not quite ...
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1answer
31 views

What does the dimension represent in the GloVe pre-trained word vectors?

I'm using GloVe pre-trained word vectors (glove.6b.50d.txt, glove.6b.300d.txt) to word embedding. I have a conceptual question: What is the difference between these files? On the other hand, what ...
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1answer
52 views

How to cluster n-grams?

I just wanted to know how to cluster n-grams based on their semantics. Like clustering together n-grams that are semantically similar by leveraging the distributional hypothesis suggesting that ...
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0answers
17 views

Training data requirements for NLP models

Are there general guidelines for how much data is required for natural language processing (NLP) classification models? I understand this may depend on the text quality, text length, how accurate the ...
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1answer
31 views

How to choose solution - Neural Neworks or Scikit-Learn/Numpy/Pandas?

I am trying to solve a problem - categorising and routing service desk emails to concerned teams for resolution. Created and tested a model using Scikit-Learn, Numpy and Pandas. - Tokenized the email ...