Questions tagged [natural-language-process]

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. See NLP.

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22 views

where to start in natural language processing for a language

My native language is a regional language and few people speak it. I have some assignements in a machine learning course and i was thinking about doing some natural languge processing on my native ...
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Has this method of NLP processing with neural networks been done?

Take the sentence: If you see a green light then you may cross the road. I propose a neural network which produces as output from this sentence 3 masks and a ...
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Generate Intro-Text for Newsletter

I am trying to implement the following idea. For a daily newsletter I would like to generate an engaging and funny intro text, such as: Good morning. Sorry if there are beer stains and buffalo ...
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what is BIO Tags for creating custom NER Named entity recognization?

I want to create custom NER Named entity recognition but im confused with this part of what is BIO Tags .Can any please explain the steps for creating NER anda bout this B,I,O tag .
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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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17 views

Looking for a causality to effect dataset

I am looking for a causality dataset that would look like this: ...
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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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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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How to generate alignments for word-based translation models if number of words are different in both sentences

I am working on implementing IBM Model 1. I have a parallel corpus of some 20,00,000 sentences (English to Dutch). Also, the sentences of the two docs are already aligned. Aim is to translate a Dutch ...
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21 views

Why don't we use BCE(Binary Cross Entropy) for language modeling?

I've seen a lot of RNN/Transformer models use cross entropy loss with softmax. but isn't language modeling a multilabel classification task? what happens if we replace cross entropy loss with binary ...
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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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Combining two structurally similar datasets from different sources

I am working with a text summarization problem, and I am trying to use this architecture [Pointer Generator]. My data set is VERY small (225 samples) compared to the CNN/ Daily Mail dataset this paper ...
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What's the size of Google's complete Conceptual Captions image captioning dataset with all the images downloaded from the listed URLs?

The original dataset provided by Google, here, consists of 'Image URL - Caption' pairs in both the provided training and validation sets. I have to work on an image captioning project and wanted to ...
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How prevalent is `C/C++` in machine learning development?

I am currently a data scientist mostly doing NLP, and I do most of my work inPython. Since I didn't get a CS degree in undergrad, I've been limited to very high ...
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Finding Criminal Name in news?

We have news URLS, which we want to classify into crimes or non-crimes and further identify criminals by using NERs. For creating a model that identifies criminals, we tried SPacy which gave all the ...
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What's the best way to store BERT training data (input IDs)

The tricky thing about the input IDs is what they're varying in length for each data sample, so regular hdf5 may not be ideal. Since Bert is so popular I am wondering if there's an established way to ...
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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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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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35 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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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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Combining decision trees and neural networks for classifying text with metadata . How to combine and train?

I have a multi-label classification problem where the input consist of free text, with metadata such as categories (from a fixed, limited set) associated with each text. The output consist of a set of ...
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Assign Topic to a document after LDA

I executed my LDA and now I have several topics with their word distributions. How do I assign each document to a topic? Is Euclidean distance a good choice? Or there are other methods? Thanks
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POS extraction using CoreNLP

I have a corpus of windows related documents, for which I need to extract nouns and verbs. However, it is required that I keep certain windows specific words such as "inline hooking", "instruction ...
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Document embedding vs locality sensitive hashing for document clustering

I would like to compare two methods: locality sensitivity hashing and document embedding to get the similarity between two documents. Both of those methods encode information of a document in a ...
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3answers
113 views

Using Google Translate API to create a Translation Dataset

Is it a good idea? ;-) Is it legal to do so? Is it legal to release such a dataset to public? Say I have a language X for which I want to create a dataset for translation to/from English, for which I ...
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1answer
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Automatically categorize parts of a piece of writing

Suppose I had a piece of writing. The document contains aspects like questions, assertions, examples, and explanations. Is it possible to use Natural Language Processing to categorize each sentence of ...
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How to extract electroglottograph/laryngograph using python(specially in “librosa”)?

in a certain project of mine which is related to feature extraction from speech data, I want to extract some electroglottograph/laryngograph from speech data, I have read some research papers but ...
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Where can I get an untokenized version of GLUE's SST-2 dataset?

On the GLUE faq, they say: Similarly, for SST, the data provided is already tokenized. We're working on obtaining a version that is not tokenized. Feel free to train on other distributions of ...
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59 views

What is the State-of-the-Art open source Voice Cloning tool right now?

I would like to clone a voice as precisely as possible. Lately, impressive models have been released that only need about 10 s of voice input (cf. https://github.com/CorentinJ/Real-Time-Voice-Cloning),...
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1answer
25 views

Is it possible to create a rule-based algorithm to compute the relevance score of question-answer pair?

In information retrieval or question answering system, we use TD-IDF or BM25 to compute the similarity score of question-question pair as the baseline or coarse ranking for deep learning. In ...
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1answer
21 views

What is the easiest way to identify a gender for a noun (in a french)?

I am working on an app where in order to process some data, I need to be able to identify the gender for some selected words. My data is in French. The feature I am looking for should be able to tell ...
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1answer
52 views

NMT, What if we do not pass input for decoder?

For transformer-based neural machine translation (NMT), take English-Chinese for example, we pass English for encoder and use decoder input(Chinese) attend to encoder output, then final output. What ...
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Step extraction from a paragraph

Came across an interesting problem: Given a paragraph describing how to do a process, need to break it down to various steps. Basically, need to determine for each sentence in the paragraph, if this ...
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1answer
14 views

Identifying if the sentence if it comprise information about education

Given a sentence I am trying to classify if the sentence contain information about education. For example: ...
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1answer
78 views

How to extract location related terms from raw text in python

I want to extract location related keywords from raw text in python. I have already tried spacy but the results were not good and I just got names of countries while I want fine-grained location ...
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1answer
19 views

Simplest way to build a semantic analyzer

I want to build a semantic analyzer i.e., to find how similar the meaning of two sentences are. For example- English: Birdie is washing itself in the water basin. English Paraphrase: The bird is ...
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1answer
45 views

Smallest Possible Dataset for Text Classification using BERT

What are your experiences for appropriate dataset sizes for usual text classification tasks using a finetuned BERT such as sentiment analysis? ~100 examples ~1000 examples ... ~10000000 examples ...
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Skip-gram trained on The Hobbit: no improvement in the similarity of the word representation

I've trained a simple skipgram NNLM (window size = 5) on The Hobbit. This is the rough pseudocode: ...
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1answer
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Why are bigger embedding vectors not necessarily better?

I'm wondering why increasing the dimension of a word dimension vector in NLP doesn't necessarily lead to a better result. For instance, on examples I run, I see sometimes that using a pre-trained 100d ...
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Training data for doc2vec models, general vs specific

I have quite a general question about doc2vec models. Let's say I have a specific NLP task whose goal is to understand the similarity between two sports news articles. Now I have the option to train ...
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1answer
25 views

Why TREC set two task: document ranking and passage ranking

TREC is https://microsoft.github.io/TREC-2019-Deep-Learning/ I am new to text retrieval. Still can not understand why set the two similar task. Thank you very much.
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33 views

Guidelines to debug REINFORCE-type algorithms?

I implemented a self-critical policy gradient (as described here), for text summarization. However, after training, the results are not as high as expected (actually lower than without RL...). I'm ...
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What can NLI do for a chatbot?

Natural Language Inference(NLI) is the task of predicting the labels(entailment, contradiction, and neutral,) for sentence pairs. People invent a lot of deep model to solve this problem. But I can ...
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1answer
36 views

For text match problem, what is the different between question-question match and question-answer match?

I know question-question match is a text similarity problem. What about question-answer or question-doc match? It is used in information retrieval. question-question match is indeed text similarity. ...
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How can one determine that Word2Vec (CBOW method) embeddings are related to each other?

I read some fascinating stuff about the potential for using the Word2Vec algorithm to speed up the pace of scientific discovery here https://www.researchgate.net/publication/...
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Multitask learning in Keras issue

I am implementing multitask regression model using code from the Keras API under the shared layers section. There are two data sets, Let's call them data_1 and <...
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Difference between from nltk import word_tokenize and from nltk.tokenize import word_tokenize?

What is the difference between the word_tokenize, one imported directly from nltk and the other being imported from tokenize package of nltk?
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221 views

What is the best question generation state of art with nlp?

I was trying out various projects available for question generation on GitHub namely NQG,question-generation and a lot of others but I don't see good results form them either they have very bad ...
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NLP - How can I encode single words from one feature (No word frequency)

I'm constructing a pandas data-frame as an input for some sklearn machine learning models. It is a supervised learning problem that consists in classify 'words' included in the body-content of ...
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fit transform with mysterious special chracters

I tried to make the bag of words ...