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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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Having trouble figuring out how loss was calculated for SQuAD task in BERT paper

The BERT Paper https://arxiv.org/pdf/1810.04805.pdf Section 4.2 covers the SQuAD training. So from my understanding, there are two extra parameters trained, they are two vectors with the same ...
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sklearn & Meanshift for NLP only returns 1 cluster

I am using sklearn.clustering to work with some text data and the MeanShift algorithm. I have: Done all standard NLP data prep like lemmatizing, removing stop ...
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18 views

Scraping financial web data

I recently started working as a data scientist and I am starting a web scraping and NLP project using Python. The idea is to create a program that searches for public information on the company's ...
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19 views

Predict a 100 dimensional array

I have several sentences that I transformed into vectors. With these vectors I would like to predict another vector (which represents a vector of a sentence (the answer)). Can you tell me if this ...
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10 views

How to use document and sentence embeddings in Keras?

Although there is an easy way to use Embedding layer in keras and make use of pretrained word embeddings, is there a way to use document or sentence embeddings?
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18 views

Chinese Word Segmentation

Following this paper https://www.aclweb.org/anthology/D17-1079, I'm stuck with Paragraph 2 "Baseline Segmentation Model". In this section they basically explain how they feed the chinese word ...
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8 views

BERT has a non deterministic behaviour

I am using the BERT implementation in https://github.com/google-research/bert for feature extracting and I have noticed a weird behaviour which I was not expecting: if I execute the program twice on ...
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17 views

NLP - Retrieval-based model

My goal is to predict the most appropriate answer from an utterance, in a group of 21 potential answers. (I'm not sure the "question" is called utterance though. ) Example: Utterance: How are you ...
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9 views

Is there a rule for deciding dictionary size for sentiment analysis with massive datasets?

I will be performing sentiment analysis on fiction. I'll be working with around 300 books of 350 pages. Before performing word2vec, can I limit the dictionary size by ignoring less frequent words? If ...
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7 views

Neural network that identify if tagging looks like a real tag

i want to build rnn that say how likely a tag (my other lstm) is a good fit for some sentences Which model should i use? I have data set of songs and matching chords... The lstm is input is sentence ...
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10 views

Why is the training accuracy not increasing while implementing MemN2N?

I am trying to implement the MemN2N from here. I tried implementing the code using the Functional API of Keras. I tried changing the optimizer but, I got the same results. Below is my implementation:...
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38 views

How to build a machine translation system for a new language

I am trying to figure out what are the options for building a natural language translation model for a language that is not yet supported by existing machine translations. The project is to build a ...
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33 views

Extracting data from documents

I'm looking for guidance on taking a large documnet such as this clinical study and extracting various pieces of information. For example, I'd like to locate "Exclusion criteria" and extract: On page ...
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24 views

Policy gradient/REINFORCE algorithm with RNN: why does this converge with SGM but not Adam?

I am working on training RNN model on caption generation with REINFORCE algorithm. I adopt self-critic strategy (see paper Self-critical Sequence Training for Image Captioning) to reduce the variance. ...
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3answers
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Why did Logistic regression perform better than svm? [closed]

I have a data set of movies and their subtitles.My task is to classify them based on their ratings-[R,NR,PG,PG-13,G]. I have tried different ML algorithms and found that Logistic regression out ...
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1answer
23 views

Will a Count vectorizer ever perform (slightly) better than tf-idf?

For the task of binary classification, I have a small data-set of a total 1000 texts (~590 positive and ~401 negative instances). With a training set of 800 and test set of 200, I get a (slightly) ...
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1answer
30 views

What does localist one-hot vector mean in cs224n NLP course?

Chris said one-hot is a "localist" representation. what does "localist" mean here? I've searched on recommended text, didn't find explanation. any clue?
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1answer
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Story Tag Prediction - Optional Labels

I'm currently working on a prediction for fiction. I have a database with fiction, which are each described with different story tags. My idea is to use a neural network that can tell you by ...
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22 views

Output range of BERT model shrinks after fine-tuning on domain specific dataset

My model's sigmoid output range has shrunk after transfer learning with small a dataset. My pretrained model has an output range of 0 to 1. After fine-tuning with a smaller domain specific dataset, ...
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1answer
20 views

How does GlobalMaxPooling work on the output of Conv1D?

In the field of text classification, it is common to use Conv1D filters running over word embeddings and then getting a single value on the output for each filter using GlobalMaxPooling1D. As I ...
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2answers
36 views

Training an acoustic model for a speech-to-text engine

What are the steps for training an acoustic model? The format of the data (the audio) includes its length and other characteristics. If anyone could provide a simple example of how to train an ...
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1answer
25 views

Is there any NLP library or package which can help in adding coma, punctuations, new line appropriately in text?

I have movie transcript, where no coma, punctuations or new line. Is there any NLP technique which can help to implement this?
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14 views

Methods for Finding correlation between various types of columns?

When we have a combination of columns of text, numerical and categorical data, what are the general ways to obtain correlation among the columnar variables other that Chisquare test of independence ...
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30 views

Seq2seq model that gets as input a sentence and outputs the same sentence

I tried to implement a model that takes as input sentences, which are hate_tweets and outputs exactly the same sentences. For this reason, I gave Input to the encoder and decoder exactly the same ...
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1answer
32 views

How to find possible subjects for given verb in everyday object domain

I am asking for tools (possibly in NLTK) or papers that talk about the following: e.g. Input: Vase(Subject1) put(verb) Ans I am looking for: flower, water Is there a tool that can output subjects (...
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1answer
60 views

Sentence similarity using Doc2vec

I have a list of 50k sentences such as : 'bone is making noise', 'nose is leaking' ,'eyelid is down' etc.. I'm trying to use Doc2Vec to find the most similar sentence from the 50k given a new ...
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robots.txt communication in webmining

I hope this is the right subforum as I did not really find a suiting one. I'm mining data from a website that does specifically exclude some crawlers from it's site in the robots.txt like this: ...
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What to use in setting up a Speech to Text engine in production?

So i have the task to study the feasability of setting up a Speech-To-Text engine in a production environnement, and i've been researching on this topic, so I tried Google's Speech-To-Text API and ...
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12 views

How PV-DBOW works

The authors of the Paragraph Vector paper describe PV-DBOW with: 2.3. Paragraph Vector without word ordering: Distributed bag of words The above method considers the concatenation of the ...
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1answer
39 views
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60 views

Named Entity Recognition using context of the sentence

I have a problem in which I want to know how can we extract or name the entity based on the context in which it is getting used in a sentence. For example: If we have to extract date field which is ...
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32 views

Implementation of NLP to categorize text into two categories

I can't discuss my actual dataset, so please bear with me. Let's say I have a dataset that contains a population of 20,000 examinations by a school principal. The principal is to record their ...
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19 views

Given an input phrase, is there a way I can find the most similar phrase within a document?

I am completing a task where I need to retrieve the corresponding values to a set of given labels from many legal contracts. For example, one of the labels is "Floating rate payment dates" and it's ...
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19 views

Implementing back translation as a data augmentation for text classification

Since back translation English->other language -> English seems like quite a useful data augmentation technique , I wanted to experiment with it. E.g. it occurred to me that languages from very ...
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Why does all of NLP literature use Noise contrastive estimation loss for negative sampling instead of sampled softmax loss?

A sampled softmax function is like a regular softmax but randomly selects a given number of 'negative' samples. This is difference than NCE Loss, which doesn't use a softmax at all, it uses a ...
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How is WordPiece tokenization helpful to effectively deal with rare words problem in NLP?

I have seen that NLP models such as BERT utilize WordPiece for tokenization. In WordPiece, we split the tokens like playing to play and ##ing. It is mentioned that it covers a wider spectrum of Out-Of-...
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44 views

How to calculate which word fits the best given a context and possible words?

I have this task for research purposes and searched a while for a framework or a paper which already took care of this problem. Unfortunately I don't find anything which helps me with my problem. I ...
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28 views

What simple machine learning algorithm to use in POS tagging [closed]

I am new to NLP. I have a dataset that has already a parts of speech included, the only problem is what algorithm to use in order to train my dataset. Sample: ...
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How to measure the co-variance of multiple choice answers to predict a correct answer to a question?

In this article from Mux: https://mux.com/blog/how-we-used-machine-learning-to-predict-hq-trivia-answers/ They talk about using the xgboost algorithm to predict multiple choice answers to questions ...
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1answer
24 views

How to use a one-hot encoded nominal feature in a classifier in Scikit Learn?

Im working on a genre classification problem on a songs dataset. Since genre is a nominal feature, I used sklearn's LabelBinarizer to get the one-hot encoding for this feature for every row in the ...
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1answer
30 views

Automatic labelling of text data based on predefined entities

I'm new to NLP. I have a folder containing .txt files which are legal and specific documents. I want to label all these files based on four predefined labels. How can I do that automatically?
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Multi review summarization

I'm working on a project in which I want to summarize hotel reviews into a abstract summary. I've seen that RNN are the state of the art among all the abtractive summarization methods (Seq2Seq, etc). ...
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32 views

Paragraph Generator using BERT or GPT

I am trying to generate similar sentences, called paragraph generation. For example, what is the name of eldest brother of ram? - For this paragraphs can be - who is oldest brother of ram? , Who is ...
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3answers
192 views

Fuzzy name and nickname match

I have a dataset with the following structure: ...
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1answer
16 views

Using the Stanford Named Entity Tagger in R [closed]

I am experimenting with the Stanford Named Entity Tagger here http://nlp.stanford.edu:8080/ner/process and I feel it would be useful in my research. Does anyone know of a example that I could follow ...
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1answer
54 views

How can I output tokens from MWE Tokenizer?

How to output the tokens produced using MWE Tokenizer? NLTK's multi-word expression tokenizer (MWETokenizer) provides a method/function add_mwe() that allows the ...
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1answer
48 views

Detect sensitive data from unstructured text documents

I know this question is broad, but I need an advice to know if it's possible to achieve what I want to do. The problem is that I have around 2500 documents with sensitive data being replaced by four ...
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2answers
31 views

ways to represent document by its keyword vectors

I have documents, say for example, D1, D2, D3... Dm. Every Di has its individual components or keywords k1, k2, k3,... kn, where ki is an n-dimensional vector. The number of individual components ...
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1answer
22 views

Comparing English word pronunciation complexity

I'm trying to figure out a way to compute a score for the pronunciation of a given english word, so I can use that score to compare the pronunciation complexity between english words. Eg: Given ...
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“Context Resolution” Task in NLP

I'm looking for references to a standard(-ish) task/dataset in NLP that is close(-ish) to the following: we have a document with a list of references (sorry), for example, a scientific paper. For ...