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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301 views

ngram and RNN prediction rate wrt word index

I tried to plot the rate of correct predictions (for top 1 shortlist) with relation to word's position in sentence : I was expecting to see a plateau sooner on the ngram setup since it need less ...
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3answers
73 views

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

I'm 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
150 views

How to compute document similarities in case of source codes?

I try to detect the probability of common authorship (person, company) of different kind of source code texts (webpages, program codes). My first idea is to apply the usual NLP tools like any token ...
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2answers
1k views

How to add extra word features other then word Embedding in Recurrent Neural Network model

I am building a deep learning model for NLP. I am pretty comfortable with adding word embedding from word2vec or Glove vectors as extra word features but I wanted to add other word features like POS ...
4
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1answer
97 views

Autocomplete with deep learning

I got interested in autocompletion using deep learning and tutorials that I found where conditioned always on specific number of characters (given 40 characters predict the next character or the whole ...
4
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2answers
86 views

How to detect if one tweet is agreeing with another

I want to detect tweet text agreement. Suppose someone posts some subjective opinion in twitter. Other users will post reply either agreeing or opposing the original tweet. I want to estimate the ...
4
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1answer
1k views

Convolutional Network for Text Classification

I am trying to train a convolutional neural network with Keras at recognizing tags for Stack Exchange questions about cooking. The i-th question element of my data-set is like this: ...
4
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2answers
196 views

How to change default values of ANNIE resources in GATE from java code?

In GATE default values for ANNIE are set during initialization, but sometimes based on requirements they have to be changed. My Requirement : I want to extract English sentences without considering ...
3
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0answers
66 views

Why categorical cross entropy loss is not correlated with NLP scores?

I'm training a deep network for image captioning which is consist of one CNN and three GRUs. During training epoch by epoch model loss (categorical cross entropy) decreases but when I'm measuring <...
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0answers
1k views

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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68 views

How to implement hierarchical labeling classification?

I am currently working on task of eCommerce product name classification, so I have categories and subcategories in product data. I noticed that using subcategories as labels delivers worse results (84%...
3
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1answer
63 views

How to train neural word embeddings?

So I am new to Deep Learning and NLP. I have read several blog posts on medium, towardsdatascience and papers where they talk about pre-training the word embeddings in an unsupervised fashion and then ...
3
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0answers
144 views

What are the rules when extracting SVO triples from preprocessed text?

If you have some already preprocessed text that is tagged, what are the rules to extract SVO triplets if you want a triple like (word, word, word). Can you give the sentence as example and extract all ...
3
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1answer
277 views

Find matching text from a text column

This is my first time to use Data Analytics tool to figure out a solution to a problem. I have a table with following columns ...
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0answers
81 views

multiple intents for modifying an intent of a sentence?

Say I have a sentence like 'I refuse to fly' or 'I'd like to fly'. I also have a sentence like 'I don't want to sit'. When training custom intents in one of the available NLU engines (rasa/wit/luis), ...
3
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1answer
67 views

Is it a red flag that increasing the number of parameters makes the model less able to overfit small amounts of data?

I'm training a deep network (CNN-LSTM-CRF) for Named Entity Recognition. Is there a reason that increasing the number of parameters would make the network less able to overfit a small training set (~...
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55 views

Determine the most important documents for supervised learning

I have somewhat of a general/high level question. Assume I'm doing supervised machine learning on some text data (tweets for example) and categorizing the documents to a certain taxonomy (multi-class ...
3
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1answer
111 views

How to filter Named Entity Recognition results

I have a pipeline built which at the end outputs a bunch (thousands to tens of thousands or more) of named entities. I'd like to do aggregates on those named entities (to see, e.g. how many times a ...
3
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2answers
1k views

Are there any good NLP APIs for comparing strings in terms of semantic similarity?

I want to create a chatbot which informs the user about traffic at the streets but not in real-time for the moment. I have created a small database with MySQL which has some data stored regarding ...
3
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0answers
128 views

How to do give input to CNN when doing a text processing?

As a signal processing engineering and being new to NLP, I am confused with giving input to CNN network. With my knowledge of CNN, I am trying to build a classifier for ethnicity with inputs as text ...
3
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2answers
4k views

Is there “Attention Is All You Need” implementation in Keras?

Has anyone seen this model's implementation using Keras? inb4: tensorflow, pytorch
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149 views

How does Api.ai Google dialogueflow classifies “intent” and extracts data from slots

I am trying to build a very naive version of Api.ai, now Google DailogueFlow. I wanted to know two things. How DF classifies sentences with entities in it that can be user created and/or things like ...
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2answers
217 views

How to create domain rules from raw unstructured text using NLP and deep learning?

How to create domain rules from raw unstructured text using NLP and deep learning techniques ? For example for the below text on symptoms of Dengue, all three look pretty similar but if you want to ...
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0answers
269 views

sLDA vs. LDA+Classifier

For simplicity, suppose we're looking at Yelp reviews of restaurants, and are trying to classify the restaurant by cuisine type (e.g. "Italian, Japanese," etc.). Lets also assume our data already a ...
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0answers
403 views

Complete a Hungarian stem to a real word

I'm quite new to the NLTK package of Python and to NLP too (I usually work in R but for NLP purposes and scraping maybe Python is more able). I scrap articles from Hungarian newsportals and want to ...
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11 views

How does a CBoW model convert a word to a vector?

A CBOW model actually takes multiple words as inputs and a targeted central word as the output. So, the trained model actually maps several words to a single one, I mean it takes context words and ...
2
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1answer
22 views

Is there a way to rank the Extracted Named Entities based on their importance/occurence in a document?

Looking for a way to rank the tens and hundreds of named entities present in any document in order of their importance/relevance in the context. Any thoughts ? Thanks in advance!
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29 views

how to work with NLP with other features

My dataset looks like this ...
2
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1answer
17 views

How to segment old digitized newspapers into articles

I'm working on a large corpus of french daily newspapers from the 19th century that have been digitized and where the data are in the form of raw OCR text files (one text file per day). In terms of ...
2
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1answer
13 views

Classification of substrings?

What is the appropriate method to find n-grams/sub-phrases/parts-of-sequences that are referring to a specific topic or belong to a certain category? For instance: Imagine a topic of "transfer of ...
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0answers
11 views

pros and cons of lexical vs machine learning methods for text mining

I wanted to know what are the pros and cons are of using lexical methods and machine learning methods for classifying texts based topic. I have used a simple method of mining documents related to a ...
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0answers
50 views

SpaCy vs AllenNLP?

I have used a little of both spaCy and allenNLP in my NLP projects. I like them both as they work very well with PyTorch (my DL framework choice!). But, I still cannot decide which one to master in a ...
2
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0answers
33 views

Predicting word from a set of words

My task is to predict relevant words based on a short description of an idea. for example "SQL is a domain-specific language used in programming and designed for managing data held in a relational ...
2
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1answer
40 views

Xgboost multiple class predictive performance beats one versus rest

I have an NLP task I'm tackling with xgboost (R implementation). Before describing my doubt I'll give you some background: I have a corpus of documents for which I did topic discovery, using a term ...
2
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2answers
25 views

Using nlp to analyze accident report

I want to use Natural Language Processing to analyze traffic accident reports and from the text determine two things: Direction of vehicle travel (just compass directions like north, southeast, etc.)...
2
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0answers
110 views

How to add a CNN layer on top of BERT?

I am just playing with bert (Bidirectional Encoder Representation from Transformer) Research Paper Suppose I want to add any other model or layers like Convolutional Neural Network layers (CNN), Non ...
2
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0answers
63 views

meaning of fine-tuning in nlp task

There are two types of transfer learning model. One is feature extraction, where the weights of the pre-trained model are not changed while training on the actual task and other is the weights of the ...
2
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1answer
56 views

Bidirectional Encoder Representations from Transformers in R

Can anybody suggest to me, where I can find example code for R language for BERT neural network for text mining tasks. All I can see are python examples, and I need R. https://github.com/google-...
2
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0answers
21 views

identifying the primary and secondary keywords in sentense

want to identify the primary and secondary keywords which are having an impact to sentences or comparison between 2 keywords. below is the example India and China has highest population in the ...
2
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0answers
84 views

Convert natural language text to structured data

Convert natural language text to structured data. I'm developing a bot to help user assist in identifying Apparels. The problem is to convert natural language text to structured data (list of ...
2
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0answers
50 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 ...
2
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1answer
286 views

Training NLP with multiple text input features

Question: How can I train a NLP model with discrete labels that is based on multiple text input features? Background: I'm trying to predict the difficulty of a 4-option multiple choice exam ...
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0answers
38 views

What is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?

I was reading the paper by Kalchbrenner et al. titled A Convolutional Neural Network for Modelling Sentences and am struggling to understand their definition of convolutional layer. First, let's take ...
2
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0answers
124 views

Keras value error: Operands could not be broadcast with with shapes(100,100) - GRU

I am trying to use Hierarchical Attention Networks for classification of news articles using 20 newsgroup dataset that i downloaded from the internet. I came across this code of the implementation and ...
2
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0answers
82 views

how to extract the Top contributing labels/words in universal-sentence-encoder-large - TransformerModel?

I'm using the universal-sentence-encoder-large (Transformer Model) encoding process for embedding and then using the embedding for Clustering - Basically for unsupervised learning. I want to get the ...
2
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0answers
28 views

I have data of some movies and their subtitles.I want to classify them based on their ratings

I will convert the subtitles into vectors and use them as features to classify the movies into different categories based on their ratings.The problem that I am facing is my feature vector is much ...
2
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0answers
367 views

Combine multiple features for text classification

Recently I started reading more about NLP and following tutorials in Python in order to learn more about the subject. I'm trying to make my own classification algorithm (the text sends a positive/...
2
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1answer
26 views

Classifying objects based of a varying number of the same type of feature vector for each object

For a congressional session, I have created a doc2vec model of speeches made. Using the vectors from this model, I have a dataset of each congressperson, their political affiliation, and a list of the ...
2
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2answers
2k views

Increasing SpaCy max NLP limit

I'm getting this error: ...
2
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1answer
34 views

How to compute unseen bi-grams in a corpus (for Good-Turing Smoothing)

Consider a (somewhat nonsensical) sentence - "I see saw a see saw" The observed bi-grams would be: "I see""see saw""saw a"and,"a see". My aim is to smoothen out the probability mass of the bi-gram ...