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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5 views

Literature on selecting specific dimensions in a word embedding vector

I am aware that the different dimensions in the word embedding represents different information and algebraic operations can be performed between two embeddings for example. Can anyone point me to ...
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How to fine-tune BERT for Question Answering?

I wish to train two domain-specific models: Domain 1: Constitution and related Legal Documents Domain 2: Technical and related documents. For Domain 1, I've access to a text-corpus with texts from ...
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Is there a NLP model that creates a code words for each of the columns

Following are the sample list of columns names; words. I've many more such column names. ...
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1answer
20 views

Unhashable type 'list' while looping through dataframe in Python [closed]

I have the following dataframe comments. I have segregated a list of users based on certain conditions. I want to get the count of words based on those users which ...
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1answer
19 views

How to work with different Encoding for Foreign Languages

I've got a Word Embedding File called model.txt. This contains 100 Dimensional vectors for over a million French words. These words contain accented characters such ...
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1answer
39 views

KeyError: Selecting text from a dataframe based on values of another dataframe

I have the following two dataframes badges and comments. I have created a list of 'gold users' from ...
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spacy train cli compare iterations

So I am running: spacy train da [several] [options] [here] And I am getting a nice overview in the console about the performance of each iteration, in terms of P, ...
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1answer
20 views

How to use unigram and bigram as an feature on SVM or logistic regression [closed]

How to use unigram and bigram as an feature to build an Natural Language Inference model on SVM or logistic regression?on my dataset i have premise, hypotesis and label column. I'm planning to use the ...
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Comparing soccer teams name

Which is the best way to compare soccer teams from different sites? For example, one soccer team in a site is named Academica Clinceni and in another ...
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How does the ULM subword tokenization avoid just splitting every word into single characters?

According to the paper: The probability of a subword sequence X = (x_1, x_2...) is formulated as the product of the subword occurence probabilities p(x_i) sum_i p(x_i) = 1 The most probable ...
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CountVectorizer vs HashVectorizer for text

I'd like to tokenize a column of my training data (n-gram word-wise), but I'm working with a very large dataset distributed across a compute cluster. For this use case, Count Vectorizer doens't work ...
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1answer
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What are the merges and vocab files used for in BERT-based models?

The title says it all. I see plenty online about how to initialize RoBERTa with a merges and vocab file, but what is the point of these files? What exactly are they used for?
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Extract average embedding for class labels

I am training a neural net to predict class labels for documents. The first layer in my network is an embedding layer. Once the network is fully trained, I would like to extract an a single embedding ...
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Next sentence prediction in RoBERTa

I'm trying to wrap my head around the way next sentence prediction works in RoBERTa. Based on their paper, in section 4.2, I understand that in the original BERT they used a pair of text segments ...
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What methods to get the intention behind questions (time, preferences, …)?

I have a csv with different questions, answer and question types. So far I have only been able to differentiate the questions between muliple answers and likert scale. I would rather like to get the ...
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1answer
29 views

Does stronger regularization always improve performance on testing set?

I am using the Sklearn logistic regression function to do a binary classification task on texts. I did the task using three different inputs: Bag-Of-Words, TF-IDF, Doc2vec embeddings. The question is ...
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1answer
18 views

Automatic topic labelling for topic modelling

I am just curious to know if there is a way to automatically get the lables for the topics in Topic modelling. It would be really helpful if there's any python implementation of it.
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1answer
48 views

Sentiment analysis of tweets (Train model on a labelled dataset and use on some other unlabelled data)

I have a huge amount of tweets on a particular topic say 'ABC' and the data is not labelled. I want to perform multi-class sentiment analysis of these tweets. I tried many unsupervised clustering ...
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1answer
18 views

Question on bootstrap sampling

I have a corpus of manually annotated (aka "gold standard) documents and a collection of NLP systems annotations on the text from the corpus. I want to do a bootstrap sampling of the system and ...
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10 views

Grouping pages in a pdf into logical documents

I have a PDF file(single training instance) which can contain many documents(multi-page) within it(invoices, different forms, emails). For e.g a single PDF instance may contain Page 1 - Overall Cover ...
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2answers
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How can I encode a 'Name' so that similar names are represented by vectors close in n-dimensional plane?

I want to encode names of people for similarity comparison between them such that a name like 'Sarah' is closer when represented in vector to a name like 'Sarah connor', something very similar to what ...
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1answer
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1answer
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Closed Domain Question Answering which doesn't answer Questions

I've been exploring Closed Domain Question Answering Implementations which have been trained on SQuAD 2.0 dataset. Ideally, it should not answer questions which the context text corpus doesn't contain ...
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1answer
34 views

How to build recommendation model based on resume and job description?

How to build a model which will result in better recommendation of resumes based on the job description given? I am familiar with bow or tfidf (n-grams) approach and then take a cosine similarity but ...
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1answer
17 views

Constituency vs Dependency Parsing: What is more effective for Sentiment Analysis?

Parsing is often used to understand the sentiment of complex sentences filled with double negations or very articulated. There are two main ways of parsing a sentence: Constituency and Dependency ...
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Model for extracting common context from a similar cluster of sentence

I have multiple clusters of similar sentence embeddings (one cluster holding one type of similar sentences) Is there any unsupervised model that can extract the common context from each cluster in: a ...
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1answer
17 views

ELMo - How does the model transfer its learning/weights on new sentences

Word2vec and Glove embeddings have the same vector representation for every word in the corpus and does not take context into consideration. For eg: The dog does bark at people The bark of the tree ...
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N-grams for RNNs

Given a word $w_{n}$ a statistical model such a Markov chain using n-grams predicts the subsequent word $w_{n+1}$. The prediction is by no means random. How is this translated into a neural model? I ...
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1answer
25 views

Classify text as logical/ not logical

Can some one advise me direction where to look in.Or some resources. Here is a task: User leaves feed back-text with min 50 characters. I need to check if it's normal human sentences/ word ...
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1answer
23 views

LSA, LDA or NMF in Topic Modeling?

I'm trying to implement Topic Modeling via Python & NLP but can't figure out what algorithm should I use. I have studied Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and Non-...
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Explain FastText model using SHAP values

I have trained fastText model and some fully connected network build on its embeddings. I figured out how to use Lime on it: complete example can be found here: https://medium.com/@ageitgey/natural-...
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1answer
23 views

Detect passive voice in headlines

To detect passive voice in sentences, we can use the spacy module to tag each token in the sentence, then build a classifier to classify it as passive or active based on conventional grammatical rules,...
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1answer
26 views

What is the theoretical differences of Multitask learning vs Fine tuning based transfer learning?

Suppose, I have the following scenarios. I have a bunch of fruits, i.e., apple, orange, and banana. I simply made a Multitask model, where my network first tell me which fruit it is, and then telling ...
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12 views

TF classes missing in huggingface's transformers

I've downloaded huggingface's transformers module, but I don't see any of the classes with the "TF" prefixes listed in the documentation such as "TFRobertaForSequenceClassification"...
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1answer
39 views

deep learning and uncertainty estimation

Recently I got very interested in NLP applications of deep learning. Diving into literature (on arXiv for instance) I noticed that is very unpopular to quote and estimate uncertainties on scores of ML ...
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1answer
26 views

Unusually High BLEU score on a NMT model

This is the project on Neural Machine Translation on the English/Irish language pair. I have been spending the past month or so trying to train a good baseline to do 'experimentation' on. I have a ...
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1answer
16 views

Text Classification on a very small data set with a lot of classes

I have a data set consisting of 455 rows spread over 21 different classes. The data set is imbalanced as well as you can see below. ...
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1answer
19 views

Using BM25 to rank words

How effective is it to use BM25 to rank words, to be more specific i have a dictionary of words and i want to rank only words in a document that are also in my dictionary. I want to rank all words in ...
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0answers
18 views

Is there an existing way to find out if there is an association between two words in a sentence using NLP

So, I was working on word2vec , playing with it and although it's a great tool for finding similarities between two words,But when we come to finding a semantic association between two words it cannot ...
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13 views

Is there a way to find difference between topics in two languages using nlp

I want to analyse queries and their differences between two different languages English and Spanish in this case. I'm aware about topic modelling. I'm in search of any corpus available or any ...
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10 views

Looking for a web based tool for natural language to PostgreSQL query for custom database

I need to build a web-based solution that will take a natural language query in English, convert it into an SQL query that I need to execute on a PostgreSQL database. I tried ln2sql/nl2sql but it only ...
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20 views

How to group news by topics when they do not have labels

I have a dataset that includes 20000 news titles and 9 columns that are not relevant (boolean values) for labelling them. I have tried to extract top words using tokenizetion and tf-idf for n-grams. ...
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2answers
45 views

Where can I get Insurance claim data for practicing NLP(Natural Language) processing?

I am looking for specifically Insurance dataset for practicing Machine Learning & NLP, but unable to find much in kaggle, udemy or other websites. Is there a way to get that dataset or any website ...
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1answer
15 views

in NLP academic paper, would it be okay to refer the “token embeddings” as “tokens”?

I am writing a paper in Natural Language Processing (NLP), and I just have a quick question about terminology. In language models like Transformers, "token" refers to individual word in a text ...
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2answers
27 views

Machine Learning - Input Prepocessing - NLP email classification model

So I created a model which classifies emails into different categories, just like a spam filter. I deployed the model as a webservice, no problem with that but I can’t get my head around how I would ...
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What's the general procedure to include conversational context in binary classification using BERT?

My data set looks something like this: Post | Comment1 | Comment2 | Comment3 | label [0 or 1] Where the 'label' indicates whether 'Comment3' is positive (1) or negative (0) So currently, my input ...
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1answer
12 views

approach for multi label text classification

I want to make a classifier that will label each text in a corpus with the correct label(s). I can go straight to ML using sklearn multi-label text classification, or even to DL using LSTM. But is it ...
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1answer
23 views

How to detect medicine name from the medicine wrapper

I have got medicine wrapper ( Packaging ) of different medicines. I want to detect medicine name out of it. I'm using Google Cloud Vision to extract all the text from the medicine wrapper. Text ...
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6 views

Multi-documents text annotation tool?

I have been looking for a good annotation tool/interface for text annotation. My requirements are as follows: Display of multiple short-text social media posts on the same page, as one task; ...
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44 views

BPE vs WordPiece Tokenization - when to use / which?

What's the general tradeoff between choosing BPE vs WordPiece Tokenization? When is one preferable to the other? Are there any differences in model performance between the two? I'm looking for a ...

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