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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Synonymous word suggestion Model [closed]

I want create a machine Learning model which will suggest synonyms of the words of a sentence which can adapt to user usage. I am targeting Continuous Learning Model for this purpose. I have already ...
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Learning words embedding for bigrams and unigrams in a corpus

I am working on a topic modeling for tweets projects. I have generated my topics using both unigrams and bigrams. Topics are defined with a mixture of both bigrams and unigrams. Now I am planning to ...
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Is it possible to use unlabeled text articles for summarization when fine tuning BERT?

I know that unlabeled data could be used in pre-training but if I want to do a fine tuning of unlabeled articles for summarization, is it mandatory that the articles are labeled with existing ...
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how to programmatically introduce grammatical errors in sentences

I've a set of sentences in English language. I'm exploring ways to create a dataset of sentences with grammatical errors programmatically. The following options has been tried out randomly - identify ...
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1answer
13 views

How to handle like meaning sentences when working on text summarization

Suppose we have a text like Today is a very bad day. Very bad day is today. I wont come to play. What kind of technique should I use to summarize similar texts like ...
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132 views

Converting paragraphs into sentences

I'm looking for ways to extract sentences from paragraphs of text containing different types of punctuations and all. I used SpaCy's ...
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How to correctly lemmatize the text column in R?

I'm working on a project in Natural Language Processing. I have a data frame that has a text column. I have to lemmatize that text column. I'm using lemmatize_strings() function in R. However, there's ...
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Is it acceptable to append information to word embeddings?

Let's say I have my 300 dimensional word embedding trained with Word2Vec and it contains 10,000 word vectors. I have additional data on the 10,000 words in the form of a vector (10,000x1), containing ...
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10 views

auto updating text comparison model

I have a need to create a model that compares and groups distinct snippets of text based on keywords. I can extract similar keywords with NLP methods and simply comparing sentence text. I want these ...
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Word2Vec: Why do some dimensions of an embedding have an interpretation, and why does addition/subtraction of embedding vectors work?

I'm reading about Word2Vec from this source: http://jalammar.github.io/illustrated-word2vec/. Below is the heatmap of the embeddings for various words. In the source, it's claimed that we can get an ...
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2answers
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Is NLP suitable for my legal contract parsing problem?

My company has a product that involves the extraction of a variety of fields from legal contract PDFs. The current approach is very time consuming and messy, and I am exploring if NLP is a suitable ...
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Bag-of-words and Spam classifiers

I implemented a spam classifier using Bernoulli Naive Bayes, Logistic Regression, and SVM. Algorithms are trained on the entire Enron spam emails dataset using the Bag-of-words (BoW) approach. ...
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23 views

How to add time as a feature into word embeddings?

I have a text corpus and I'm using TfidfVectorizer. Would it be possible to cluster the resultant matrix once I concatenate tf-idf vector and time feature matrix I built([year, month, day])? I'm also ...
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Projection layer function

I am trying to understand word-vectors and was reading this paper https://arxiv.org/pdf/1301.3781.pdf This paper proposes CBOW architecture which uses projection layer. What is projection layer? I ...
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What are the different ways to feature engineer webpage data for input into a webpage classification model?

Looking for resources on the different ways that one can manipulate webpage data to input as features into a neural net. I'm aware of a service called diffbot that claims to use a CV based method to &...
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1answer
26 views

Optimal input setup for character-level text classification RNN

I want to classify 500-character long text samples as to whether they look like natural language using a character-level RNN. I'm unsure as to the best way to feed the input to the RNN. Here are two ...
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8 views

Research on Product Search Machine Learning @ Amazon

I am looking for any sort of research paper/ reference where we can read more about machine learning principles using for the amazon's main website. Mainly interested in how they do the product search ...
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1answer
24 views

Working Behavior of BERT vs Transformers vs Self-Attention+LSTM vs Attention+LSTM on the scientific STEM data classification task?

So I just used BERT pre-trained with Focal Loss to classify Physics, Chemistry, Biology and Mathematics and got a good f-1 macro of 0.91. It is good given it only had to look for the tokens like ...
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13 views

Converting a string to a recommendation type string

I am trying to build a recommendation system and some of the labels are ...
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1answer
31 views

From where does BERT get the tokens it predicts?

When BERT is used for masked language modeling, it masks a token and then tries to predict it. What are the candidate tokens BERT can choose from? Does it just predict an integer (like a regression ...
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13 views

How one can match similar sounding features of two items

I am trying to build simple app which will compare two products from provideid URLs based on their features. We will be showing the products in tabular form with columns being each product and rows ...
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1answer
23 views

Bug in sentiment analysis and classification for unlabeled text [closed]

I'm working on the transcript of Trump and Biden's debate and want to analyze the sentences and classify negative, positive, or neutral comments, but I ran into one problem. I used both TextBlob and ...
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1answer
38 views

Why BERT tokenizers function differently?

While experimenting with transformers' TFBertForSequenceClassification and BertTokenizer, I noticed that BertTokenizer: ...
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18 views

Question about Relative-Position-Representation code

In https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/layers/common_attention.py In _relative_attention_inner method, which I think is one of the ...
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2answers
52 views

Evaluating Language Model on specific topic

I have finetuned a pretrained Language Model(GPT-2) on a custom dataset of mine. I would like a way of evaluating the ability of my model to generate sentences of a specific predefined topic, given in ...
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1answer
41 views

Ordering of standardization, pca, and/or tfidf for neural network

I have 60k rows of text data. I have tokenized it into 55k columns. I am using a neural network to classify the data but have some questions about how to order my preprocessing steps. I have too much ...
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20 views

How to process list type questions in Question Answering task [closed]

How to generate question-answer-context triplets for questions with multiple answer strings? How to measure performance for it? For a question with one single answer, we generate one question-answer-...
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1answer
47 views

What's the best way to detect bible verse mentions in a text?

I have a set of 10 verses from the Bible in English. I want to detect the occurrence of any of these verses in a text. What would be the best way to go about doing this? Note that verses of the Bible ...
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1answer
78 views

Question about BERT embeddings with high cosine similarity

Under what circumstances would BERT assign two occurrences of the same word similar embeddings? If those occurrences are contained within similar syntactic relations with their co-occurrents?
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Trying to use CBOW for tweet classification

I'm trying to use the Continuous Bag Of Words method for word embedding on a corpus of 7503 tweets. In particular, I'm trying to use CBOW on this Kaggle competition, which involves classifying tweets ...
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1answer
26 views

I want to start studying the field of machine translation [closed]

I've studied Japanese language and literature and passed some linguistic courses and now as for my masters, I want to study natural language processing and especially machine translation. so I tried ...
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1answer
31 views

How to handle Tokenized text content which is given in number?

i have one data set of customer review, but the text data is given is tokenized text number. I am unable to proceed thinking about how to proceed? As I am encountering such data set the first time, so ...
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1answer
26 views

Is it possible to classify documents of corpus using labels?

I have a corpus of 23000 documents that need to be classified into 5 different categories. I do not have any labeled data available to me, just freeform text documents and labels(yes, one-word labels, ...
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2answers
98 views

How is the Gaussian noise given to this BLSTM based GAN?

In a conditional GAN, we give a random noise along with a label to the generator as input. In this paper, I don't understand why in one section of the paper, they say they are giving the random noise ...
2
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1answer
23 views

Word representation that gives more weight to terms frequent in corpus?

The tf-idf discounts the words that appear in a lot of documents in the corpus. I am constructing an anomaly detection text classification algorithm that is trained only on valid documents. Later I ...
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1answer
34 views

State-of-the-art Python packages that can evaluate language similarity

I am trying to evaluate the likelihood of generating a specific sentence out of a large set of sentences. To do this, I start from a simple approach: training a custom n-gram language model and ...
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3answers
93 views

NLP SBert (Bert) for answer comparison STS

I've been researching a good way to automate short answer evaluation. Essentially a teacher gives a test with some questions like: Question: why did columbus sail westward to find asia? Answer: so he ...
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1answer
28 views

Is adding the embedded words of a sentence to represent the sentence a good approach?

I have a dataset of sentences in a non english language like : word1 word2 word3 word62 word5 word1 word2 Now i want to turn each variable length sentence to a fixed size vector to give it to my ...
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1answer
48 views

Based on transformer, how to improve the text generation results?

If I do not pretrain the text generation model like BART, how to improve the result based on transformer like tensor2tensor? What are the improvement ideas for transformer in text generation task?
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26 views

Are there any deep models that are better than BERT-CRF in NER task?

Named entity recognition (NER) is task that mark tags of the input text sequence. BERT-CRF is a good NER model. I want to find a better NER model. Or I want to improve the BERT-CRF model. What can I ...
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1answer
31 views

best approach to embed random length sequences of words as a fixed size vector without having a maximum length? [closed]

I have a dataset of sentences in a non-English language like: word1 word2 word3 word62 word5 word1 word2 and the length of each sentence is not fixed. Now, I want to represent each sentence as a ...
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1answer
38 views

Using BERT for co-reference resolving, what's the loss function?

I'm working my way around using BERT for co-reference resolving. I'm following this highly-cited paper BERT for Coreference Resolution: Baselines and Analysis (https://arxiv.org/pdf/1908.09091.pdf). I ...
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2answers
49 views

Should I keep common stop-words when preprocessing for word embedding?

If I want to construct a word embedding by predicting a target word given context words, is it better to remove stop words or keep them? the quick brown fox jumped over the lazy dog or quick brown ...
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30 views

How to insert sentences into training data which has 2 words, 3 words 4 and 5 etc into training data?

I have a set of sentences which each contain 2 words, 3 words, 4 words, 5 words etc. When I am trying to give the training data only the first two words in a sentence it is not accepting it. It is ...
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1answer
68 views

Prediction using words which were not in training in a CNN with pre-trained word embeddings

In sentence classification using pre-trained embeddings(fasttext) in a CNN, how does the CNN predict the category of a sentence when the words were not in the training set? I think the trained model ...
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1answer
104 views

I tried loading my saved .h5 model and predicting with that model, i'm getting error list index out of range

I saved my keras model into .h5 format. Again I've loaded that .h5 file into my colab and tried to predict with that model. ...
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1answer
31 views

how to train custom word2vec embeddings to find related articles?

I am beginner in machine learning. My project is to make search engine based on AI which shows related articles when we search on website. For this i decided to train my own embedding. I found two ...
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23 views

Semantic networks and conceptual graphs

I would like to use semantic networks to understand changes in texts. For example when I add/remove some words within a text. ...
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1answer
35 views

How can I compare the grammatical complexity between two texts using their sentences dependency length?

This is a continuation to the following thread. I have two texts, common English texts such as news articles and informative texts versus a technical textbook. I want to compare the grammatical ...

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