Questions tagged [ngrams]

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2
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1answer
39 views

NLP: find the best preposition for connecting parts of a sentence

My task is to connect 2-3 parts of the sentence into one whole using a preposition the first part is some kind of action. Ex. "take pictures" the second part is an object that can ...
1
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1answer
21 views

what is the training phase in N-gram model?

Following is my understanding of N gram model used in text prediction case : Given a sentence say, " I love my " (say N = 1 /bigram), using N gram and say 4 possible candidates ( country, ...
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0answers
81 views

Shouldn't ROUGE-1 precision be equal to BLEU with w=(1, 0, 0, 0) when brevity penalty is 1?

I am trying to evaluate a NLP model using BLEU and ROUGE. However, I am a bit confused about the difference between those scores. While I am aware that ROUGE is aimed at recall whilst BLEU measures ...
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0answers
36 views

Self Organising Map with variable length ordered sets of N-grams

I want to preface my question with the highlighted situation I have might not be applicable to kohonen self organising maps (SOM) due to a lack of understanding on my part so I do apologise if that is ...
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0answers
22 views

character level n-gram embedded LSTM is overfitting strong

I am working on a character level classification LSTM and I used uni-gram (hello -> h, e, l, l, o). So my vocab size was 28 (alphabet + " " + "-&...
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0answers
47 views

For an n-Gram model with n>2, do we need more context at end of each sentence?

Jurafsky's book says we need to add context to left and right of a sentence: Does this mean, for example, if we've a corpus of three sentences: ...
3
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1answer
111 views

In smoothing of n-gram model in NLP, why don't we consider start and end of sentence tokens?

When learning Add-1 smoothing, I found that somehow we're adding 1 to each word in our vocabulary but not considering start-of-sentence and end-of-sentence as two words in the vocabulary. Let me throw ...
5
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1answer
56 views

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 ...
2
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1answer
130 views

FastText Model Explained

I was reading the FastText paper and I have a few questions about the model used for classification. Since I am not from NLP background, some I am unfamiliar with the jargon. In the figure, what ...
0
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1answer
30 views

Why is n-grams language independent?

I don't understand how n-grams are language independent. I've read that by using character n-grams of a word than the word itself as dimensions of a vector space model, we can skip the language-...
1
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1answer
39 views

Is the search for a specific n-gram the same like a string search?

Is the result of a search for a specific n-gram like sherlock+holmes equal to the result of a regex search for "sherlock holmes" in the same document corpus? So if i read about n-grams for certain ...
0
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1answer
153 views

How to feed data for ngram model?

I want to train an ngram language model Let's say I have the following corpus: ...
2
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1answer
26 views

Artificially increasing frequency weight of word ending characters in word building

I have a database of letter pair bigrams. For example: ...
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0answers
37 views

Dealing with missing n-grams in Naive Bayes classifier

I am doing sentiment analysis on code-mixed text data, i.e English used interchangeably with another language. The dataset I currently have is very small in size, approx 3.5k samples. I am sure that ...
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0answers
101 views

Skip-thought models applied to phrases instead of sentences

My goal is to build a statistical model with domain specific phrase embeddings. To do this, I am doing research on how to build a model using skip-thought vectors, where instead of using sentence ...
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2answers
206 views

Discarding rare words when comparing texts - per text, per comparison, or per codex?

I'm trying to compare texts (read: books) using KL divergence of N-gram usage frequency. first I have to calculate the frequency of N-grams, and I see (perhaps unsurprisingly) that many of the words ...
2
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1answer
2k views
2
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1answer
3k views

How to improve Naive Bayes?

I am solving a problem that address this question "What are the Actions that lead to high or low score?" I have the following Data that consist of text and score , I want to derive the words or ...
1
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1answer
698 views

What affect will replacing words with bigrams have on TfIDF?

Say I have a corpus of text documents on which I have calculated each documents TfIDF vector. With this sparse matrix representation of the corpus, I can calculate similarities between documents by ...
1
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1answer
36 views

Predicting interactions [closed]

First off, I don't really know much about machine learning. In a virtual world, such as a video game like minecraft or an application like Google Street view, a user can navigate the world using the ...
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0answers
48 views

What does NIST information weights refer to?

NIST is a metric used to measure the goodness of translation. In the paper, Doddington (2002) introduce the notion of "Information weights" Information weights were computed using N-gram counts ...
6
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2answers
3k views

Clustering or classifing n-gram-based text categories

I have large set of data records looking like this: "text", "category" I extract n-grams from text (2-, 3- and 4-grams) and store count of each n-gram per ...
12
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1answer
395 views

ngram and RNN prediction rate wrt word index

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