When learning Add-1 smoothing, I found that somehow we are 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 give an example to explain. Example: Assume we have a corpus of three sentences: "John read Moby Dick", "Mary read a different book", and "She read a book by Cher". After training our bi-gram model on this corpus of three sentences, we need to evaluate the probability of …
I recently build an language model with N-gram model for text generation and for change I started exploring Neural Network for text generation. One thing I observed that the previous model results were better than the LSTM model even when both where built using same corpus.
How to keep only the top k-frequent ngrams in a text field with pandas? For example, I've a text column. For every row in it, I only want to keep those substrings that belong to the top k-frequent ngram in the list of ngrams built from the same columns with all rows. How should I implement it on a pandas dataframe?
I've got few questions about the application of bag-of-ngrams in feature engineering of texts: How to (or can we?) perform word2vec on bag-of-ngrams? As the feature space of bag of n-gram increases exponentially with 'N', what (or are there?) are commonly used together with bag-of-ngrams to increase computational and storage efficiency? Or in general, does bag of n-gram used alongside with other feature engineering techniques when it's involved in transforming a text fields into a field of text feature?
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 exactly is are the $x_i$? I am not sure what $N$ ngram features mean. If my document has total $L$ words, then how can I represent the entire document using $N$ variables ($x_1$,..,$x_n$)? What exactly is $N$? $$-\frac{1}{N}\sum_{n=1}^Ny_n\log(f(BAx_n)) $$ If $y_n$ is the label, …
I am trying to implement this formula in Python $$ \frac{\text{max}(c_{KN}(w^{i}_{i-n+1} - d), 0)}{c_{KN}(w^{i-1}_{i-n+1})} + \lambda(c_{KN}(w^{i-1}_{i-n+1})\mathbb{P}(c_{KN}(w_{i}|w^{i-1}_{i-n+2})$$ where $$ \mathrm{c_{KN}}(\cdot) = \begin{cases} \text{count}(\cdot) & \text{for the highest order } \\ % & is your "\tab"-like command (it's a tab alignment character) \text{continuationcount}(\cdot) & \text{otherwise.} \end{cases} $$ Following this link here I was able to understand how to implement the first half of the equation namely $$\frac{\text{max}(c_{KN}(w^{i}_{i-n+1} - d), 0)}{c_{KN}(w^{i-1}_{i-n+1})} $$ but the second half specifically at the moment the $\lambda(c_{KN}(w^{i-1}_{i-n+1})$ term …
I am wondering if there is a good example out there that compares N-Gram with various smoothing techniques. I found this notebook that applies Laplace transform but that is about it. Any suggestions are greatly appreciated.
Suppose we are building the following model to build a neural network over one-hot encoded vectors of characters: For a given dataset, it’s not reasonable to read the whole text! So, we take some characters of text, say 1014. Then we apply 1D convolutions + pooling 6 times and we use the following kernels width: o Kernels width: 7,7,3,3,3,3 o We will apply 1024 filters on the same data. Since we apply the same process six times, we will get …
I have a list of short strings each identifying a city. Misspellings are very common. The example below shows some of these short strings, along with the correct city they're supposed to match. string city amsterdam amsterdam asmterddam amsterdam amstterdm amsterdam new york new york new yrok new york nwe york new york neew york new york nw york new york I would like to train a classifier that takes the input string and then predict the most likely city …
Lets say I have a sentence "I need multiple ngrams". If I create bigrams using Tf idf vectorizer it will create bigrams only using consecutive words. i.e. I will get "I need", "need multiple", "multiple ngrams". How can I get "I mutiple", "I ngrams", "need ngrams"?
For a university project, I chose to do sentiment analysis on a Google Play store reviews dataset. I obtained decent results classifying the data using the bag of words (BOW) model and an ADALINE classifier. I would like to improve my model by incorporating bigrams relevant to the topic (Negative or Positive) in my features set. I found this paper which uses KL divergence to measure the relevance of unigrams/bigrams relative to a topic. The only problem is that I …
I am trying to categorize server software versions based on server responses to various slightly different hex messages. To extract the ML input parameters from these hex messages I suppose to use the n-grams method. Can you please advise on some other methods that can be used to identify ML input parameters from hex messages? Of course, I can do it manually but probably exists an automated solution. What tools/libraries better to use to apply the n-grams method to communication …
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 have tried tokenizing and sequencing my sentences, below is how they are prepared to be passed to the model: train_x = np.zeros([len(sequences), max_seq_len], dtype=np.int32) for i, sequence in enumerate(sequences[:-1]): #using all words except last for t, word in enumerate(sequence.split()): train_x[i, t] = word2idx(word) #storing in word …
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 consist of only one noun or a noun with adjectives and additions dependent on it. Ex. "juicy cherry pie", "squirrel" the third part is a place. Ex. "room", "London" To solve this task I've already tried some options such as generation using GPT-2 (or other …
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 needless context. However, one thing I wasn't expecting was that the prediction rate drops. In my understanding since we already have a context of 3 words, the plateau should converge asymptotically to its highest value. But both the recurrent network and the Ngram …
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, family, wife, school) I can estimate the conditional probability on each of the candidates and take the one with highest probability as the next word. Question : I understand the probability part of the model but to even get to the probability, …
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 precision, all ROUGE implementations I have come across also output precision and the F-score. The original ROUGE paper only briefly mentions precision and the F-score, therefore I am a bit unsure about what meaning they have to ROUGE. Is ROUGE mainly about recall …
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 the case. If this the case I would greatly appreciate any suggestions on alternative methods to compare the similarities for my given input data. I am trying to create a self organising map for the similarity comparison between the ngram ordered set …
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: "John read Moby Dick", "Mary read a different book", and "She read a book by Cher"; and after training our tri-gram model on this corpus of three sentences, we need to evaluate the probability of a sentence "John read a book", i.e. to find $P(John\; read\; a\; book)$ as below, $P(John\; read\; a\; …