natural-language-processing | Programming Assignments and Lectures for Stanford 's CS | Natural Language Processing library
kandi X-RAY | natural-language-processing Summary
kandi X-RAY | natural-language-processing Summary
Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this winter quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some recent models involving a memory component. Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems.
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- Decorator to extract the phase of each token .
- Generate an array from a text file .
- Show solver options .
- Compute the cdist between two vectors .
- Analyze a group .
- Compute the distance between two vectors .
- Least squares solution to linear operator .
- Minimize a function .
- Perform LU decomposition .
- Pad a numpy array .
natural-language-processing Key Features
natural-language-processing Examples and Code Snippets
Community Discussions
Trending Discussions on natural-language-processing
QUESTION
i'm working on an nlp project and trying to follow this tutorial https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e and while executing this part
...ANSWER
Answered 2021-Mar-21 at 00:48Spacy did away with the span.merge() method since that tutorial was made. The way to do this now is by using doc.retokenize(): https://spacy.io/api/doc#retokenize. I implemented it for your scrub function below:
QUESTION
I have found this code here:
...ANSWER
Answered 2020-Sep-28 at 07:28In the example you found the idea is to use the conventional names for syntactic constituent elements of sentences to create a chunker - a parser that breaks down sentences to a desired level of rather coarse-grained pieces. This simple(istic?) approach is used in favour of a full syntactic parse - which would require breaking the utterances down to word-level and labelling each word with appropriate function in the sentence.
The grammar defined in the parameter of RegexParser is to be chosen arbitrarily depending on the need (and structure of the utterances it is to apply to). These rules can be recurrent - they correspond to the ones of BNF formal grammar. Your observation is then valid - the last rule for VP refers to the previously defined rules.
QUESTION
I have a Django application where system on click of a button calls an API. API returns data in a complex structure consisting of a list of items with further nested jsons:
...ANSWER
Answered 2020-Aug-30 at 19:28Convert your source code into the following one. Here I have created a staticmethod named as map_and_save which will support you to map and save the data based on the given JSON data format. You can call this method from your view class.
QUESTION
As some background, I've been looking more and more into NLP and text-processing lately. I am much more familiar with Computer Vision. I understand the idea of Tokenization completely.
My confusion stems from the various implementations of the Tokenizer class that can be found within the Tensorflow ecosystem.
There is a Tokenizer class found within Tensorflow Datasets (tfds) as well as one found within Tensorflow proper: tfds.features.text.Tokenizer() & tf.keras.preprocessing.text.Tokenizer() respectively.
I looked into the source code (linked below) but was unable to glean any useful insights
tfdsimplementationtfimplementation... line18links to the next link- text data summarization function
The tl;dr question here is: Which library do you use for what? And what are the benefits of one library over the other?
NOTE
I was following along with the Tensorflow In Practice Specialization as well as this tutorial. The TF in Practice Specialization uses the tf.Keras.preprocessing.text.Tokenizer() implementation and the text loading tutorial uses tfds.features.text.Tokenizer()
ANSWER
Answered 2020-May-18 at 16:38There are many packages that have started to provide their own APIs to do the text preprocessing, however, each one has its own subtle differences.
tf.keras.preprocessing.text.Tokenizer() is implemented by Keras and is supported by Tensorflow as a high-level API.
tfds.features.text.Tokenizer() is developed and maintained by tensorflow itself.
Both have its own way of doing encoding the tokens. Which you can make out with the example below.
QUESTION
I am running the following code...
...ANSWER
Answered 2018-Mar-04 at 00:39read.csv is looking for the file names in your working directory. By changing your working directory to "C:/Users/Bob/Documents/R/natural-language-processing/class-notes", your code should work just fine.
Code:
QUESTION
I am trying to understand the math behind the TfidfVectorizer. I used this tutorial, but my code is a little bit changed:
what also says at the end that The values differ slightly because sklearn uses a smoothed version idf and various other little optimizations.
I want to be able to use TfidfVectorizer but also calculate the same simple sample by my hand.
Here is my whole code: import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_extraction.text import TfidfVectorizer
...ANSWER
Answered 2019-Oct-21 at 04:06Here is my improvisation of your code to reproduce TfidfVectorizer output for your data .
QUESTION
I am new in NLP domain and was going through this blog: https://blog.goodaudience.com/learn-natural-language-processing-from-scratch-7893314725ff
London is the capital of and largest city in England and the United Kingdom. Standing on the River Thames in the south-east of England, at the head of its 50-mile (80 km) estuary leading to the North Sea, London has been a major settlement for two millennia. It was founded by the Romans.
I have the experience in NER and POS tagging using spacy. I would like to know that how i will link the london with it like:
London is the capital .....
It has been a major settlement..
It was founded by the Romans....
I have tried the Dependency parser but not able to produce the same result. https://explosion.ai/demos/displacy
I am open to use any other library, please suggest the right approach to achieve it
...ANSWER
Answered 2019-Sep-01 at 14:29The problem which you are looking to solve is called Coreference resolution .
The dependency parser is generally not the right tool to solve it.
Spacy has a dedicated module called neuralcoref. Have a look at this page too on coreference resolution with Spacy
An example:
QUESTION
I am working on a text classification project, and I would like to use keras to rank the importance of each word (token). My intuition is that I should be able to sort weights from the Keras model to rank the words.
Possibly I am having a simple issue using argsort or tf.math.top_k.
The complete code is from Packt
I start by using sklearn to compute TF-IDF using the 10,000 most frequent words.
ANSWER
Answered 2019-May-24 at 11:34i think it is not possible first layer outputs 1000 value each value binded with each feature with some weight value and same thing continues to end of network
if input directly binded classification layer and if it is trained then
QUESTION
So I am sort of an amateur when comes to machine learning and I am trying to program the Baum Welch algorithm, which is a derivation of the EM algorithm for Hidden Markov Models. Inside my program I am testing for convergence using the probability of each observation sequence in the new model and then terminating once the new model is less than or equal to the old model. However, when I run the algorithm it seems to converge somewhat and gives results that are far better than random but when converging it goes down on the last iteration. Is this a sign of a bug or am I doing something wrong?
It seems to me that I should have been using the summation of the log of each observation's probability for the comparison instead since it seems like the function I am maximizing. However, the paper I read said to use the log of the sum of probabilities(which I am pretty sure is the same as the sum of the probabilities) of the observations(https://www.cs.utah.edu/~piyush/teaching/EM_algorithm.pdf).
I fixed this on another project where I implemented backpropogation with feed-forward neural nets by implementing a for loop with pre-set number of epochs instead of a while loop with a condition for the new iteration to be strictly greater than but I am wondering if this is a bad practice.
My code is at https://github.com/icantrell/Natural-Language-Processing inside the nlp.py file.
Any advice would be appreciated. Thank You.
...ANSWER
Answered 2018-Jan-19 at 05:58For EM iterations, or any other iteration proved to be non-decreasing, you should be seeing increases until the size of increases becomes small compared with floating point error, at which time floating point errors violate the assumptions in the proof, and you may see not only a failure to increase, but a very small decrease - but this should only be very small.
One good way to check these sorts of probability based calculations is to create a small test problem where the right answer is glaringly obvious - so obvious that you can see whether the answers from the code under test are obviously correct at all.
It might be worth comparing the paper you reference with https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm#Proof_of_correctness. I think equations such as (11) and (12) are not intended for you to actually calculate, but as arguments to motivate and prove the final result. I think the equation corresponding to the traditional EM step, which you do calculate, is equation (15) which says that you change the parameters at each step to increase the expected log-likelihood, which is the expectation under the distribution of hidden states calculated according to the old parameters, which is the standard EM step. In fact, turning over I see this is stated explicitly at the top of P 8.
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Install natural-language-processing
You can use natural-language-processing like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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