I have a list of pdf files that have different numbers of pages and presentations.
Each file contains a list of information that I need to extract. but the problem is that the information is wrapped in different type of phrases and syntax.
I need to know if I need to build a machine learning to do this and if it is the case which algorithms and techniques are suited for my case.
NB: I have a huge dataset of pdf files to use to train model.
So if you want to do this in Python it seems that PyPDF2 is the way to go. You should be able to read in and extract the text data you want from the PDFs. Automate the boring stuff has examples of using PyPDF2.
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I have 30 different types of PDFs. I need to extract information specific to each PDF. I need to do it in python preferably. I am able to extract specific information from one type of pdf but need a model which will recognize the type of document and automatically identify the keywords which need to be extracted and then retrieve them. Is it possible programmatically using python? Any help will be appreciated.
Please note that not all the documents are structured. But for the start we can assume that the document is structured.
I have tried OpenCV for text extraction from scanned images but it is giving me horrible results. I have converted the whole images to text but that's not what I am looking for. I am just looking for specific information from each of the pdfs.
You need two things.
For the Keywords, you can use tf-idf
For the Topic extraction you can use document classification
tf.data.* has dataset classes. There is a TextLineDataset, but what I need is one for multiline text (between start/end tokens). Is there a way to use a different line-break delimiter for tf.data.TextLineDataset?
I am an experienced developer, but a python neophyte. I can read but my writing is limited. I am bending an existing Tensorflow NMT tutorial to my own dataset. Most TFRecord tutorials involve jpgs or other structured data.
You can try two options:
Write a generator and then use Dataset.from_generator: In your generator you can read your file line by line, append to your example while doing that and then yield when you encounter your custom delimiter.
First parse your file, create tf.train.SequenceExample with multiple lines and then store your dataset as a TFRecordDataset (more cumbersome option in my opinion)
I have to apply Machine Learning Algorithms on a dataset. But the problem that I've been facing is loading this dataset. I've tried nanoscope library but couldn't succeed. How should I proceed with this?
The dataset can be found here :
https://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/
These appear to just be plain text files, so you should be able to read them with open and read (Python docs), or any other appropriate tool for that matter (e.g. pandas.read_csv (link), csv module (link), the list goes on).
I am trying to develop a classifier for documents. I am relatively new to python and I am trying to figure out what the best/standard way of creating the storage structure. I am looking to introduce the dataset with machine learning algos.
I am ingesting txt files and I was thinking to have one column hold the entire document content and the second column hold the class(0-1) in my case. I initially tried creating a list of lists - such that list ["the skye is blue",1]["the sky is grey",1]["the sky is red",0].
I was also trying to create a pandas Dataframe because I thought its structure may be more suitable for data manipulation.
I was also trying to create a pandas Dataframe because I thought its structure may be more suitable for data manipulation.
I would go with that. Given that the goal is to build and train a classifier you will need to extract/compute some features from the text of the files. When you decide to do that the capability to easily generate and add new variables to a Dataframe will come in handy.
However, it also depends on the size of the data you will be crunching. If you will have massive data you should research different concepts and frameworks (for instance TensorFlow)
You can use python to automate things in SPSS or to shorten the way, but I need to know if it is possible to replace the SPSS Syntax with python for example to aggregate data in loops etc..
Or another example. I have 2 datesets with the follwing variables id, begin, end and type. It is
possible to put them into different arrays/lists and then compare the arrays/lists so that at the end i have a new table/dataset
with non matching entries and a dataset with the matching entries in SPSS.
My idea is to extend the context of matching files in SPSS.
Normally programming languages like python or php can handle this.
Excuse me. I hope someone will understand what I mean.
There are many ways to do this sort of thing with Python. The SPSS module Dataset class allows you to read and write the case data. The spssdata module provides a somewhat simpler way to do this. These are included when you install the Python Essentials. There are also utility modules available from the SPSS Community website. In particular, the extended Transforms module provides a standard lookup function and an interval-based lookup.
I'm not sure, though, that the standard MATCH FILES won't do what you need here. Mismatches will generate missing data in the variables, and you can select subsets based on that criterion.
This question explains several ways how to import an SPSS dataset in Python code: Importing SPSS dataset into Python
Afterwards, you can use the standard Python tools to analyze them.
Note: I've had some success with simply formatting the data in a text file. I can then use any diff tool to compare the files.
The advantage of this approach is that's usually very easy to write text exporters which sort the data to make it easier for the diff tool to see what is similar.
The drawback is that text only works for simple cases. When your data has a recursive structure, then text is not ideal. In that case, try an XML diff tool.