Processing a huge amount of text data is always busy and time consuming. Therefore, many companies and organizations rely on data decryption technologies to automate manual work with intelligent algorithms. Acquiring information can reduce human labor, reduce costs, and make the process less error-prone and more efficient.
This article discusses data mining algorithms for unbuilt data using OCR, Deep Learning, and NLP techniques. It also covers use cases, challenges, and discusses how to determine the NLP workflows for data acquisition for your company.
Data extraction is the unstructured unstructured data and the extraction of relevant data into more editable and structured data formats.
For example, consider reviewing a company’s financial information from a few documents. Usually, we look for some of the required information when the information is digital, or review it manually. But with data mining NLP algorithms, we can automate the extraction of all required information, such as spreadsheets, company growth data, and other financial details, from different types of documents (PDF, Docs, Images, etc.).
Below is a screenshot that explains how we can collect information about your invoice.
Data Extract from textual data can be achieved by utilizing in-depth learning and NLP techniques such as Named Entity Recognition. However, if we build one from scratch, we need to decide the algorithm taking into account the type of data we are working on, such as invoices, medical reports, etc. This is to ensure that the model is specific to a particular use case. We will learn more about this in the following sections.
To understand the mechanics of data mining NLP algorithms, we need to understand what types of data we are working on. This helps us figure out the data we want to extract from the unbuilt data. For example, for invoice-related information, the algorithm should understand the invoice items, company name, billing address, etc. When processing medical reports, it should identify and extract patient names, medication data, and other general reports.
Once the data is curated, we begin to process the data decompression NLP techniques and build the data models. Below are some of the most common techniques that are often used.
Computers usually do not understand the language we speak or communicate. Therefore, we break down the language, basically words and phrases, into symbols and then load it into the program. Dividing a language into symbols is called tokenization.
For example, consider a simple sentence: “Extracting NLP data is fun.” This can be identified as follows:
- One word (sometimes called unigram): NLP, data, extraction, is fun
- Two-word sentence (bigram tokens): NLP information, data extraction, extraction is fun, fun NLP
- Three-word sentences (trigram characters): NLP data extraction, data extraction is, extraction is fun
import spacy nlp = spacy.load("en_core_web_sm") doc = nlp("Apple is looking at buying U.K. startup for $1 billion") for token in doc: print(token.text)
Apple is looking at buying U.K. startup for $ 1 billion
Parts of a speech note
Marking parts of the speech is very important to extract information from the text. It helps us understand the context of text data. We usually refer to the text of documents as “ unstructured data ” – data that does not have a defined structure or model. Thus, POS tags allow us to use techniques that provide context for the words or symbols used to classify them in certain ways.
In the speech tag sections, all text data tags are classified into different word categories, such as nouns, verbs, adjectives, prepositions, determiners, etc. This additional word-related information allows for further processing and analysis, such as opinion analytics, lemization, or reports where we can take a closer look at a particular word category.
Here is a simple piece of python code that uses a space that returns the speech parts of a particular sentence.
import spacy NLP = spacy.load("en_core_web_sm") doc = NLP("Apple is looking at buying U.K. startup for $1 billion") for token in doc: print(token.text, token.pos_)
Apple PROPN is AUX looking VERB at ADP buying VERB U.K. PROPN startup NOUN for ADP $ SYM 1 NUM billion NUM
Dependency charts help us find relationships between neighboring words using directional charts. This relationship provides information about the type of dependency (e.g., subject, object, etc.). The following is a figure that illustrates the dependency diagram of a short sentence. An arrow directed at a word faster indicates it faster edit mobileand etiquette “advmod` the arrow shown depicts the exact nature of the dependence.
Similarly, we can build our own dependency diagrams using frameworks such as nltk and spacy. Below is an example:
import spacy from spacy import displacy NLP = spacy.load("en_core_web_sm") doc = NLP("This is a sentence.") displacy.serve(doc, style="dep")
With NER Space
Spacy is an open source NLP library for advanced natural language handling in Python and Cython. It is well maintained and has over 20,000 stars in Github. NER models are widely used to extract data using the spacy function.
Make sure you install the latest version of python3, pip and space. In addition, we need to download pre-trained spacy kernels to use them directly in our programs. Use a suffix or command prompt and type the following command after installing spacy:
python -m spacy download en_core_web_sm
# import spacy import spacy # load spacy model NLP = spacy.load('en_core_web_sm') # load data sentence = "Apple is looking at buying U.K. startup for $1 billion" doc = NLP(sentence) # print entities for ent in doc.ents: print(ent.text, ent.start_char, ent.end_char, ent.label_)
Apple 0 5 ORG U.K. 27 31 GPE $1 billion 44 54 MONEY
We’ve loaded a simple sentence into this and applied NER with Space, and it works like magic. Let’s decode the program now.
First, we have brought a spacy module into the program. Next, we load the spacy model into a variable called NLP. Next, we load the data with the template specified in the template and store it in the doc variable. Now we iterate over the doc variable to search for entities and then print the word, its start, end characters and the entity to which it belongs.
This is a simple example: if we want to try this in real large data sets, we can use medium and large models as standard.
NLP = spacy.load('en_core_web_md') NLP = spacy.load('en_core_web_lg')
These work very accurately to identify some common entities such as names, location, organization, etc. The next section looks at some business applications where NER is extremely necessary!
Many industries process a lot of documents on a daily basis and rely on manual work. These include finance, medical chains, transportation and construction. Using NLP data extraction techniques in documents allows teams to find, edit, and analyze important events and details in business processes.
The following is a detailed example of how data extraction from text can be done in general for all types of documents.
# 1 Data Collection
First, we need to gather data from different sources to build a data mining model. Usually we see documents in emails, cloud drives, scanned copies, computer software, and many other business sources. Therefore, we need to write different scripts to collect and store data in one place. This is usually done either by using web application interfaces or by building Robotic Process Automation (RPA) pipes.
# 2 Process data
Once we have collected the data, the next step is to process it. There are usually two types of documents: electronically generated (editable) and other non-electronically generated (scanned documents). For electronically generated documents, we can send them directly to the pre-processing pipeline. Still, we need OCR to first read all the information from the images and then send it to the preprocessing tubes for scanned copies. We can use either open source tools like Tesseract, or any online service like Nanonets or Textract. Once all the data is in editable or electronic format, we can then apply preprocessing steps such as Tokenization and POS entries, and then use data loaders to load the data into NLP data decryption models.
# 3 Choosing the right model
As discussed in previous sections, the choice of an appropriate model depends largely on the type of information we are working on. Today, there are several top models we can rely on. Below are some commonly used open source templates:
- Recognition of a designated entity in CoNLL 2003 (English)
- Extraction of key information from documents: evaluation and generator
- Deep Reader: Extracting information from document images through relationship breakdown and natural language
These are some of the data mining models. However, they are trained for a specific set of data. If we take advantage of these in our models, we need to experiment with hyperparameters and fine-tune the model accordingly.
Another way is to use pre-trained models and fine-tune them based on our data. In particular, BERT models are widely used to extract data from text. To learn more about these, read our blog post here.
# 4 Model evaluation
We estimate that the training process is crucial before using the models in production. This is usually done by creating by testing the data and find the most important metrics:
- Accuracy: the ratio of correct predictions according to the size of the test results.
- Accuracy: total ratio of true positives to predicted positives.
- Recall the ratio of true positives to true positives.
- F1 points: harmonic mean of accuracy and recovery.
Different metrics are a priority when considering different use cases. When processing an invoice, we know that an increase in the number or absence of an item can cause losses for the company. This means that in addition to good accuracy, we must also ensure false positive data in money-related fields are minimum requirements, so achieving high accuracy may be ideal. We must also ensure this details such as invoice numbers and dates are always picked because they are needed for legal and for compliance. A. Maintenance high reminder the value of these fields may take precedence.
# 5 Introduction of the model in production
The full potential of NLP models is known only when they are introduced into production. Today, because the world is completely digital, these models are stored on cloud servers with a suitable background. In most cases, Python is used as its more convenient programming language for text data and machine learning. The model is exported as either an API or SDK (software development package) to integrate with enterprise tools. However, we don’t have to build everything from scratch because there are several tools and online services for such use cases. For example, Nanonets has a highly accurate, fully trained NLP model for billing data extraction, and you can integrate our applications directly using APIs or supported SDKs.
Ideally, these are the steps required to extract information from text data. Here is an example of how Nanonets works on an ID card:
There are several applications for data mining, especially with large corporations and corporations. However, we can perform IE tasks as you work with major text sources such as emails, datasets, invoices, reports, and more. Here are some applications:
- Invoice Automation: Automates the extraction of billing information.
- Healthcare Systems: Manage patient records by identifying patient records and their prescriptions.
- KYC Automation: Automate the KYC process by extracting ethical information from customer IDs.
- Economic Research: Extract import data from financial documents. (Taxes, growth, quarterly income, profit / loss)
In this tutorial, we have learned about data extraction techniques from text data using several NLP-based methods. Next, we have seen how NER is crucial in obtaining information, especially when working with a wide variety of documents. Next, we’ve learned how companies can create workflows to automate the data collection process using a real-time example.