The ability for humans to speak to each other has existed for millenniums. It has been essential in the advancement of the world. Societal norms, communal morals and intellectual progress would not be possible without it. But we have reached a point where humans need to move beyond just communicating with one another. We now need to be able to communicate with computers. And while humans have the ability to understand computer language, the interaction must be both ways, meaning that humans need to enable computers to glean value from natural human language. This is where Natural Language Processing comes in.
1. The History
Natural Language Processing began to pick up speed in the 1950s. Automatic translation, human-like interaction ability, conceptual ontologies, and other hand-written rule-based NLP systems were developed. In the 1980s, when Machine Learning began to be developed, language processing algorithms were created. These used ‘if-then’ and statistical models to produce more reliable results. In recent years, these statistical models have been supplemented with both semi-supervised and unsupervised learning algorithms, allowing the computer to learn from a combination of hand annotated and non-annotated data. The move away from manual entry and search is widespread, which is why companies like IntouchCheck offer such high value.
2. How It’s Different
As opposed to word processing software, NLP doesn’t regard a sentence as just a sequence of symbols. It understands that language has a hierarchical structure–words make up phrases, phrases make up sentences and sentences have meaning that can be summarized, categorized, and responded to.
3. Where It’s Going
But NLP isn’t just being developed so humans and computers can have interesting conversations. The end goal is for NLP to enable computers to usefully analyze, understand and derive applicable meaning from human language. When this is accomplished, computers can segment topics, recognize speech, analyze sentiment, extract relationships, recognize named entities, translate, and summarize automatically, among other things. What if companies could eventually partner with an agency like Boomsourcing, and then record and analyze these customer conversations? The value that could be gleaned is nearly infinite.
4. Who’s Using It
The greater applicability of Natural Language Processing has long been embraced by companies such as IBM, Microsoft and Apple. Google’s predictive search answers are Natural Language Processing. Siri’s responses to inquiries is Natural Language Processing. But NPL is now spreading into just about every other industry.
NLP Information extraction stores information in a database that is searched for and extracted from documents and recordings of unstructured data. This ability is especially useful to IP attorneys, energy industry firms, derivatives traders and contract management departments.
Contract management departments can use NLP to search for and find keywords in contracts, which then allows them to develop summary reports that compare the contractual and standard terms (i.e. information like dollar amounts and dates to guide risk mitigation, budgeting and planning). Derivatives traders can use NLP to analyze their contracts; extract information such as termination dates and interest rates; use the information to support compliance and regulatory requirements, manage collateral and guide trading decisions. Lawyers in the energy industry face months of work when trying to identify and summarize the conveyances and encumbrances in the title abstraction process for clients with multiple parcels of gas, oil and land. This can be reduced to weeks through NLP information extraction. Finally, IP consultants can implement NLP when extracting information (i.e. patents, outcomes and parties) from public court records, and then use it to create summary reports that they can use to craft their IP strategy.
NLP speech recognition is the ability to convert spoken information into text, and question answering is the ability to understand a question and provide the correct answer by searching through a set of documents. While NLP in this area is still being developed to be more intelligent, and eventually allow professionals to utilize it to reason about cases or contracts, research topics, and create predictions about possible outcomes, the current statistical NLP is providing significant value through converting interviews into text and providing statistical answers to questions. This means less time is being used to transcribe or research information, and more time is being invested in building valuable analyses off of transcriptions and accurate, efficient answers. VoiceBase is just one company that has started offering this service to others.
NLP information retrieval is the ability of an NLP system to find relevant information for a query, even when the query isn’t clearly articulated. This is most often applied in e-discovery–it assists firms in searching relevant documents in the discovery process. It not only helps to identify the relevant documents, but also the relevant information within those documents.
5. Varying Degrees Of Complexity
Research and manual language processing is intellectually challenging and tedious. It requires professionals to put in long hours and have the ability to think on their feet. In some cases, though, there are ways to minimize tedious tasks and avoid human error. Natural Language Processing is one of those ways. professionals in a variety of fields can integrate NLP into their work to allow them to serve clients, customers and the general public more efficiently and effectively. They can get the right information at the right time. NLP is no longer solely for the financial and tech industries, it has spread to every industry and aspect of life.
Some NLP applications are simple, allowing experts to input keywords and phrases and coming up with all matches in the batch of documents. Other NLP applications are more complex, enabling users to search for concepts. In other words, if the professional is looking for any documents that relate to payments, the search will also look for related words, such as invoice, compensation, fee and more. There are even applications that can be trained. As individuals use the application, they mark results as relevant or irrelevant and the computer learns from this.