Natural Language Processing (NLP) is an important bridge in the gap between digital data and human communication. NLP is likely to be used for years, in order to help with several different things.
Machine Translation: As more information is available online, the tasks of making that data accessible becomes even more important. In order to get the information accessible to everyone and deal with all the langue barriers, the information needs to be translated. However, there is just too much information for this to be done by humans. Some companies are looking at ways to recruit large amounts of people to assist with translating, but machine translation will offer a more scalable approach to getting the world’s information out to the masses. The challenge with machine translation is not just translating the words, but also preserving the meaning of the sentences. This is one of the complex issues that are at the heart of NLP.
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Combating Spam: Spam filters are an important first line of defense against the increasing problem of unwanted emails. Almost everyone who uses email regularly has experienced the trouble over unwanted emails that are still received or important emails that are flagged and caught in the filter by accident. The issues that come with spam filtering can be helped with NLP. It boils down to the challenge of extracting meaning from a string of text. The Bayesian spam filter technology is promising and it’s a statistical technique where the incidences of words found in an email are measured against the typical occurrence of the words in non-spam and spam emails.
Extraction of Information: Leaders are making important decisions, especially in financial markets, by moving away from human control and oversight. Instead, algorithmic trading has become more popular and this is a form of financial investment that is controlled entirely by technology. Many of these decisions are being affected by the news, which is presented predominately in English. A task of Natural Language Processing has then been to take these announcements and extract the important information in a format that can be used with algorithmic trading decisions. An example can be news of a merger between two companies that has an impact on trading decisions.
Summarization: In the digital age, information overload can be a huge problem. Access to information is already exceeding the capacity to understand it. With machine translation and making data accessible to everyone, this trend isn’t going to slow down, which means there needs to be a way to summarize information. This helps users recognize and absorb relevant information. Another useful application of summarization is to understand the deeper emotional meanings, based on data from social media.
Answering Questions: Search engines give a wealth of information, but they are still primitive when it comes to answering questions that are posed by humans. Google has seen several problems that have caused frustration with the users who will need to try different searches in order to find the answer. Google has used NLP to recognize natural language questions, extract the meaning, and then provide the answer. While things are improving, this is still a focus and challenge of search engines to be able to utilize Natural Language Processing to the full extent.
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