Loek Essers of Tech World recently wrote, “Researchers at the University of Amsterdam are using neural networks to help a statistical machine translation systems learn what all human translators know — that the best translation of a word often depends on the context. Machine translation systems such as Google Translate or those at iTranslate4.eu guess how to translate words and phrases based on how often they appear in a large corpus of human-translated texts. Such tools are increasingly important as individuals and businesses seek to access information or buy products and services from other countries where different languages are spoken.” Read more
Posts Tagged ‘machine translation’
The natural language processing (NLP) market is moving ahead at a steady clip. According to the recently released report, Natural Language Processing Market – Worldwide Market Forecast and Analysis (2013 – 2018), the sector is estimated to grow from $3,787.3 million in 2013 to $9,858.4 million in 2018. That’s an estimated 21 percent CAGR.
The report considers the market to factor in multiple technologies — recognition technologies such as Interactive Voice Response, Optical Character Recognition, and pattern and image recognition; operational technologies such as auto coding and classification and categorization technologies; and text analytics and speech analytics technologies; as well as machine translation, information extraction and question-answer report generation.
Driving the uptake, the report notes, is the need to enhance customer experiences, especially in an age when the smartphone rules, and Big Data predominates. Big-time industry adopters of the technology, it cites, are healthcare, banking and financial services, and e-commerce, where a big growth in real-time and unstructured customer data and transaction information can be taken in hand by NLP technology to analyze customer needs and then optimize responses to them, taking out some of the human labor costs of doing so.
What do you get when you mix two parts natural language processing with a little personalization, and add in a dash of the cloud? The answer is Whisk, a U.K. company building a service that lets users purchase the ingredients for any recipe they find on the Internet.
“The crux of it is that you can take any recipe on the ‘Net and turn it into a transaction in on online market,” says co-founder Craig Edmunds. “There’s a machine translation problem from the recipe up through to our internal language, which is one NLP problem, and then another is from our internal language into online markets.” Another leg of the work is that the service seeks to not match to just one item at a market but as many as possible, and consider user preferences as to which is the optimal product, too.
At the upcoming Semantic Technology and Business Conference in the U.K., Edmunds will be considering how the issues of machine translation, manual intervention, personalization and the cloud intersect in creating a service that adds all the ingredients they need for dishes they find online straight into their online shopping basket.