Starting from the moment we hear our alarms in the morning, our emotions guide the thousands of decisions we make every day. More specifically, it is the valence of our emotions that determines our subsequent behavior. Valence is a concept that was originally defined in psychology and corresponds to the value we assign to the perceptions of our external and internal environments (1). Valence varies from negative, when we are afraid or anxious, to positive, when we are happy or peaceful. In the case of the morning alarm, if your emotional state has a positive valence you might jump out of bed, eager to engage with whatever is motivating you.

We wanted to gather and analyze news content in order to look for similarities and differences in the way two journalists write headlines for their respective news articles and blog posts. Note: For a more technical, in-depth and interactive representation of this project, check out the Jupyter notebook we created. This includes sample code and more in depth descriptions of our approach. In linguistics, a parse tree is a rooted tree that represents the syntactic structure of a sentence, according to some pre-defined grammar. For example with a simple sentence like "The cat sat on the mat", a parse tree might look like this; Thankfully parsing our extracted headlines isn't too difficult.

"The future is independent of the past, given the present." Orthography encapsulates a set of conventions for a written language, which includes spellings, punctuation, hyphenation, capitalisation, and other features. For example, in English, we begin a sentence with a capital letter and have spaces between words; each word is spelt in a unique way (most of the times), and we use commas to separate clauses and mark the end of a sentence with a period. Reading about the history of English spelling is fascinating. You probably know Geoffrey Chaucer for his collection of short stories, The Canterbury Tales.

Google has just open-sourced its'Parsey McParseface' AI tool and it understands English Google has open-sourced its neural network framework, SyntaxNet to include new language parsing model for English, which it calls'Parsey McParseface.' This is a tool that developers can use to analyze English text. In other words, developers will be able to swindle with the underlying technology powering Google's powerful natural language software so that apps, voice assistants and robots can better comprehend what English-speaking users want. "One of the main problems that makes parsing so challenging is that human languages show remarkable levels of ambiguity," Google says in a blog post. "It is not uncommon for moderate length sentences--say 20 or 30 words in length--to have hundreds, thousands, or even tens of thousands of possible syntactic structures. A natural language parser must somehow search through all of these alternatives, and find the most plausible structure given the context."

A long-standing goal of artificial intelligence (AI) is to build systems capable of understanding natural language. To focus the notion of "understanding" a bit, let us say the system must produce an appropriate action upon receiving an input utterance from a human. We are interested in utterances such as the ones listed here, which require deep understanding and reasoning. This article focuses on semantic parsing, an area within the field of natural language processing (NLP), which has been growing over the last decade. Semantic parsers map input utterances into semantic representations called logical forms that support this form of reasoning.

Google today announced the public beta launch of its Cloud Natural Language API, a new service that gives developers access to Google-powered sentiment analysis, entity recognition, and syntax analysis. This new API joins Google's other pre-trained machine-learning APIs like the Cloud Speech API, which is now also available in public beta, the Vision API and the Translate API. The new Cloud Natural Language API currently supports texts in English, Spanish and Japanese. Google notes that the idea here is to offer a service "that can meet the scale and performance needs of developers and enterprises in a broad range of industries." Offering an API for sentiment analysis and entity recognition isn't new, of course.

For the human eye reading a CV (resume) or a job ad is an easy task. These semi-structured documents are usually separated in sections and have layouts that makes it easy to quickly identify important information. In contrast, a computer system that parses CVs needs to be continuously trained and adapted to deal with the endless expressivity of human language. As a leader in the field of language technology, Textkernel is working hard to provide the best CV parser to our customers. In this blog article I will explain how we achieve this and discuss the focus of our current research efforts.

Facebook is getting even closer to a human-level understanding of what people are saying. Facebook has developed DeepText, a new way to parse text using artificial intelligence processes that's quicker at picking up new languages and slang than traditional approaches. In a company blog post published on Wednesday, three members of the company's applied machine learning team -- Ahmad Abdulkader, Aparna Lakshmiratan and Joy Zhang -- announced the technology that's already being used across Facebook and Facebook Messenger. DeepText is able to churn through "several thousands of posts per second" across more than 20 languages and understand what's being communicated with "near-human accuracy," according to the announcement post. Facebook's ability to comprehend what people are saying on its platform isn't new.

We present a framework that couples the syntax and semantics of natural language sentences in a generative model, in order to develop a semantic parser that jointly infers the syntactic, morphological, and semantic representations of a given sentence under the guidance of background knowledge. To generate a sentence in our framework, a semantic statement is first sampled from a prior, such as from a set of beliefs in a knowledge base. Given this semantic statement, a grammar probabilistically generates the output sentence. A joint semantic-syntactic parser is derived that returns the $k$-best semantic and syntactic parses for a given sentence. The semantic prior is flexible, and can be used to incorporate background knowledge during parsing, in ways unlike previous semantic parsing approaches. For example, semantic statements corresponding to beliefs in a knowledge base can be given higher prior probability, type-correct statements can be given somewhat lower probability, and beliefs outside the knowledge base can be given lower probability. The construction of our grammar invokes a novel application of hierarchical Dirichlet processes (HDPs), which in turn, requires a novel and efficient inference approach. We present experimental results showing, for a simple grammar, that our parser outperforms a state-of-the-art CCG semantic parser and scales to knowledge bases with millions of beliefs.

A TensorFlow implementation of the models described in Andor et al. (2016). At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. We are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework for TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you, and that you can use to analyze English text. So, how accurate is Parsey McParseface?