Google has released an English parser called Parsey McParseface. Despite the name, the parser is entirely serious - here’s part of their description of it: 

One of the main problems that makes parsing so challenging is that human languages show remarkable levels of ambiguity. 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. As a very simple example, the sentence Alice drove down the street in her car has at least two possible dependency parses:

Dependency trees, Natural Language Processing, and Google Ngrams

An explanation of syntactic dependency trees from the Google Research blog: 

Dependency-trees representation is centered around words and the relations between them. Each word in a sentence can either modify or be modified by other words. The various modifications can be represented as a tree, in which each node is a word.

Unlike X-bar syntax trees, dependency trees allow more than two branches from each node and don't have any bar levels or abstract categories like IP, CP, TP, etc. 

Google is interested in syntactic structure because they've just released an update to Google Ngrams that encodes structural relationships between words: 

Once we know the structure of many sentences, we can use these structures to infer the meaning of words, or at least find words which have a similar meaning to each other. For example, consider the fragments: "order a XYZ" "XYZ is tasty" "XYZ with ketchup" "juicy XYZ" By looking at the words modifying XYZ and their relations to it, you could probably infer that XYZ is a kind of food. And even if you are a robot and don't really know what a "food" is, you could probably tell that the XYZ must be similar to other unknown concepts such as "steak" or "tofu". But maybe you don't want to infer anything. Maybe you already know what you are looking for, say "tasty food". In order to find such tasty food, one could collect the list of words which are objects of the verb "ate", and are commonly modified by the adjective "tasty" and "juicy". This should provide you a large list of yummy foods. Imagine what you could achieve if you had hundreds of millions of such fragments. The possibilities are endless, and we are curious to know what the research community may come up with. So we parsed a lot of text (over 3.5 million English books, or roughly 350 billion words), extracted such tree fragments, counted how many times each fragment appeared, and put the counts online for everyone to download and play with. 350 billion words is a lot of text, and the resulting dataset of fragments is very, very large. The resulting datasets, each representing a particular type of tree fragments, contain billions of unique items, and each dataset’s compressed files takes tens of gigabytes. Some coding and data analysis skills will be required to process it, but we hope that with this data amazing research will be possible, by experts and non-experts alike.