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31 May 16. Google Implements Neural Network Computing For Machine Learning. Google’s SyntaxNet, an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Google 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.
Parsey McParseface is built on powerful machine learning algorithms that learn to analyze the linguistic structure of language, and that can explain the functional role of each word in a given sentence. Because Parsey McParseface is the most accurate such model in the world, we hope that it will be useful to developers and researchers interested in automatic extraction of information, translation, and other core applications of NLU.
How does SyntaxNet work?
SyntaxNet is a framework for what’s known in academic circles as a syntactic parser, which is a key first component in many NLU systems. Given a sentence as input, it tags each word with a part-of-speech (POS) tag that describes the word’s syntactic function, and it determines the syntactic relationships between words in the sentence, represented in the dependency parse tree. These syntactic relationships are directly related to the underlying meaning of the sentence in question. To take a very simple example, consider the following dependency tree for Alice saw Bob:
This structure encodes that Alice and Bob are nouns and saw is a verb. The main verb saw is the root of the sentence and Alice is the subject (nsubj) of saw, while Bob is its direct object (dobj).
This structure again encodes the fact that Alice and Bob are the subject and object respectively of saw, in addition that Alice is modified by a relative clause with the verb reading, that saw is modified by the temporal modifier yesterday, and so on. The grammatical relationships encoded in dependency structures allow us to easily recover the answers to various questions, for example whom did Alice see? who saw Bob? what had Alice been reading about? or when did Alice see Bob?
Why is Parsing So Hard for Computers to Get Right?
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
The first corresponds to the (correct) interpretation where Alice is driving in her car; the second corresponds to the (absurd, but possible) interpretation where the street is located in her car. The ambiguity arises because the preposition in can either modify drove or street; this example is an instance of what is called prepositional phrase attachment ambiguity.
Humans do a remarkable job of dealing with ambiguity, almost to the point where the problem is unnoticeable; the challenge is for computers to do the same. Multiple ambiguities such as these in longer sentences conspire to give a combinatorial explosion in the number of possible structures for a sentence. Usually the vast majority of these structures are wildly implausible, but are nevertheless possible and must be somehow discarded by a parser.
SyntaxNet applies neural networks to the ambiguity problem. An input sentence is processed from left to right, with dependencies between words being incrementally added as each word in the sentence is considered. At each point in processing many decisions may