Instead, we introduce a Compositional Vector Grammar (CVG), which combines PCFGs with a syntactically untied recursive neural network that learns syntactico-semantic, compositional vector representations .

Previous RNN-based parsers used the same (tied) weights at all nodes to compute the vector representing a constituent (Socher et al., 2011b).

Therefore we combine syntactic and semantic information by giving the parser access to rich syntactico-semantic information in the form of distributional word vectors and compute compositional semantic vector representations for longer phrases (Costa et al., 2003; Menchetti et al., 2005; Socher et al., 2011b).

We will first briefly introduce single word vector representations and then describe the CVG objective function, tree scoring and inference.

Annotation of quality of test vectors The quality of the corpus-based vectors representing derived test items was determined by collecting human semantic similarity judgments in a crowdsourcing survey.

The first experiment investigates to what extent composition models can approximate high-quality (HQ) corpus-extracted vectors representing derived forms.

Lexfunc provides a flexible way to account for affixation, since it models it directly as a function mapping from and onto word vectors, without requiring a vector representation of bound affixes.

Although these works exploit vectors representing complex forms, they do not attempt to generate them compositionally.

Unlike in most applications of HMMs in text processing, in which the representation of a token is simply its word or lemma identity, tokens in DSHMM are also associated with a vector representation of their meaning in context according to a distributional semantic model (Section 3.1).

All the methods below start from this basic vector representation .

Let event head h be the syntactic head of a number of arguments a1,a2, ...am, and 27h,27a1,27a2, ...27am be their respective vector representations according to the SIMPLE method.

The recursive application of autoencoders was first introduced in Pollack (1990), whose recursive auto-associative memories learn vector representations over pre-specified recursive data structures.

The unsupervised method described so far learns a vector representation for each sentence.

Their purpose is to learn semantically meaningful vector representations for sentences and phrases of variable size, while the purpose of this paper is to investigate the use of syntax and linguistic formalisms in such vector-based compositional models.