parse2vec

Tool for generating parse tree embeddings, parse tree enriched word embeddings and parse tree enriched sentence embeddings.

How it works

Given a set of sentences (one by line) in a text file, this tool:

• learns word embeddings using word2vec
• builds the parse tree of each sentence
• using the parse tree structure it recursively averages word embeddings PoS Type-wise from all the sentences' parse tree
• each PoS tag finally has an embedding
• to enrich the word embedding with parse tree information, for each existing word:
  • recursively sums the type embeddings of the word ancestors (in the parse tree) and averages the result with its word embedding
• to generate a sentence embedding enhanced with parse tree information:
  • recursively builds the sentence vector using parse tree enriched word embeddings using the algorithm from par2hier to build sentence vectors from hierarchical structures

Examples

parse tree embeddings nearest neighbour sample results:

nearest(VB) = VP
nearest(JJR) = RBR
nearest(CONJP) = AUX
...

parse tree enriched word embeddings sample results:

nearest(crowd) = multiple, man, ...
nearest(hierarchical) = relationship, soft-max ...
nearest(Sutskever) = Greg, Kai, ...
...

parse tree enriched sentence embeddings sample results:

nearest(In order to capture in a quantitative way the nuance necessary to distinguish man from woman ...) = 
 - In parallel in the last few years language models based on neural networks have been used to cope with complex natural language processing tasks like emotion and paraphrase detection.
 - Based on a recent work that proposed to learn a generic language model that can be modified through a set of document-specific parameters we explore use of new neural network models that are adapted to ad-hoc IR tasks.

nearest(We introduce a new dataset with human judgments on pairs of words in sentential context and ...) =
 - The result can be used to enrich lexicons of under-resourced languages to identify ambiguities and to perform clustering and classification .
 - We consider the conditional probabilities p(c|w) and given a corpus Text the goal is to set the parameters θ of p(c|w;θ) so as to maximize the corpus probability .
...