18,175 research outputs found
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interactions between words in the context of a sentence. Embeddings and
composition layers are jointly learned against a generic objective that
enhances the vectors with syntactic information from the surrounding context.
Furthermore, each word is associated with a number of senses, the most
plausible of which is selected dynamically during the composition process. We
evaluate the produced vectors qualitatively and quantitatively with positive
results. At the sentence level, the effectiveness of the framework is
demonstrated on the MSRPar task, for which we report results within the
state-of-the-art range.Comment: Accepted for presentation at EMNLP 201
Implicit learning of recursive context-free grammars
Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning
experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have
not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing
features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured
the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both
distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes
even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between
individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for
tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex
context-free structures, which model some features of natural languages. They support the relevance of artificial grammar
learning for probing mechanisms of language learning and challenge existing theories and computational models of
implicit learning
Beyond Word N-Grams
We describe, analyze, and evaluate experimentally a new probabilistic model
for word-sequence prediction in natural language based on prediction suffix
trees (PSTs). By using efficient data structures, we extend the notion of PST
to unbounded vocabularies. We also show how to use a Bayesian approach based on
recursive priors over all possible PSTs to efficiently maintain tree mixtures.
These mixtures have provably and practically better performance than almost any
single model. We evaluate the model on several corpora. The low perplexity
achieved by relatively small PST mixture models suggests that they may be an
advantageous alternative, both theoretically and practically, to the widely
used n-gram models.Comment: 15 pages, one PostScript figure, uses psfig.sty and fullname.sty.
Revised version of a paper in the Proceedings of the Third Workshop on Very
Large Corpora, MIT, 199
Compositional Vector Space Models for Knowledge Base Completion
Knowledge base (KB) completion adds new facts to a KB by making inferences
from existing facts, for example by inferring with high likelihood
nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop
relational synonyms like this, or use as evidence a multi-hop relational path
treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper
presents an approach that reasons about conjunctions of multi-hop relations
non-atomically, composing the implications of a path using a recursive neural
network (RNN) that takes as inputs vector embeddings of the binary relation in
the path. Not only does this allow us to generalize to paths unseen at training
time, but also, with a single high-capacity RNN, to predict new relation types
not seen when the compositional model was trained (zero-shot learning). We
assemble a new dataset of over 52M relational triples, and show that our method
improves over a traditional classifier by 11%, and a method leveraging
pre-trained embeddings by 7%.Comment: The 53rd Annual Meeting of the Association for Computational
Linguistics and The 7th International Joint Conference of the Asian
Federation of Natural Language Processing, 201
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