2,995 research outputs found
Compositional Distributional Semantics with Long Short Term Memory
We are proposing an extension of the recursive neural network that makes use
of a variant of the long short-term memory architecture. The extension allows
information low in parse trees to be stored in a memory register (the `memory
cell') and used much later higher up in the parse tree. This provides a
solution to the vanishing gradient problem and allows the network to capture
long range dependencies. Experimental results show that our composition
outperformed the traditional neural-network composition on the Stanford
Sentiment Treebank.Comment: 10 pages, 7 figure
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
Morphological Priors for Probabilistic Neural Word Embeddings
Word embeddings allow natural language processing systems to share
statistical information across related words. These embeddings are typically
based on distributional statistics, making it difficult for them to generalize
to rare or unseen words. We propose to improve word embeddings by incorporating
morphological information, capturing shared sub-word features. Unlike previous
work that constructs word embeddings directly from morphemes, we combine
morphological and distributional information in a unified probabilistic
framework, in which the word embedding is a latent variable. The morphological
information provides a prior distribution on the latent word embeddings, which
in turn condition a likelihood function over an observed corpus. This approach
yields improvements on intrinsic word similarity evaluations, and also in the
downstream task of part-of-speech tagging.Comment: Appeared at the Conference on Empirical Methods in Natural Language
Processing (EMNLP 2016, Austin
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Because of their superior ability to preserve sequence information over time,
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with
a more complex computational unit, have obtained strong results on a variety of
sequence modeling tasks. The only underlying LSTM structure that has been
explored so far is a linear chain. However, natural language exhibits syntactic
properties that would naturally combine words to phrases. We introduce the
Tree-LSTM, a generalization of LSTMs to tree-structured network topologies.
Tree-LSTMs outperform all existing systems and strong LSTM baselines on two
tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task
1) and sentiment classification (Stanford Sentiment Treebank).Comment: Accepted for publication at ACL 201
Don't Blame Distributional Semantics if it can't do Entailment
Distributional semantics has had enormous empirical success in Computational
Linguistics and Cognitive Science in modeling various semantic phenomena, such
as semantic similarity, and distributional models are widely used in
state-of-the-art Natural Language Processing systems. However, the theoretical
status of distributional semantics within a broader theory of language and
cognition is still unclear: What does distributional semantics model? Can it
be, on its own, a fully adequate model of the meanings of linguistic
expressions? The standard answer is that distributional semantics is not fully
adequate in this regard, because it falls short on some of the central aspects
of formal semantic approaches: truth conditions, entailment, reference, and
certain aspects of compositionality. We argue that this standard answer rests
on a misconception: These aspects do not belong in a theory of expression
meaning, they are instead aspects of speaker meaning, i.e., communicative
intentions in a particular context. In a slogan: words do not refer, speakers
do. Clearing this up enables us to argue that distributional semantics on its
own is an adequate model of expression meaning. Our proposal sheds light on the
role of distributional semantics in a broader theory of language and cognition,
its relationship to formal semantics, and its place in computational models.Comment: To appear in Proceedings of the 13th International Conference on
Computational Semantics (IWCS 2019), Gothenburg, Swede
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