3,562 research outputs found
Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation
In this paper, we empirically evaluate the utility of transfer and multi-task
learning on a challenging semantic classification task: semantic interpretation
of noun--noun compounds. Through a comprehensive series of experiments and
in-depth error analysis, we show that transfer learning via parameter
initialization and multi-task learning via parameter sharing can help a neural
classification model generalize over a highly skewed distribution of relations.
Further, we demonstrate how dual annotation with two distinct sets of relations
over the same set of compounds can be exploited to improve the overall accuracy
of a neural classifier and its F1 scores on the less frequent, but more
difficult relations.Comment: EMNLP 2018: Conference on Empirical Methods in Natural Language
Processing (EMNLP
Distributional semantics beyond words: Supervised learning of analogy and paraphrase
There have been several efforts to extend distributional semantics beyond
individual words, to measure the similarity of word pairs, phrases, and
sentences (briefly, tuples; ordered sets of words, contiguous or
noncontiguous). One way to extend beyond words is to compare two tuples using a
function that combines pairwise similarities between the component words in the
tuples. A strength of this approach is that it works with both relational
similarity (analogy) and compositional similarity (paraphrase). However, past
work required hand-coding the combination function for different tasks. The
main contribution of this paper is that combination functions are generated by
supervised learning. We achieve state-of-the-art results in measuring
relational similarity between word pairs (SAT analogies and SemEval~2012 Task
2) and measuring compositional similarity between noun-modifier phrases and
unigrams (multiple-choice paraphrase questions)
Coordinate noun phrase disambiguation in a generative parsing model
In this paper we present methods for improving the disambiguation of noun phrase (NP) coordination within the framework of a lexicalised history-based parsing model. As
well as reducing noise in the data, we look at modelling two main sources of information for disambiguation: symmetry in conjunct structure, and the dependency between conjunct lexical heads. Our changes to the baseline model result in an increase in NP coordination dependency f-score from 69.9% to
73.8%, which represents a relative reduction in f-score error of 13%
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