2,578 research outputs found
Neural Reranking for Named Entity Recognition
We propose a neural reranking system for named entity recognition (NER). The
basic idea is to leverage recurrent neural network models to learn
sentence-level patterns that involve named entity mentions. In particular,
given an output sentence produced by a baseline NER model, we replace all
entity mentions, such as \textit{Barack Obama}, into their entity types, such
as \textit{PER}. The resulting sentence patterns contain direct output
information, yet is less sparse without specific named entities. For example,
"PER was born in LOC" can be such a pattern. LSTM and CNN structures are
utilised for learning deep representations of such sentences for reranking.
Results show that our system can significantly improve the NER accuracies over
two different baselines, giving the best reported results on a standard
benchmark.Comment: Accepted as regular paper by RANLP 201
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Over the past decade, large-scale supervised learning corpora have enabled
machine learning researchers to make substantial advances. However, to this
date, there are no large-scale question-answer corpora available. In this paper
we present the 30M Factoid Question-Answer Corpus, an enormous question answer
pair corpus produced by applying a novel neural network architecture on the
knowledge base Freebase to transduce facts into natural language questions. The
produced question answer pairs are evaluated both by human evaluators and using
automatic evaluation metrics, including well-established machine translation
and sentence similarity metrics. Across all evaluation criteria the
question-generation model outperforms the competing template-based baseline.
Furthermore, when presented to human evaluators, the generated questions appear
comparable in quality to real human-generated questions.Comment: 13 pages, 1 figure, 7 table
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
Emergent Predication Structure in Hidden State Vectors of Neural Readers
A significant number of neural architectures for reading comprehension have
recently been developed and evaluated on large cloze-style datasets. We present
experiments supporting the emergence of "predication structure" in the hidden
state vectors of these readers. More specifically, we provide evidence that the
hidden state vectors represent atomic formulas where is a
semantic property (predicate) and is a constant symbol entity identifier.Comment: Accepted for Repl4NLP: 2nd Workshop on Representation Learning for
NL
- …