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Found 11 papers, 7 papers with code
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders
Understanding text often requires identifying meaningful constituent spans such as noun phrases and verb phrases.
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders
We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree.
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets
This paper presents a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types.
Linguistically-Informed Self-Attention for Semantic Role Labeling
Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates.
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction
Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention.
Finer Grained Entity Typing with TypeNet
We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types.
Attending to All Mention Pairs for Full Abstract Biological Relation Extraction
We propose a model to consider all mention and entity pairs simultaneously in order to make a prediction.
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs.
Ranked #24 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema
In experiments predicting both relations and entity types, we demonstrate that despite having an order of magnitude fewer parameters than traditional universal schema, we can match the accuracy of the traditional model, and more importantly, we can now make predictions about unseen rows with nearly the same accuracy as rows available at training time.
Row-less Universal Schema
In experimental results on the FB15k-237 benchmark we demonstrate that we can match the performance of a comparable model with explicit entity pair representations using a model of attention over relation types.
Multilingual Relation Extraction using Compositional Universal Schema
In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation.
