Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers
نویسندگان
چکیده
We present a neural architecture for containment relation identification between medical events and/or temporal expressions. We experiment on a corpus of deidentified clinical notes in English from the Mayo Clinic, namely the THYME corpus. Our model achieves an F-measure of 0.613 and outperforms the best result reported on this corpus to date.
منابع مشابه
Neural Temporal Relation Extraction
We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-ofthe-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-b...
متن کاملNeural Architecture for Negative Opinion Expressions Extraction
Opinion expressions extraction is one of the main frameworks in opinion mining. Extracting negative opinions is more difficult than positive opinions because of indirect expressions. Especially, in the domain of consumer reviews, consumers are easier to be influenced by negative reviews when making decision. In this paper, we focus on the extraction of negative opinion expressions of consumer r...
متن کاملSemantic Relation Classification by Bi-directional LSTM Architecture
Semantic relation extraction is a meaningful task in NLP that could provide some helpful information and semantic relation classification attracts many people to research it. This paper mainly introduces a Bi-direction LSTM (long short-term memory) deep neutral network and the parameter of embedding layer, and this network can solve the problem of over-fitting. And then according to the text of...
متن کاملSimulating Player Behavior for Data-Driven Interactive Narrative Personalization
Data-driven approaches to interactive narrative personalization show significant promise for applications in entertainment, training, and education. A common feature of datadriven interactive narrative planning methods is that an enormous amount of training data is required, which is rarely available and expensive to collect from observations of human players. An alternative approach to obtaini...
متن کاملA Convolutional Encoder Model for Neural Machine Translation
The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT’16 EnglishRomanian translation w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017