51 research outputs found
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
In this paper, the answer selection problem in community question answering
(CQA) is regarded as an answer sequence labeling task, and a novel approach is
proposed based on the recurrent architecture for this problem. Our approach
applies convolution neural networks (CNNs) to learning the joint representation
of question-answer pair firstly, and then uses the joint representation as
input of the long short-term memory (LSTM) to learn the answer sequence of a
question for labeling the matching quality of each answer. Experiments
conducted on the SemEval 2015 CQA dataset shows the effectiveness of our
approach.Comment: 6 page
Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?
Event co-occurrences have been proved effective for event extraction (EE) in
previous studies, but have not been considered for event argument extraction
(EAE) recently. In this paper, we try to fill this gap between EE research and
EAE research, by highlighting the question that ``Can EAE models learn better
when being aware of event co-occurrences?''. To answer this question, we
reformulate EAE as a problem of table generation and extend a SOTA prompt-based
EAE model into a non-autoregressive generation framework, called TabEAE, which
is able to extract the arguments of multiple events in parallel. Under this
framework, we experiment with 3 different training-inference schemes on 4
datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the
model to extract all events in parallel, it can better distinguish the semantic
boundary of each event and its ability to extract single event gets
substantially improved. Experimental results show that our method achieves new
state-of-the-art performance on the 4 datasets. Our code is avilable at
https://github.com/Stardust-hyx/TabEAE.Comment: Accepted to ACL 2023 main conferenc
Chunking with Max-Margin Markov Networks
PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200
PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
Biomedical language understanding benchmarks are the driving forces for
artificial intelligence applications with large language model (LLM) back-ends.
However, most current benchmarks: (a) are limited to English which makes it
challenging to replicate many of the successes in English for other languages,
or (b) focus on knowledge probing of LLMs and neglect to evaluate how LLMs
apply these knowledge to perform on a wide range of bio-medical tasks, or (c)
have become a publicly available corpus and are leaked to LLMs during
pre-training. To facilitate the research in medical LLMs, we re-build the
Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark into a
large scale prompt-tuning benchmark, PromptCBLUE. Our benchmark is a suitable
test-bed and an online platform for evaluating Chinese LLMs' multi-task
capabilities on a wide range bio-medical tasks including medical entity
recognition, medical text classification, medical natural language inference,
medical dialogue understanding and medical content/dialogue generation. To
establish evaluation on these tasks, we have experimented and report the
results with the current 9 Chinese LLMs fine-tuned with differtent fine-tuning
techniques
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical
language understanding, is gradually changing medical practice. With the
development of biomedical language understanding benchmarks, AI applications
are widely used in the medical field. However, most benchmarks are limited to
English, which makes it challenging to replicate many of the successes in
English for other languages. To facilitate research in this direction, we
collect real-world biomedical data and present the first Chinese Biomedical
Language Understanding Evaluation (CBLUE) benchmark: a collection of natural
language understanding tasks including named entity recognition, information
extraction, clinical diagnosis normalization, single-sentence/sentence-pair
classification, and an associated online platform for model evaluation,
comparison, and analysis. To establish evaluation on these tasks, we report
empirical results with the current 11 pre-trained Chinese models, and
experimental results show that state-of-the-art neural models perform by far
worse than the human ceiling. Our benchmark is released at
\url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}
Prediction of protein binding sites in protein structures using hidden Markov support vector machine
<p>Abstract</p> <p>Background</p> <p>Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.</p> <p>Results</p> <p>In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.</p> <p>Conclusion</p> <p>The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.</p
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