Received Feb 21; Accepted Jul This article has been cited by other articles in PMC. The document referred to as Additional file 1 containing details of the corpora used is also available at this site. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance.
To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings.
Each dataset represent a task. The results from the single-task model and the multi-task models are then compared for evidence of benefits from Multi-task Learning. With the Multi-output multi-task model we observed an average F-score improvement of 0.
Although there was a significant drop in performance on one dataset, performance improves significantly for five datasets by up to 6.
For the Dependent multi-task model we observed an average improvement of 0. There were no significant drops in performance on any dataset, and performance improves significantly for six datasets by up to 1. Conclusions Our results show that, on average, the multi-task models produced better NER results than the single-task models trained on a single NER dataset.
We also found that Multi-task Learning is beneficial for small datasets. Across the various settings the improvements are significant, demonstrating the benefit of Multi-task Learning for this task. Electronic supplementary material The online version of this article doi Keywords: Multi-task learning, Convolutional neural networks, Named entity recognition, Biomedical text mining Background Biomedical text mining and Natural Language Processing NLP have made tremendous progress over the past decades, and are now used to support practical tasks such as literature curation, literature review and semantic enrichment of networks [ 1 ].
- Для приключений и развития воображения саги предоставляли все, что только можно было пожелать.
- Сирэйнис сказала, что Хедрон исчез.
While this is a promising development, many real-life tasks in biomedicine would benefit from further improvements in the accuracy of text mining systems. The necessary first step in processing literature for biomedical text mining is identifying relevant named entities such as protein names in text.
High accuracy NER systems require manually annotated named entity datasets for training and evaluation. Many such datasets have been created and made publicly available.
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These include annotations for a variety of named entities such as genes and proteins [ 2 ], chemicals [ 3 ] and species [ 4 ] names. Because manual annotations are expensive to develop, datasets are limited in size and not available for many sub-domains of biomedicine [ 56 ]. As a consequence, many NER systems suffer from poor performance [ 78 ]. The question of how to improve the performance of NER, especially in the very common situation where only limited annotations are available, is still an open area of research.
One potentially promising solution is to use multiple annotated datasets together to train a model for improved performance on a single dataset. This can help since datasets may contain complementary information that can help to solve individual tasks more accurately when trained jointly. The basic idea of MTL is to learn a problem together with other related problems at the same time, using a shared representation.
When tasks have commonality and especially when training data for them are limited, MTL can lead to better performance than a model trained on only a single dataset, allowing the learner to capitalise on the commonality among the tasks. This has been previously demonstrated in several learning scenarios in bioinformatics and in several other application areas of machine learning [ 10 — 12 ]. A variety of different methods have been used for MTL, including neural networks, joint inference, and learning low dimensional features that can be transferred to different tasks [ 111314 ].
This is, to the best of our knowledge, the first application of this MTL framework to dtal in binary options task. Dtal in binary options other language processing tasks in biomedicine, NER is made challenging by the nature of biomedical texts, e.
A neural network multi-task learning approach to biomedical named entity recognition
Additionally, the annotated datasets available vary greatly in the nature of named entities e. It is therefore an open question whether this task can benefit from MTL.
Due to the aforementioned disparities between data-sets, we treat each dataset as a separate task even when the annotators sought to annotate the same named entities.
Thus datasets and tasks are used interchangeably. The results are then compared for evidence of benefits from MTL.
On one Dtal in binary options model we observe an average F-score improvement of 0. Although there is a significant drop in performance on one dataset, performance improves significantly for five datasets.
For the other MTL model we observe an average F-score improvement of 0. There is no significant drop in performance on any dataset, and performance improves significantly for six datasets. Motivation Previous work have demonstrated the benefits of MTL. These include leveraging the information contained in the training signals of related tasks during training to perform better at a given task, combining data across tasks when few data are available per task and discovering relatedness among data previously thought to be unrelated [ 121719 ].
These benefits can be seen in potentially ambiguous terms which are spelled the same and are named entities in some situations, but not in others.
Some training sets may contain examples of both so that a model can learn to distinguish between them, but others may only contain one type.
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A model trained with a dataset combination which contains both types even if each dataset contains only one but they are opposites can learn to distinguish between them and perform better. We are similarly interested in these benefits, but are additionally interested in the following benefits, given the particular challenges of biomedical text mining. Making the best use of information in existing datasets Given the level of knowledge interaction and overlap in the biomedical domain, it is conceivable that signals learned from one dataset could be helpful in learning to perform well on other datasets.
There are three other datasets which do contain Pebp2 and its variants in their training data so models trained with these datasets may do better on the evaluation than models trained in isolation. If a model can utilize such information it could conceivably perform better as a result of having access to this additional knowledge. Currently, when models use additional knowledge as guidance it is typically handcrafted and passed to models during training rather than learned as part of the training process.
Efficient creation and use of datasets The datasets used to train supervised and semi-supervised models are expensive to create.
They typically contain manual annotations by highly trained domain specialists e. If models which facilitate the transfer of knowledge between existing datasets can be developed and understood, they may be able to reduce the annotation overhead.
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For example, such models may be able to detect which type of annotations are really needed and which are not because the information is already included in another dataset or the knowledge requirements of tasks overlap. This can help to focus annotation efforts aimed at types not covered in any existing datasets and can aid in obtaining required annotations faster even if the resulting datasets are smaller. Caruana [ 9 ] demonstrated that sampling data amplification can help small datasets in MTL where tasks are related by combining the estimates of the learned parameters to obtain better estimates than it would by estimating them from small samples which may not provide enough information for modeling complex relationships between input and predictions.
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It can be tempting to think that these objectives can be met by simply combining the existing corpora into a single large corpus which can then be used to train a model. Thus the problem of utilizing all the knowledge in existing datasets in a single model to gain the benefits of doing so, including those highlighted in this section, remains a challenging open problem in biomedical NLP.
Related work MTL uses inductive transfer in such a way as to improve learning for a task by using signals of related tasks discovered during training. The work of [ 9 ] motivated and laid the foundation for much of the work done in MTL by demonstrating feasibility and important early findings.
The author applied MTL on various detailed synthetic and four real-world problems. He highlighted the importance of the tasks being related and defined to a great extent what related meant in the context of MTL.
He defines a related task as one which gives the main task better performance than when dtal in binary options is trained on its own. He found that: related tasks are not correlated tasks, related tasks must share input features and hidden units to benefit each other during training and finally that related tasks would not always help each other.
This final finding may seem at odds to the given definition of related, but he explains that the learning algorithm also affects whether related tasks are able to benefit each other and allows for the existence of related tasks which the algorithm may not be able to take advantage of.
Collobert et al. They achieved a unified model which performed all tasks without significant degradation of performance, but there was little benefit from MTL. Ando and Zhang [ 11 ] investigated learning functions which serve as good predictors of good classifiers on hypothesis spaces using MTL of labeled and unlabeled data.
They reported good results when tested on several machine learning tasks including Dtal in binary options, POS tagging and hand-written digit image classification. Liu et al. Their model outperformed strong baselines for both query classification and web search tasks. MTL can be related in some sense to joint learning and to that end [ 22 ] presented a model which used single-task annotated data as additional information to improve the performance of a model for jointly learning two tasks over five datasets.
Qi et al.
They first trained a model on supervised classification task with fully-labeled examples then shared some layers of the model with a semi-supervised model which is trained on only partially-labeled examples. Zeng and Ji [ 15 dtal in binary options successfully used the weights of CNNs from [ 26 ] trained on general domain images as the starting point for further training on images in the biomedical domain to gain improved performance. Zhang et al.
Their features learned from deep models with multi-task methods outperformed other methods in annotating gene expression patterns.
In summary, research in MTL using neural networks has produced a wide spectrum of approaches. These approaches have yielded impressive results on some tasks e.
Лишь на первый взгляд казалось, что статуя глядит на город: встав прямо перед ней, можно было заметить, что глаза ее опущены, и ускользающая усмешка направлена к месту, расположенному сразу после входа в Гробницу. Зная секрет, в этом уже нельзя было сомневаться. Элвин перешел на соседний блок и удостоверился, что взгляд Ярлана Зея обращен теперь уже чуть-чуть в сторону от. Он вернулся к Хедрону и в уме повторил слова, произнесенные Шутом вслух: "Диаспар не всегда был таким".
We present a single task and two multi-task models which train these datasets and compare their performance across the two settings. We were able to achieve significant gains in several datasets with both of the multi-task models despite the difference in the way in which they apply MTL.
Methods Pre-trained biomedical word embeddings All our experiments used pre-trained, static word representations as input to the models. These representations are called word embeddings and are the inputs to most current neural network models which operate on text. Popular embeddings include those created by [ 2829 ]. Those are however aimed at general domain work and can produce very high out-of-vocabulary rates when used on biomedical texts, thus for this work we used the embeddings created in [ 30 ] which are dtal in binary options from biomedical texts.
An embedding for unknown words was also trained for use with out-of-vocabulary words during training of our models. POS tagging is a sequential labeling task which assigns a part-of-speech e. Verb, Nouns to each word in text. Table 1 The datasets and details of their annotations Dataset.