Code switching (CS) is a very common phenomenon in written and spoken
communication but one that is handled poorly by many natural language
processing applications. Looking to the application of building CS corpora, we
explore CS language identification (LID) for corpus building. We make the task
more realistic by scaling it to more languages and considering models with
simpler architectures for faster inference. We also reformulate the task as a
sentence-level multi-label tagging problem to make it more tractable. Having
defined the task, we investigate three reasonable models for this task and
define metrics which better reflect desired performance. We present empirical
evidence that no current approach is adequate and finally provide
recommendations for future work in this area.

通过建立代码切换语料库的应用，我们研究了代码切换语言识别，考虑到多种语言和较简单的模型架构以实现更快速推理。我们将任务重新定义为句子级多标签标注问题，以使其更易处理，并提出反映所需性能的度量指标。我们通过实证实现表明当前的方法都不足够，并在这一领域提供未来工作的建议。

代码切换语言识别更加困难

Code-Switched Language Identification is Harder Than You Think

Named Entity Recognition (NER) or the extraction of concepts from clinical
text is the task of identifying entities in text and slotting them into
categories such as problems, treatments, tests, clinical departments,
occurrences (such as admission and discharge) and others. NER forms a critical
component of processing and leveraging unstructured data from Electronic Health
Records (EHR). While identifying the spans and categories of concepts is itself
a challenging task, these entities could also have attributes such as negation
that pivot their meanings implied to the consumers of the named entities. There
has been little research dedicated to identifying the entities and their
qualifying attributes together. This research hopes to contribute to the area
of detecting entities and their corresponding attributes by modelling the NER
task as a supervised, multi-label tagging problem with each of the attributes
assigned tagging sequence labels. In this paper, we propose 3 architectures to
achieve this multi-label entity tagging: BiLSTM n-CRF, BiLSTM-CRF-Smax-TF and
BiLSTM n-CRF-TF. We evaluate these methods on the 2010 i2b2/VA and the i2b2
2012 shared task datasets. Our different models obtain best NER F1 scores of 0.
894 and 0.808 on the i2b2 2010/VA and i2b2 2012 respectively. The highest span
based micro-averaged F1 polarity scores obtained were 0.832 and 0.836 on the
i2b2 2010/VA and i2b2 2012 datasets respectively, and the highest
macro-averaged F1 polarity scores obtained were 0.924 and 0.888 respectively.
The modality studies conducted on i2b2 2012 dataset revealed high scores of
0.818 and 0.501 for span based micro-averaged F1 and macro-averaged F1
respectively.

本研究提出了三种结构以实现多标签的命名实体识别，这三种方法是 BiLSTM n-CRF、BiLSTM-CRF-Smax-TF 和 BiLSTM n-CRF-TF。通过评估在 i2b2/VA 2010 和 i2b2 2012 共享任务数据集上的不同模型，本研究证明这些方法可以获得较高的准确性。