# On the Use of Parsing for Named Entity Recognition
## 7 Conclusions  
Written text is the fundamental element by which human beings record their ideas, desires, aspirations, creations, and the events that occur in their environment; it is ultimately the main medium by which knowledge is transmitted. A clear example of this can be found in articles published in scientific journals like this one. The huge amount of text that is currently generated on a daily basis makes its manual examination unfeasible, making it necessary to create automatic tools, that is, NLP tools, to extract knowledge from it. NER emerges as a basic NLP task for this purpose. NER is a difficult task. Probably because of this, most systems have opted for an approach based on sequence labeling that makes a limited use of the inherent structure of text. Although this approach has not been able to solve the NER task, it has managed to yield systems with sufficient performance to be applicable in practice. In particular, recent developments in neural architectures have allowed us to increase sequence labeling performance in NLP tasks [27, 35, 156, 157, 158], in part due to the use of contextualized embeddings from language models like Embeddings from Language Models (ELMo) or BERT [159, 160]. However, to continue improving the performance of NER systems, it is necessary to incorporate the information provided by the techniques that can analyze, process, and elaborate the structural information of sentences--in other words, parsing. Throughout this article we have shown how NER systems that use parsing information manage to improve over the results of those that do not use it, and how improvements in parsing techniques in recent years allow a smoother and more efficient incorporation of structural information to NER systems. Regarding the future evolution of the integration of syntactic and semantic information in NER systems, we advise to cast parsing itself as a sequence labeling task, making it much more straightforward to integrate with NER. This way, we will be able to use complete syntactic trees, while at the same time not having to resort to non-standard architectures and retaining the simplicity, genericity, and efficiency of a sequence labeling architecture for NER. This approach can also be used to apply the parsing component of a NER system in a multilingual setting, thanks to the availability of Universal Dependencies ([[https://universaldependencies.org/](https://universaldependencies.org/)]([https://universaldependencies.org/](https://universaldependencies.org/))) (UD), a unified parsing framework that currently supports 111 languages, with 32 more to be added soon. For example, this approach can be applied to incorporate a parsing component to a NER system in Arabic [161], Persian [162], or French [163] by training a parser with the UD treebank for each of those languages. Moreover, the strategy used in [120, 164] to build multilingual sentiment analysis systems can be applied toward building truly multilingual NER systems. Finally, we would like to point out what we consider to be the main challenges for the successful application of parsing in NER systems:  
* A standard framework for NER resources. Although the availability of UD makes it possible to have a multilingual parsing component with common annotation criteria across languages, the same is not the case for the rest of components of a NER system. The NER community needs to move in this direction, which will also facilitate the creation of truly multi-domain NER systems. * Larger data sets. The deep learning techniques that currently represent the state of the art in both parsing and NER require the largest possible data sets to exploit their full potential. * Semantic parsing. The latest developments in syntactic parsing indicate that the proposed approach is fast enough to be applicable in large-scale NER systems, as well as accurate enough to provide useful information for the task. It is still unknown whether similar performance can be achievable with respect to semantic parsing, although the prospects are encouraging.