# On the Use of Parsing for Named Entity Recognition
## 5 Discussion  
Parsing makes it possible to represent the structure of a text and has been repeatedly shown to be useful for improving NER accuracy. Table 1 compiles a summary of the main characteristics of the most relevant NER systems that make use of information derived from parsing. As we explained in the previous section, existing approaches have been limited by the difficulty of integrating hierarchical information such as a parse tree into a task that is linear in nature. Thus, either they make limited use of such syntactic information [128; 129], or they develop ad hoc architectures that result in more complex, less generic, and less efficient models [95; 136; 137]. What we know for sure is that the use of information from parsers is beneficial but, since they have been tested on different data sets, it is difficult to determine which of those approaches for incorporating parsing information is more effective in general terms. Paradoxically, this issue stems from the success of the NER task: In recent years NER has been applied to so many domains that it has been necessary to create at least one data set for each of them, which has the pernicious effect that the research community has dispersed, with researchers creating systems to work effectively in a particular domain. At this point, a promising line of research that has not been tried yet is to integrate the hierarchical information provided by parsing processes into a linear setting by casting syntactic parsing itself as a sequence labeling task [121; 123]. Until recently, full syntactic parsing was considered infeasible in practice within the sequence labeling framework: While it was theoretically possible to cast it as sequence labeling, learning algorithms like averaged perceptron or CRF were not powerful enough to achieve practical results. As an example, [PERSON] and [PERSON] [138] presented a sequence labeling approach to dependency parsing as early as 2010, but they reported accuracies 5%-10% behind the state of the art of the time. Thus, while their work was an interesting exploration and proof of concept, it could hardly be considered a competitive system. It was not until recent years, with the popularization of dense vector representations of linguistic units (embeddings [139]) and the use of recurrent neural networks (especially BiLSTMs) to enrich these representations with context information [140], that generic sequence labeling models started being capable of doing full syntactic parsing. In a pioneering work, [PERSON] and [PERSON] [121] introduced an encoding to represent any constituency tree for a sentence of length \(n\) as a sequence of \(n\) labels. Several sequence labeling architectures were tried, showing that BiLSTMs were capable of achieving good parsing accuracy (and  
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very fast speeds) where simpler architectures failed. In later work [123], the same approach was tried with dependency parsing, exploring four different ways to cast the problem as sequence labeling and achieving competitive accuracies with two of them, including the one where [PERSON] and [PERSON] [138] had previously obtained impractical results using pre-neural techniques. This new paradigm of parsing as sequence labeling will be usable not only to integrate deep syntactic trees with NER, but to do it in such a way that we will effectively use the full syntactic information without needing to forgo the standard sequence labeling architectures of NER. Thus, we will obtain NER systems that are fast, scalable, and easily integrable with upstream tasks while also boosting accuracy, thanks to the use of deep syntax. This approach can also be extended to semantic parsing, which generates meaning representations that go beyond syntax trees. However, this will require a reduction of semantic parsing to sequence labeling. Some NER systems, notably [129], resort to pre-trained language models.