# On the Use of Parsing for Named Entity Recognition
## 3 Syntax and Semantic Parsing as Building Blocks for NLP Applications
### Recent Advancements in the Representation of Parsing Results  
The use of the linguistic structures resulting from parsing in NLP applications is not a trivial problem at all. This is in contrast to PoS tagging, for which the fact that PoS tags are simply a sequence of one tag per word, provides a highly standard and universal way of using such information in any neural NLP model: in the form of embeddings that are plugged as input to the network [116]. This makes it extremely easy to plug them into different models, regardless of architecture, or to try embeddings of different kinds of linguistic units (e.g., fine or coarse-grained PoS tags, lemmata, etc.). However, with constructions resulting from parsing, the situation is very different: since syntactic trees (unlike PoS tags) are structures that go beyond the linear order of the words in the sentence, it is not so obvious how they can be used in a way that takes full advantage of syntax while being modular and pluggable, i.e., not conditioning the rest of the model or requiring special resources.  
A classic way to use parsing is to extract features from parse trees (such as individual dependencies from dependency trees) and inject them into the application model, as [PERSON] and [PERSON] [117] or [PERSON] et al. [118] do with dependency trees for opinion mining. While this approach is relatively simple and pluggable, it does not really make full use of syntax as it is applying a lossy encoding of dependency trees into \"bags of dependencies,\" without regard for the overall structure or the relations between dependencies. In a much more involved approach, [PERSON] et al. [119] syntactically annotated a sentiment treebank to then train a recursive neural network that learns how to apply semantic composition for sentiment analysis. Although this does exploit syntax more fully, it has no modularity as it requires training a special ad hoc model, apart from requiring an ad hoc corpus where tree nodes are annotated with sentiment, a rarely available resource. [PERSON] et al. [120] forwent the need of special corpora by just computing a dependency parse of the target sentence and then applying handwritten rules to extract polarity from it, but, again, this is an ad hoc approach and cannot be used to improve existing models or to try different syntactic representations. In a similar way, several recent papers have used tree-specific encoders like graph convolutional networks [101]; but these are specialized models that are not easily pluggable.  
Recently, a new paradigm of _parsing as sequence labeling_ has arisen; in it, parsing is performed by encoding syntactic trees into sequences of one categorical label per word. This paradigm has been successfully applied to both constituency parsing [121; 122] and dependency parsing [123], or even both at once [124]. Apart from providing very fast and environmentally friendly [125] parsers for practical applications, this approach makes it possible to plug syntax into models in the same generic way as for PoS tags, as well as opening possibilities for multitask learning with other sequence labeling tasks, such as NER. Unfortunately, to the best of our knowledge, no sequence labeling approach is available for semantic parsing at the moment.