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# GRAIL---A Generalized Representation and Aggregation of Information Layers
Sameer Pradhan (he/him)
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The human brain receives various signals that it assimilates (filters,
splices, corrects, etc.) to build a syntactic structure and its semantic
interpretation. This is a complex process that enables human communication.
The field of artificial intelligence (AI) is devoted to studying how we
generate symbols and derive meaning from such signals and to building
predictive models that allow effective human-computer interaction.
For the purpose of this talk we will limit the scope of signals to the
domain to language—text and speech. Computational Linguistics (CL),
a.k.a. Natural Language Processing (NLP), is a sub-area of AI that tries to
interpret them. It involves modeling and predicting complex linguistic
structures from these signals. These models tend to rely heavily on a large
amount of \`\`raw'' (naturally occurring) data and a varying amount of
(manually) enriched data, commonly known as \`\`annotations''. The models are
only as good as the quality of the annotations. Owing to the complex and
numerous nature of linguistic phenomena, a divide and conquer approach is
common. The upside is that it allows one to focus on one, or few, related
linguistic phenomena. The downside is that the universe of these phenomena
keeps expanding as language is context sensitive and evolves over time. For
example, depending on the context, the word \`\`bank'' can refer to a financial
institution, or the rising ground surrounding a lake, or something else. The
verb \`\`google'' did not exist before the company came into being.
Manually annotating data can be a very task specific, labor intensive,
endeavor. Owing to this, advances in multiple modalities have happened in
silos until recently. Recent advances in computer hardware and machine
learning algorithms have opened doors to interpretation of multimodal data.
However, the need to piece together such related but disjoint predictions
poses a huge challenge.
This brings us to the two questions that we will try to address in this
talk:
1. How can we come up with a unified representation of data and annotations that encompasses arbitrary levels of linguistic information? and,
2. What role might Emacs play in this process?
Emacs provides a rich environment for editing and manipulating recursive
embedded structures found in programming languages. Its view of text,
however, is more or less linear–strings broken into words, strings ended by
periods, strings identified using delimiters, etc. It does not assume
embedded or recursive structure in text. However, the process of interpreting
natural language involves operating on such structures. What if we could
adapt Emacs to manipulate rich structures derived from text? Unlike
programming languages, which are designed to be parsed and interpreted
deterministically, interpretation of statements in natural languages has to
frequently deal with phenomena such as ambiguity, inconsistency,
incompleteness, etc. and can get quite complex.
We present an architecture (GRAIL) which utilizes the capabilities of Emacs
to allow the representation and aggregation of such rich structures in
a systematic fashion. Our approach is not tied to Emacs, but uses its many
built-in capabilities for creating and evaluating solution prototypes.
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[[!taglink CategoryLinguistics]]
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