Supporting Annotation Layers for Natural Language Processing

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Supporting Annotation Layers for Natural Language
Processing

• Preslav Nakov, Ariel Schwartz, Brian Wolf, Marti
  Hearst
• Computer Science Division and SIMSUniversity of
  California, Berkeley http//biotext.berkeley.edu

Supported by NSF DBI-0317510 and a gift from
Genentech
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Project overview

• A system for flexible querying of text that has
  been annotated with the results of NLP
  processing.
• Supports
• self-overlapping and parallel layers,
• integration of syntactic and ontological
  hierarchies,
• and tight integration with SQL.
• Designed to scale to very large corpora.
• Demo of LQL (Layered Query Language) on examples
  taken from the NLP literature.

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Key Contributions

• Multiple overlapping layers (cannot be expressed
  in a single XML file)
• Self-overlapping, parallel layers, allowing
  multiple syntactic parses of the same text
• Integration of multiple intersecting hierarchies
  (e.g. MeSH, UMLS, Wordnet)
• Specialized query language
• Flexible results format
• Focused on scaling annotation-based queries to
  very large corpora (millions of documents) with
  many layers of annotations
• 1.4 million MEDLINE abstracts
• 10 million sentences annotated
• 320 million multi-layered annotations
• 70 GB database size.

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Layers of Annotations

• Each annotation represents an interval spanning a
  sequence of characters
• absolute start and end positions
• Each layer corresponds to a conceptually
  different kind of annotation
• Layers can be
• Sequential
• Overlapping (e.g., two multiple-word concepts
  sharing a word)
• Hierarchical
• spanning, when the intervals are nested as in a
  parse tree, or
• ontologically, when the token itself is derived
  from a hierarchical ontology

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Annotation Layers Example
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System Architecture(Main table)
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System Architecture(Indexes)

• (Forward) doc_idsectionlayer_idsentencefirst_
  word_poslast_word_postag_type
• (Inverted) layer_idtag_typedoc_idsectionsente
  ncefirst_word_poslast_word_pos
• (Inverted) word_idlayer_idtag_typedoc_idsecti
  onsentencefirst_word_pos

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Example query I

• Protein-Protein Interactions
• Goal Find all sentences that consist of a noun
  phrase containing a gene followed by a
  morphological variant of the verb activate,
  inhibit, or bind, followed by another NP
  containing a gene.

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Example query I - LQL

• SELECT p1_text, verb_content, p2_text, COUNT()
  AS cnt
• FROM (
• BEGIN_LQL
• layer'sentence' ALLOW GAPS
• layer'shallow_parse' tag_name'NP'
• layer'gene'
• AS p1
• layer'pos' tag_name"verb"
• (content "activate" content
  "inhibit" content "bind")
• AS verb
• layer'shallow_parse' tag_name'NP'
• layer'gene'
• AS p2
• SELECT p1.text AS p1_text, verb.content AS
  verb_content, p2.text AS p2_text
• END_LQL
• ) lql
• GROUP BY p1_text, verb_content, p2_text
• ORDER BY count() DESC

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Example query I Sample output
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Example query II

• ChemicalDisease Interactions
• Adherence to statin prevents one coronary heart
  disease event for every 429 patients.
• Goal extract the relation that statin
  (potentially) prevents coronary heart disease.
• MeSH C subtree contains diseases
• MeSH supplementary concepts represent chemicals.

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Example query II - LQL

• layer'sentence' NO ORDER, ALLOW GAPS
  layer'shallow_parse' tag_name'NP
  layer'chemicals' AS chemical
  layer'shallow_parse' tag_name'NP'
  layer'mesh' tree_number 'C' AS disease
  AS sent SELECT sent.pmid,
  chemical.text, disease.text, sent.text