Seminar Series

Automatically Detecting Negation and Speculation in Biomedical Text

Analysis of negation and speculation is an increasingly important NLP task which can enrich text mining applications by detecting hedged and negated statements. Our approach to this task begins with a maximum entropy classifier that identifies cues of negation/speculation using surface features. We then utilise two methods to detect the scope of such cues. Firstly we apply a set of heuristics that operate on dependency graph representations of sentences. Secondly we employ a data driven method to rank candidate scopes based on constituents in head-driven phrase structure grammar analyses. The effectiveness of the methods are evaluated using the BioScope corpus, a collection of biomedical texts annotated for negation and speculation.

[Joint work with Erik Velldal, Lilja Øverlid and Stephan Oepen]

Nominalisation and Discourse Relations

Discourse relations such as Cause, Sequence, Condition and so on have been standardly treated as holding between adjacent clauses, or text spans consisting of an integral sequence of clauses. Kibble (1999) and Danlos (2006) independently observed that rhetorical relations may also be realised as verbs which take nominalised propositions as arguments, in contrast to conventional analyses which only recognised "discourse connectives" as playing this role. Both studies offered formalisations using Asher and Lascarides' SDRT (2003). Kibble (1999), reporting a small corpus study using Patient Information Leaflets (PILS), hypothesized that clause-internal relations are limited to the "informational" or "semantic" subset of Mann and Thompson's RST repertoire (1987), while "intentional" or "presentational" relations invariably hold between clauses. However Power (2007) offers constructed examples involving both "intentional" relations such as Concession, Restatement, Summary in addition to "informational" relations. This talk will draw on new corpus studies to present evidence that the key factor is the nominalisation of propositional arguments which enables relations to be realised either as verbs or prepositions, in contrast to Power and Danlos who focus on verbs. I will also discuss recent work by my student Susan Lynch on automated recognition of nominalisations.

References:
Nicholas Asher and Alex Lascarides, 2003. Logics of Conversation.
Laurence Danlos, 2006. "Discourse verbs" and discourse periphrastic links, Proceedings of Constraints in Discourse Workshop, ed. Candy Sidner, John Harpur, Anton Benz and Peter Kuehnlein.
Rodger Kibble, 1999. Nominalisation and rhetorical structure, ESSLLI Formal Grammar Workshop, Utrecht.
William Mann and Sandra Thompson, 1987. Rhetorical structure theory: a theory of text organization. In L. Polanyi, ed., The Structure of Discourse.
Richard Power, 2007. Abstract verbs. Proceedings of INLG.

Natural Language Understanding in Google Research Zurich

In this talk I will describe the aims, scope and results of the Natural Language Understanding team in Google Research Zurich. Areas where we have experience include distributional similarities, open information extraction (learning attributes, values and relations for populating knowledge bases) and automatic text summarization. I will discuss real-world commercial applications, how we plan to put all these lines of work together around the topic of text summarization, and my vision of the future of the field.

To be confirmed