195 research outputs found
A comparison of parsing technologies for the biomedical domain
This paper reports on a number of experiments which are designed to investigate the extent to which current nlp resources are able to syntactically and semantically analyse biomedical text. We address two tasks: parsing a real corpus with a hand-built widecoverage grammar, producing both syntactic analyses and logical forms; and automatically computing the interpretation of compound nouns where the head is a nominalisation (e.g., hospital arrival means an arrival at hospital, while patient arrival means an arrival of a patient). For the former task we demonstrate that exible and yet constrained `preprocessing ' techniques are crucial to success: these enable us to use part-of-speech tags to overcome inadequate lexical coverage, and to `package up' complex technical expressions prior to parsing so that they are blocked from creating misleading amounts of syntactic complexity. We argue that the xml-processing paradigm is ideally suited for automatically preparing the corpus for parsing. For the latter task, we compute interpretations of the compounds by exploiting surface cues and meaning paraphrases, which in turn are extracted from the parsed corpus. This provides an empirical setting in which we can compare the utility of a comparatively deep parser vs. a shallow one, exploring the trade-o between resolving attachment ambiguities on the one hand and generating errors in the parses on the other. We demonstrate that a model of the meaning of compound nominalisations is achievable with the aid of current broad-coverage parsers
Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning
Compositional generalization is a basic mechanism in human language learning,
which current neural networks struggle with. A recently proposed Disentangled
sequence-to-sequence model (Dangle) shows promising generalization capability
by learning specialized encodings for each decoding step. We introduce two key
modifications to this model which encourage more disentangled representations
and improve its compute and memory efficiency, allowing us to tackle
compositional generalization in a more realistic setting. Specifically, instead
of adaptively re-encoding source keys and values at each time step, we
disentangle their representations and only re-encode keys periodically, at some
interval. Our new architecture leads to better generalization performance
across existing tasks and datasets, and a new machine translation benchmark
which we create by detecting naturally occurring compositional patterns in
relation to a training set. We show this methodology better emulates real-world
requirements than artificial challenges
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