4,023 research outputs found
Parsing Using the Role and Reference Grammar Paradigm
Much effort has been put into finding ways of parsing natural language. Role and Reference Grammar (RRG) is a linguistic paradigm that has credibility in linguistic circles. In this paper we give a brief overview of RRG and show how this can be implemented into a standard rule-based parser. We used the chart parser to test the concept on sentences from student work. We present results that show the potential role of this method for parsing ungrammatical sentences
Efficient deep processing of japanese
We present a broad coverage Japanese grammar written in the HPSG formalism with MRS semantics. The grammar is created for use in real world applications, such that robustness and performance issues play an important role. It is connected to a POS tagging and word segmentation tool. This grammar is being developed in a multilingual context, requiring MRS structures that are easily comparable across languages
Learning Sentence-internal Temporal Relations
In this paper we propose a data intensive approach for inferring
sentence-internal temporal relations. Temporal inference is relevant for
practical NLP applications which either extract or synthesize temporal
information (e.g., summarisation, question answering). Our method bypasses the
need for manual coding by exploiting the presence of markers like after", which
overtly signal a temporal relation. We first show that models trained on main
and subordinate clauses connected with a temporal marker achieve good
performance on a pseudo-disambiguation task simulating temporal inference
(during testing the temporal marker is treated as unseen and the models must
select the right marker from a set of possible candidates). Secondly, we assess
whether the proposed approach holds promise for the semi-automatic creation of
temporal annotations. Specifically, we use a model trained on noisy and
approximate data (i.e., main and subordinate clauses) to predict
intra-sentential relations present in TimeBank, a corpus annotated rich
temporal information. Our experiments compare and contrast several
probabilistic models differing in their feature space, linguistic assumptions
and data requirements. We evaluate performance against gold standard corpora
and also against human subjects
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