11,607 research outputs found
Inferring Sentence-internal Temporal Relations
In this paper we propose a data intensive approach for inferring sentence-internal temporal relations, which relies on a simple probabilistic model and assumes no manual coding
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
The Generalised Liar Paradox: A Quantum Model and Interpretation
The formalism of abstracted quantum mechanics is applied in a model of the
generalized Liar Paradox. Here, the Liar Paradox, a consistently testable
configuration of logical truth properties, is considered a dynamic conceptual
entity in the cognitive sphere. Basically, the intrinsic contextuality of the
truth-value of the Liar Paradox is appropriately covered by the abstracted
quantum mechanical approach. The formal details of the model are explicited
here for the generalized case. We prove the possibility of constructing a
quantum model of the m-sentence generalizations of the Liar Paradox. This
includes (i) the truth-falsehood state of the m-Liar Paradox can be represented
by an embedded 2m-dimensional quantum vector in a (2m)^m dimensional complex
Hilbert space, with cognitive interactions corresponding to projections, (ii)
the construction of a continuous 'time' dynamics is possible: typical truth and
falsehood value oscillations are described by Schrodinger evolution, (iii)
Kirchoff and von Neumann axioms are satisfied by introduction of 'truth-value
by inference' projectors, (iv) time invariance of unmeasured state.Comment: 13 pages, to be published in Foundations of Scienc
EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria.
Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria
What does semantic tiling of the cortex tell us about semantics?
Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions
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