3,369 research outputs found
Morphological Disambiguation by Voting Constraints
We present a constraint-based morphological disambiguation system in which
individual constraints vote on matching morphological parses, and
disambiguation of all the tokens in a sentence is performed at the end by
selecting parses that receive the highest votes. This constraint application
paradigm makes the outcome of the disambiguation independent of the rule
sequence, and hence relieves the rule developer from worrying about potentially
conflicting rule sequencing. Our results for disambiguating Turkish indicate
that using about 500 constraint rules and some additional simple statistics, we
can attain a recall of 95-96% and a precision of 94-95% with about 1.01 parses
per token. Our system is implemented in Prolog and we are currently
investigating an efficient implementation based on finite state transducers.Comment: 8 pages, Latex source. To appear in Proceedings of ACL/EACL'97
Compressed postscript also available as
ftp://ftp.cs.bilkent.edu.tr/pub/ko/acl97.ps.
Inducing Constraint Grammars
Constraint Grammar rules are induced from corpora. A simple scheme based on
local information, i.e., on lexical biases and next-neighbour contexts,
extended through the use of barriers, reached 87.3 percent precision (1.12
tags/word) at 98.2 percent recall. The results compare favourably with other
methods that are used for similar tasks although they are by no means as good
as the results achieved using the original hand-written rules developed over
several years time.Comment: 10 pages, uuencoded, gzipped PostScrip
Word sense disambiguation and information retrieval
It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval
(IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will
increase. However, recent research into the application of a word sense disambiguator to an IR system failed
to show any performance increase. From these results it has become clear that more basic research is needed
to investigate the relationship between sense ambiguity, disambiguation, and IR.
Using a technique that introduces additional sense ambiguity into a collection, this paper presents research
that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have
on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an IR system
when it is retrieving from very short queries. In addition we argue that if a word sense disambiguator is to
be of any use to an IR system, the disambiguator must be able to resolve word senses to a high degree of
accuracy
Word sense disambiguation and information retrieval
It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval
(IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will
increase. However, recent research into the application of a word sense disambiguator to an IR system failed
to show any performance increase. From these results it has become clear that more basic research is needed
to investigate the relationship between sense ambiguity, disambiguation, and IR.
Using a technique that introduces additional sense ambiguity into a collection, this paper presents research
that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have
on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an IR system
when it is retrieving from very short queries. In addition we argue that if a word sense disambiguator is to
be of any use to an IR system, the disambiguator must be able to resolve word senses to a high degree of
accuracy
Neighbourhood Consensus Networks
We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information
Processing Systems (NeurIPS 2018
Examining inter-sentential influences on predicted verb subcategorization
This study investigated the influences of prior discourse context and cumulative syntactic priming on readers' predictions for verb subcategorizations. An additional aim was to determine whether cumulative syntactic priming has the same degree of influence following coherent discourse contexts as when following series of unrelated sentences. Participants (N = 40) read sentences using a self-paced, sentence-by-sentence procedure. Half of these sentences comprised a coherent discourse context intended to increase the expectation for a sentential complement (S) completion. The other half consisted of scrambled sentences. The trials in both conditions varied according to the proportion of verbs that resolved to an S (either 6S or 2S). Following each condition, participants read temporarily ambiguous sentences that resolved to an S. Reading times across the disambiguating and postdisambiguating regions were measured. No significant main effects or interactions were found for either region. However, the lack of significant findings for these analyses may have been due to low power. In a follow-up analysis, data from each gender were analyzed separately. For the data contributed by males, there were no significant findings. For the data contributed by females, the effect of coherence was significant (by participants but not by items) across the postdisambiguating region, and there was a marginally significant interaction (p =.05) between coherence and frequency across this region suggesting that discourse-level information may differentially influence the local sentence processing of female and male participant
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