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|Title: ||Resolving Other-Anaphora|
|Authors: ||Nygren Modjeska, Natalia|
|Supervisor(s): ||Webber, Bonnie|
|Issue Date: ||Jul-2004|
|Publisher: ||University of Edinburgh. College of Science and Engineering. School of Informatics.|
|Abstract: ||Reference resolution is a major component of any natural language system. In the past
30 years significant progress has been made in coreference resolution. However, there
is more anaphora in texts than coreference. I present a computational treatment of
other-anaphora, i.e., referential noun phrases (NPs) with non-pronominal heads modi-
fied by “other” or “another”:
[. . . ] the move is designed to more accurately reflect the value of products
and to put steel on more equal footing with other commodities.
Such NPs are anaphoric (i.e., they cannot be interpreted in isolation), with an antecedent
that may occur in the previous discourse or the speaker’s and hearer’s mutual
knowledge. For instance, in the example above, the NP “other commodities” refers to
a set of commodities excluding steel, and it can be paraphrased as “commodities other
Resolving such cases requires first identifying the correct antecedent(s) of the
other-anaphors. This task is the major focus of this dissertation. Specifically, the
dissertation achieves two goals. First, it describes a procedure by which antecedents
of other-anaphors can be found, including constraints and preferences which narrow
down the search. Second, it presents several symbolic, machine learning and hybrid
resolution algorithms designed specifically for other-anaphora. All the algorithms have
been implemented and tested on a corpus of examples from the Wall Street Journal.
The major results of this research are the following:
1. Grammatical salience plays a lesser role in resolving other-anaphors than in resolving
pronominal anaphora. Algorithms that solely rely on grammatical features
achieved worse results than algorithms that used semantic features as well.
2. Semantic knowledge (such as “steel is a commodity”) is crucial in resolving
other-anaphors. Algorithms that operate solely on semantic features outperformed
those that operate on grammatical knowledge.
3. The quality and relevance of the semantic knowledge base is important to success.
WordNet proved insufficient as a source of semantic information for resolving
other-anaphora. Algorithms that use the Web as a knowledge base achieved better performance than those using WordNet, because the Web contains domain specific
and general world knowledge which is not available from WordNet.
4. But semantic information by itself is not sufficient to resolve other-anaphors, as
it seems to overgenerate, leading to many false positives.
5. Although semantic information is more useful than grammatical information,
only integration of semantic and grammatical knowledge sources can handle the
full range of phenomena. The best results were obtained from a combination of
semantic and grammatical resources.
6. A probabilistic framework is best at handling the full spectrum of features, both
because it does not require commitment as to the order in which the features
should be applied, and because it allows features to be treated as preferences,
rather than as absolute constraints.
7. A full resolution procedure for other-anaphora requires both a probabilistic model
and a set of informed heuristics and back-off procedures. Such a hybrid system
achieved the best results so far on other-anaphora.|
|Description: ||Institute for Communicating and Collaborative Systems|
|Sponsor(s): ||This research was made possible through funding from the Engineering and Physical
Sciences Research Council (grant GR/M75129) in the UK.|
|Appears in Collections:||Informatics thesis and dissertation collection|
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