2,416 research outputs found
Machine-learning-based vs. manually designed approaches to anaphor resolution: the best of two worlds
In the last years, much effort went into the design of robust anaphor resolution algorithms. Many algorithms are based on antecedent filtering and preference strategies that are manually designed. Along a different line of research, corpus-based approaches have been investigated that employ machine-learning techniques for deriving strategies automatically. Since the knowledge-engineering effort for designing and optimizing the strategies is reduced, the latter approaches are considered particularly attractive. Since, however, the hand-coding of robust antecedent filtering strategies such as syntactic disjoint reference and agreement in person, number, and gender constitutes a once-for-all effort, the question arises whether at all they should be derived automatically. In this paper, it is investigated what might be gained by combining the best of two worlds: designing the universally valid antecedent filtering strategies manually, in a once-for-all fashion, and deriving the (potentially genre-specific) antecedent selection strategies automatically by applying machine-learning techniques. An anaphor resolution system ROSANA-ML, which follows this paradigm, is designed and implemented. Through a series of formal evaluations, it is shown that, while exhibiting additional advantages, ROSANAML reaches a performance level that compares with the performance of its manually designed ancestor ROSANA
OntoAna: Domain Ontology for Human Anatomy
Today, we can find many search engines which provide us with information
which is more operational in nature. None of the search engines provide domain
specific information. This becomes very troublesome to a novice user who wishes
to have information in a particular domain. In this paper, we have developed an
ontology which can be used by a domain specific search engine. We have
developed an ontology on human anatomy, which captures information regarding
cardiovascular system, digestive system, skeleton and nervous system. This
information can be used by people working in medical and health care domain.Comment: Proceedings of 5th CSI National Conference on Education and Research.
Organized by Lingayay University, Faridabad. Sponsored by Computer Society of
India and IEEE Delhi Chapter. Proceedings published by Lingayay University
Pres
Comparing knowledge sources for nominal anaphora resolution
We compare two ways of obtaining lexical knowledge for antecedent selection in other-anaphora
and definite noun phrase coreference. Specifically, we compare an algorithm that relies on links
encoded in the manually created lexical hierarchy WordNet and an algorithm that mines corpora
by means of shallow lexico-semantic patterns. As corpora we use the British National
Corpus (BNC), as well as the Web, which has not been previously used for this task. Our
results show that (a) the knowledge encoded in WordNet is often insufficient, especially for
anaphor-antecedent relations that exploit subjective or context-dependent knowledge; (b) for
other-anaphora, the Web-based method outperforms the WordNet-based method; (c) for definite
NP coreference, the Web-based method yields results comparable to those obtained using
WordNet over the whole dataset and outperforms the WordNet-based method on subsets of the
dataset; (d) in both case studies, the BNC-based method is worse than the other methods because
of data sparseness. Thus, in our studies, the Web-based method alleviated the lexical knowledge
gap often encountered in anaphora resolution, and handled examples with context-dependent relations
between anaphor and antecedent. Because it is inexpensive and needs no hand-modelling
of lexical knowledge, it is a promising knowledge source to integrate in anaphora resolution systems
Focusing for Pronoun Resolution in English Discourse: An Implementation
Anaphora resolution is one of the most active research areas in natural
language processing. This study examines focusing as a tool for the resolution
of pronouns which are a kind of anaphora. Focusing is a discourse phenomenon
like anaphora. Candy Sidner formalized focusing in her 1979 MIT PhD thesis and
devised several algorithms to resolve definite anaphora including pronouns. She
presented her theory in a computational framework but did not generally
implement the algorithms. Her algorithms related to focusing and pronoun
resolution are implemented in this thesis. This implementation provides a
better comprehension of the theory both from a conceptual and a computational
point of view. The resulting program is tested on different discourse segments,
and evaluation and analysis of the experiments are presented together with the
statistical results.Comment: iii + 49 pages, compressed, uuencoded Postscript file; revised
version of the first author's Bilkent M.S. thesis, written under the
supervision of the second author; notify Akman via e-mail
([email protected]) or fax (+90-312-266-4126) if you are unable to
obtain hardcopy, he'll work out somethin
Anaphor resolution and the scope of syntactic constraints
An anaphor resolution algorithm is presented which relies on a combination of strategies for narrowing down and selecting from antecedent sets for re exive pronouns, nonre exive pronouns, and common nouns. The work focuses on syntactic restrictions which are derived from Chomsky's Binding Theory. It is discussed how these constraints can be incorporated adequately in an anaphor resolution algorithm. Moreover, by showing that pragmatic inferences may be necessary, the limits of syntactic restrictions are elucidated
Design and enhanced evaluation of a robust anaphor resolution algorithm
Syntactic coindexing restrictions are by now known to be of central importance to practical anaphor resolution approaches. Since, in particular due to structural ambiguity, the assumption of the availability of a unique syntactic reading proves to be unrealistic, robust anaphor resolution relies on techniques to overcome this deficiency.
This paper describes the ROSANA approach, which generalizes the verification of coindexing restrictions in order to make it applicable to the deficient syntactic descriptions that are provided by a robust state-of-the-art parser. By a formal evaluation on two corpora that differ with respect to text genre and domain, it is shown that ROSANA achieves high-quality robust coreference resolution. Moreover, by an in-depth analysis, it is proven that the robust implementation of syntactic disjoint reference is nearly optimal. The study reveals that, compared with approaches that rely on shallow preprocessing, the largely nonheuristic disjoint reference algorithmization opens up the possibility/or a slight improvement. Furthermore, it is shown that more significant gains are to be expected elsewhere, particularly from a text-genre-specific choice of preference strategies.
The performance study of the ROSANA system crucially rests on an enhanced evaluation methodology for coreference resolution systems, the development of which constitutes the second major contribution o/the paper. As a supplement to the model-theoretic scoring scheme that was developed for the Message Understanding Conference (MUC) evaluations, additional evaluation measures are defined that, on one hand, support the developer of anaphor resolution systems, and, on the other hand, shed light on application aspects of pronoun interpretation
Automatic case acquisition from texts for process-oriented case-based reasoning
This paper introduces a method for the automatic acquisition of a rich case
representation from free text for process-oriented case-based reasoning. Case
engineering is among the most complicated and costly tasks in implementing a
case-based reasoning system. This is especially so for process-oriented
case-based reasoning, where more expressive case representations are generally
used and, in our opinion, actually required for satisfactory case adaptation.
In this context, the ability to acquire cases automatically from procedural
texts is a major step forward in order to reason on processes. We therefore
detail a methodology that makes case acquisition from processes described as
free text possible, with special attention given to assembly instruction texts.
This methodology extends the techniques we used to extract actions from cooking
recipes. We argue that techniques taken from natural language processing are
required for this task, and that they give satisfactory results. An evaluation
based on our implemented prototype extracting workflows from recipe texts is
provided.Comment: Sous presse, publication pr\'evue en 201
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