title: Syntax-driven argument identification and multi-argument classification for semantic role labeling

authors: Lin, Chi-San Althon

subjects: Semantic Role Labeling

abstract: Semantic role labeling is an important stage in systems for Natural Language Understanding.  The basic problem is one of identifying who did what to whom for each predicate in a sentence.  Thus labeling is a two-step process: identify constituent phrases that are arguments to a predicate, then label those arguments with appropriate thematic roles.

Existing systems for semantic role labeling use machine learning methods to assign roles one-at-a-time to candidate arguments.  There are several drawbacks to this general approach.  First, more than one candidate can be assigned the same role, which is undesirable.  Second, the search for each candidate argument is exponential with respect to the number of words in the sentence.  Third, single-role assignment cannot take advantage of dependencies known to exist between semantic roles of predicate arguments, such as their relative juxtaposition.  And fourth, execution times for existing algorithm are excessive, making them unsuitable for real-time use.

This thesis seeks to obviate these problems by approaching semantic role labeling as a multi-argument classification process.  It observes that the only valid arguments to a predicate are unembedded constituent phrases that do not overlap that predicate.  Given that semantic role labeling occurs after parsing, this thesis proposes an algorithm that systematically traverses the parse tree when looking for arguments, thereby eliminating the vast majority of impossible candidates.   

Moreover, instead of assigning semantic roles one at a time, an algorithm is proposed to assign all labels simultaneously; leveraging dependencies between roles and eliminating the problem of duplicate assignment.

Experimental results are provided as evidence to show that a combination of the proposed argument identification and multi-argument classification algorithms outperforms all existing systems that use the same syntactic information.

text: Syntax-driven argument identification and multi-argument classification for semantic role labeling Semantic role labeling is an important stage in systems for Natural Language Understanding.  The basic problem is one of identifying who did what to whom for each predicate in a sentence.  Thus labeling is a two-step process: identify constituent phrases that are arguments to a predicate, then label those arguments with appropriate thematic roles.

Existing systems for semantic role labeling use machine learning methods to assign roles one-at-a-time to candidate arguments.  There are several drawbacks to this general approach.  First, more than one candidate can be assigned the same role, which is undesirable.  Second, the search for each candidate argument is exponential with respect to the number of words in the sentence.  Third, single-role assignment cannot take advantage of dependencies known to exist between semantic roles of predicate arguments, such as their relative juxtaposition.  And fourth, execution times for existing algorithm are excessive, making them unsuitable for real-time use.

This thesis seeks to obviate these problems by approaching semantic role labeling as a multi-argument classification process.  It observes that the only valid arguments to a predicate are unembedded constituent phrases that do not overlap that predicate.  Given that semantic role labeling occurs after parsing, this thesis proposes an algorithm that systematically traverses the parse tree when looking for arguments, thereby eliminating the vast majority of impossible candidates.   

Moreover, instead of assigning semantic roles one at a time, an algorithm is proposed to assign all labels simultaneously; leveraging dependencies between roles and eliminating the problem of duplicate assignment.

Experimental results are provided as evidence to show that a combination of the proposed argument identification and multi-argument classification algorithms outperforms all existing systems that use the same syntactic information.

year: 2007

title: Editorial: Non-themed issue: 2010

authors: Locke, Terry

abstract: The decision to have regular non-themed issues of English Teaching: Practice and Critique was made by way of Board consultation some time ago. As a Board, we believe that the policy of having a panel of guest editors taking control of a “themed” issue has worked well. In many cases, guess editors have worked together for the first time in a common enterprise. In all cases, having panels of guest editors has expanded the reach of the journal, increasing its subscriber base and the number of distinct educational constituencies who view the journal as a desirable target for contributions. It has facilited the journal’s aim of providing “a place where authors from a range of backgrounds can identify matters of common concern and thereby foster professional communities and networks”.

text: Editorial: Non-themed issue: 2010 The decision to have regular non-themed issues of English Teaching: Practice and Critique was made by way of Board consultation some time ago. As a Board, we believe that the policy of having a panel of guest editors taking control of a “themed” issue has worked well. In many cases, guess editors have worked together for the first time in a common enterprise. In all cases, having panels of guest editors has expanded the reach of the journal, increasing its subscriber base and the number of distinct educational constituencies who view the journal as a desirable target for contributions. It has facilited the journal’s aim of providing “a place where authors from a range of backgrounds can identify matters of common concern and thereby foster professional communities and networks”.

year: 2010

title: Taking Taniwha seriously

authors: Kingsbury, Justine

subjects: intercultural understanding

abstract: Taniwha are powerful water creatures in te ao Māori (the Māori world/worldview). Taniwha sometimes afect public works in Aotearoa New Zealand: for example, consultation between government agencies and tangata whenua (the people of the land) about proposed roading developments sometimes results in the route being moved to avoid the dwelling place of a taniwha. Mainstream media responses have tended to be hostile or mocking, as you might expect, since on the face of it the dominant western scientifc worldview has no place for beings like taniwha. However, in the 2020s, there appears to be an increased willingness to engage with te ao Māori. In this spirit, this paper proposes a way for non-Māori to begin to take taniwha more seriously, taking as its starting point the work of Dan Hikuroa on the practical usefulness of taniwha pūrākau (traditional narratives) in encoding information about natural hazards. The focus of this paper is narrow, but aspects of the strategy it proposes may be generalisable both to other aspects of te ao Māori and to other bicultural and multicultural contexts.

text: Taking Taniwha seriously Taniwha are powerful water creatures in te ao Māori (the Māori world/worldview). Taniwha sometimes afect public works in Aotearoa New Zealand: for example, consultation between government agencies and tangata whenua (the people of the land) about proposed roading developments sometimes results in the route being moved to avoid the dwelling place of a taniwha. Mainstream media responses have tended to be hostile or mocking, as you might expect, since on the face of it the dominant western scientifc worldview has no place for beings like taniwha. However, in the 2020s, there appears to be an increased willingness to engage with te ao Māori. In this spirit, this paper proposes a way for non-Māori to begin to take taniwha more seriously, taking as its starting point the work of Dan Hikuroa on the practical usefulness of taniwha pūrākau (traditional narratives) in encoding information about natural hazards. The focus of this paper is narrow, but aspects of the strategy it proposes may be generalisable both to other aspects of te ao Māori and to other bicultural and multicultural contexts.

year: 2022-11-10