Difference: ThesisAbstracts (1 vs. 8)

Revision 82010-11-19 - gslayden

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Lab member thesis topics

Changed:
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<		Please add a short description of your thesis (MA or PhD) here, with a date indicating when the description was last added/modified.
>
>		Please add a short description of your thesis (MA or PhD) here, with a date indicating when the description was last added/modified.
 

Master's students

Utilizing Multilingual Resources for Automatic Lexical Acquisition (MA)

Michael Wayne Goodman

Deleted:
<
<		I'm investigating how we can leverage the knowledge built into the lexicons of large, mature grammars to help bootstrap the lexicons of much smaller grammars. For my test, I am using the Jacy Japanese grammar as the source and the Ita Italian MMT grammar as the target. I am using the Turing Center's Transgraph project to provide word translations, and some hand-built type mappings from one grammar to the other to figure out the types a word can have. Because of the nature of the project, many spurious items are produced, so I need to apply some filtering to the data to try and remove them. Another aspect of the project is to try and automatically learn transfer rules between the grammars involved. This becomes difficult when source words do not transfer to a single target word, when they change argument structure, etc. (updated 2008.06.27)
 
Added:
>
>		I'm investigating how we can leverage the knowledge built into the lexicons of large, mature grammars to help bootstrap the lexicons of much smaller grammars. For my test, I am using the Jacy Japanese grammar as the source and the Ita Italian MMT grammar as the target. I am using the Turing Center's Transgraph project to provide word translations, and some hand-built type mappings from one grammar to the other to figure out the types a word can have. Because of the nature of the project, many spurious items are produced, so I need to apply some filtering to the data to try and remove them. Another aspect of the project is to try and automatically learn transfer rules between the grammars involved. This becomes difficult when source words do not transfer to a single target word, when they change argument structure, etc. (updated 2008.06.27)

Glenn Slayden

I am developing a new grammar engineering environment for DELPH-IN style TDL grammars. Building upon a considerable repertoire of established techniques in unification parsing, the work explores ideas which have become more relevant in today's computing ecosystem, such as low-lock concurrent chart parsing and cache-friendly TFS representation. For details on the project, please see http://wiki.delph-in.net/moin/TgcsTop .

-- Main.gslayden - 2010-11-19

 

Generating Referring Expressions (MA)

Margaret Ann Mitchell

Deleted:
<
<		I'm exploring the problem of how to refer to entities naturally. This is a sub-task within natural language generation, mapping nonlinguistic data to a linguistic output. My focus is primarily on creating distinguishing descriptions, ie, given a set of objects from which one object is selected, what noun phrase will be used to refer to it? Current approaches are based off Dale & Reiter's Incremental Algorithm, which uses the Gricean Maxims as a guide to naturalness, but these maxims are prescriptive, not descriptive, and fail to capture what humans actually do. I intend to do some data-mining to create a basic preference ordering for adjectives, and use this to propose a new algorithm that better captures human referring expression generation. I also want to touch on which types of referring expressions are used in which contexts, in an attempt to help natural language generators decide how to refer to entities in a stream of text. The domain will probably be limited to the Wall Street Journal. (updated 2008.04.02)
 
Changed:
<
<		-- Main.itallow - 02 Apr 2008
>
>		I'm exploring the problem of how to refer to entities naturally. This is a sub-task within natural language generation, mapping nonlinguistic data to a linguistic output. My focus is primarily on creating distinguishing descriptions, ie, given a set of objects from which one object is selected, what noun phrase will be used to refer to it? Current approaches are based off Dale & Reiter's Incremental Algorithm, which uses the Gricean Maxims as a guide to naturalness, but these maxims are prescriptive, not descriptive, and fail to capture what humans actually do. I intend to do some data-mining to create a basic preference ordering for adjectives, and use this to propose a new algorithm that better captures human referring expression generation. I also want to touch on which types of referring expressions are used in which contexts, in an attempt to help natural language generators decide how to refer to entities in a stream of text. The domain will probably be limited to the Wall Street Journal. (updated 2008.04.02)
 
Added:
>
>		-- Main.itallow - 02 Apr 2008
 

Ph.D. students

Line: 27 to 30
 (tentative title, abstract)I am exploring the various ways to use statistical syntax (e.g. the Charniak parser) for (statistical) machine translation (SMT). My research includes using syntax for word-alignment, MT evaluation, and tuning upstream systems (such as ASR). Current SMT systems do not incorporate syntax, and use "phrases' that are quite explicitly non -syntactic, which raises challenges for the inclusion of syntax in translation modeling. I am particularly interested in:
  • using dependency extraction as a measure of syntactic/semantic similarity
  • how to cope with (or better, make use of) the uncertainty of a statistical parser in these contexts: how can that uncertainty be made useful?
Deleted:
<
<		-- Main.jgk - 02 Apr 2008
 
Added:
>
>		-- Main.jgk - 02 Apr 2008
 

Mass Text Annotation With Mechanical Turk

Bill McNeill

Deleted:
<
<		I am using Amazon's Mechanical Turk service to do multi-user annotation of linguistic phenomena in Wikipedia text. I'm trying to see if I can get good inter-annotator agreement for different kinds of noun phrase annotation. The hope is that this could be an cheaper alternative way of producing annotated corpora. Along the way I am developing reusable Ruby libraries to efficiently parse web text, extract constituents matching certain criteria, and automatically generate Mechanical Turk questions. --April 2008
 
Added:
>
>		I am using Amazon's Mechanical Turk service to do multi-user annotation of linguistic phenomena in Wikipedia text. I'm trying to see if I can get good inter-annotator agreement for different kinds of noun phrase annotation. The hope is that this could be an cheaper alternative way of producing annotated corpora. Along the way I am developing reusable Ruby libraries to efficiently parse web text, extract constituents matching certain criteria, and automatically generate Mechanical Turk questions. --April 2008

Revision 72008-06-27 - goodmami

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Lab member thesis topics

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Utilizing Multilingual Resources for Automatic Lexical Acquisition (MA)

Michael Wayne Goodman

Changed:
<
<		(this is very much a rough draft. expect further revisions later this term) I will explore methods for using resources such as the Turing Center's Transgraph to automatically map words to lexical types, as well as methods to evaluate the performance of such a system. Some of the expected problems include: how to extract, derive, or assume syntactic constraints for words when faced with minimal resources, how to deal with source words that don't map to a single word in the target language (eg. English "hurt" vs. Italian "make harm"), how to deal with small and incomplete grammars (eg. not all possible lexical types are represented), when/how to solicit information from the user/linguist, etc. (updated 2008.04.02)
>
>		I'm investigating how we can leverage the knowledge built into the lexicons of large, mature grammars to help bootstrap the lexicons of much smaller grammars. For my test, I am using the Jacy Japanese grammar as the source and the Ita Italian MMT grammar as the target. I am using the Turing Center's Transgraph project to provide word translations, and some hand-built type mappings from one grammar to the other to figure out the types a word can have. Because of the nature of the project, many spurious items are produced, so I need to apply some filtering to the data to try and remove them. Another aspect of the project is to try and automatically learn transfer rules between the grammars involved. This becomes difficult when source words do not transfer to a single target word, when they change argument structure, etc. (updated 2008.06.27)
 

Generating Referring Expressions (MA)

Margaret Ann Mitchell

Revision 62008-04-03 - goodmami

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META TOPICPARENT name="WebHome"

Lab member thesis topics

Line: 13 to 13
 I will explore methods for using resources such as the Turing Center's Transgraph to automatically map words to lexical types, as well as methods to evaluate the performance of such a system. Some of the expected problems include: how to extract, derive, or assume syntactic constraints for words when faced with minimal resources, how to deal with source words that don't map to a single word in the target language (eg. English "hurt" vs. Italian "make harm"), how to deal with small and incomplete grammars (eg. not all possible lexical types are represented), when/how to solicit information from the user/linguist, etc. (updated 2008.04.02)
Deleted:
<
<		-- Main.ebender - 02 Apr 2008
 

Generating Referring Expressions (MA)

Margaret Ann Mitchell

I'm exploring the problem of how to refer to entities naturally. This is a sub-task within natural language generation, mapping nonlinguistic data to a linguistic output. My focus is primarily on creating distinguishing descriptions, ie, given a set of objects from which one object is selected, what noun phrase will be used to refer to it? Current approaches are based off Dale & Reiter's Incremental Algorithm, which uses the Gricean Maxims as a guide to naturalness, but these maxims are prescriptive, not descriptive, and fail to capture what humans actually do. I intend to do some data-mining to create a basic preference ordering for adjectives, and use this to propose a new algorithm that better captures human referring expression generation. I also want to touch on which types of referring expressions are used in which contexts, in an attempt to help natural language generators decide how to refer to entities in a stream of text. The domain will probably be limited to the Wall Street Journal.

Revision 52008-04-02 - billmcn

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META TOPICPARENT name="WebHome"

Lab member thesis topics

Line: 33 to 33
 
  • how to cope with (or better, make use of) the uncertainty of a statistical parser in these contexts: how can that uncertainty be made useful?
-- Main.jgk - 02 Apr 2008
Added:
>
>

Mass Text Annotation With Mechanical Turk

Bill McNeill

I am using Amazon's Mechanical Turk service to do multi-user annotation of linguistic phenomena in Wikipedia text. I'm trying to see if I can get good inter-annotator agreement for different kinds of noun phrase annotation. The hope is that this could be an cheaper alternative way of producing annotated corpora. Along the way I am developing reusable Ruby libraries to efficiently parse web text, extract constituents matching certain criteria, and automatically generate Mechanical Turk questions. --April 2008

Revision 42008-04-02 - itallow

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META TOPICPARENT name="WebHome"

Lab member thesis topics

Line: 15 to 15
  -- Main.ebender - 02 Apr 2008
Added:
>
>

Generating Referring Expressions (MA)

Margaret Ann Mitchell

I'm exploring the problem of how to refer to entities naturally. This is a sub-task within natural language generation, mapping nonlinguistic data to a linguistic output. My focus is primarily on creating distinguishing descriptions, ie, given a set of objects from which one object is selected, what noun phrase will be used to refer to it? Current approaches are based off Dale & Reiter's Incremental Algorithm, which uses the Gricean Maxims as a guide to naturalness, but these maxims are prescriptive, not descriptive, and fail to capture what humans actually do. I intend to do some data-mining to create a basic preference ordering for adjectives, and use this to propose a new algorithm that better captures human referring expression generation. I also want to touch on which types of referring expressions are used in which contexts, in an attempt to help natural language generators decide how to refer to entities in a stream of text. The domain will probably be limited to the Wall Street Journal. (updated 2008.04.02)

-- Main.itallow - 02 Apr 2008

 

Ph.D. students

Dealing with imperfection in using statistical syntax for machine translation

Revision 32008-04-02 - jgk

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

Lab member thesis topics

Please add a short description of your thesis (MA or PhD) here, with a date indicating when the description was last added/modified.

Added:
>
>

Master's students

 

Utilizing Multilingual Resources for Automatic Lexical Acquisition (MA)

Michael Wayne Goodman

(this is very much a rough draft. expect further revisions later this term)
Line: 11 to 13
 I will explore methods for using resources such as the Turing Center's Transgraph to automatically map words to lexical types, as well as methods to evaluate the performance of such a system. Some of the expected problems include: how to extract, derive, or assume syntactic constraints for words when faced with minimal resources, how to deal with source words that don't map to a single word in the target language (eg. English "hurt" vs. Italian "make harm"), how to deal with small and incomplete grammars (eg. not all possible lexical types are represented), when/how to solicit information from the user/linguist, etc. (updated 2008.04.02)
Deleted:
<
<
 -- Main.ebender - 02 Apr 2008
Added:
>
>

Ph.D. students

Dealing with imperfection in using statistical syntax for machine translation

Jeremy G. Kahn

(tentative title, abstract)I am exploring the various ways to use statistical syntax (e.g. the Charniak parser) for (statistical) machine translation (SMT). My research includes using syntax for word-alignment, MT evaluation, and tuning upstream systems (such as ASR). Current SMT systems do not incorporate syntax, and use "phrases' that are quite explicitly non -syntactic, which raises challenges for the inclusion of syntax in translation modeling. I am particularly interested in:
  • using dependency extraction as a measure of syntactic/semantic similarity
  • how to cope with (or better, make use of) the uncertainty of a statistical parser in these contexts: how can that uncertainty be made useful?
-- Main.jgk - 02 Apr 2008

Revision 22008-04-02 - goodmami

Line: 1 to 1
 
META TOPICPARENT name="WebHome"

Lab member thesis topics

Please add a short description of your thesis (MA or PhD) here, with a date indicating when the description was last added/modified.

Added:
>
>

Utilizing Multilingual Resources for Automatic Lexical Acquisition (MA)

Michael Wayne Goodman

(this is very much a rough draft. expect further revisions later this term) I will explore methods for using resources such as the Turing Center's Transgraph to automatically map words to lexical types, as well as methods to evaluate the performance of such a system. Some of the expected problems include: how to extract, derive, or assume syntactic constraints for words when faced with minimal resources, how to deal with source words that don't map to a single word in the target language (eg. English "hurt" vs. Italian "make harm"), how to deal with small and incomplete grammars (eg. not all possible lexical types are represented), when/how to solicit information from the user/linguist, etc. (updated 2008.04.02)
 -- Main.ebender - 02 Apr 2008

Revision 12008-04-02 - ebender

Line: 1 to 1
Added:
>
>
META TOPICPARENT name="WebHome"

Lab member thesis topics

Please add a short description of your thesis (MA or PhD) here, with a date indicating when the description was last added/modified.

-- Main.ebender - 02 Apr 2008

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