1. WSD for Applications Bill Dolan SenseEval 2004

2. Where is WSD useful?  Lots of work in the field, but still no clear answer Where WSD = classical, dictionary-sense resolution

3. Intuitive Motivations  Automates something we already do with dictionaries  Many applications seem to require WSD Information Retrieval/Question Answering Cross-language information retrieval Information extraction Proofing tools, e.g. synonym replacement Translation

4. Pragmatic Motivations  Splitting off WSD yields a pleasing division of the NLP problem space  manageable in size  clear success metrics  readily available training data: annotated and unannotated

5. But where are the applications?  Why is it so hard to find a convincing app?  Hopeful answer: the quality bar just hasn’t been met yet But even experimentally, little/no evidence that WSD helps any application  Alternatively: maybe we’re trying to automate the wrong task Then what is the right task?

6. An Application-centric view  What do apps actually need? Information Retrieval/Question Answering Cross-language information retrieval Information extraction Proofing tools, e.g. synonym replacement Translation  Not a sense, a cluster of related words, etc. Instead: The ability to map one string into another that’s superficially distinct Regardless of length or language  Paraphrase

7. Question Answering  The genome of the fungal pathogen that causes Sudden Oak Death has been sequenced by US scientists  Researchers announced Thursday they've completed the genetic blueprint of the blight- causing culprit responsible for sudden oak death  Scientists have figured out the complete genetic code of a virulent pathogen that has killed tens of thousands of California native oaks  The East Bay-based Joint Genome Institute said Thursday it has unraveled the genetic blueprint for the diseases that cause the sudden death of oak trees

8. Information Extraction  The genome of the fungal pathogen that causes Sudden Oak Death has been sequenced by US scientists  Researchers announced Thursday they've completed the genetic blueprint of the blight- causing culprit responsible for sudden oak death  Scientists have figured out the complete genetic code of a virulent pathogen that has killed tens of thousands of California native oaks  The East Bay-based Joint Genome Institute said Thursday it has unraveled the genetic blueprint for the diseases that cause the sudden death of oak trees

9. Cross-lingual Information Retrieval  The genome of the fungal pathogen that causes Sudden Oak Death has been sequenced by US scientists  Researchers announced Thursday they've completed the genetic blueprint of the blight- causing culprit responsible for sudden oak death  Scientists have figured out the complete genetic code of a virulent pathogen that has killed tens of thousands of California native oaks  The East Bay-based Joint Genome Institute said Thursday it has unraveled the genetic blueprint for the diseases that cause the sudden death of oak trees

10. Proofing: rewriting tool  The genome of the fungal pathogen that causes Sudden Oak Death has been sequenced by US scientists  Researchers announced Thursday they've completed the genetic blueprint of the blight- causing culprit responsible for sudden oak death  Scientists have figured out the complete genetic code of a virulent pathogen that has killed tens of thousands of California native oaks  The East Bay-based Joint Genome Institute said Thursday it has unraveled the genetic blueprint for the diseases that cause the sudden death of oak trees

11. A different take on the problem  What’s missing is a basic enabling technology Paraphrase identification/generation capability  The applications for WSD that have been suggested over the years really need more general paraphrase identification/generation skills Resolving lexical associations is just one aspect of this  Problem begins to look more like an MT problem Map one chunk of text to another, similar or not Not clear that explicit WSD useful

12. Some Apps  Machine Translation Data-driven techniques predominate, work pretty well  No explicit WSD, just learned associations between bilingual pairings Lexical mappings learned through statistical association  not perfect, but given the right data, pretty good  Different language pairs require different sense breakdowns  Paraphrase/MT are the same problem  Cross-language IR  What else but MT?  Proofing tools, e.g. thesaurus-level replacements  But often not terribly useful; as any writer knows, there’s usually no good synonym, and a complete rewrite is necessary  Question Answering/IR Map a query to a piece of text to semantically similar but potentially formally distinct prose  For all of these apps, problem is less individual words than whole sequences

13. Direction?  The applications that have been suggested for WSD are all just aspects of the larger paraphrase problem Even MT is a paraphrase problem, though a bit more extreme than the monolingual case  Focus on the broader paraphrase problem, rather than on individual words