View source for Nschneid writeup of Wang talk

From Cohen Courses

← Nschneid writeup of Wang talk

You do not have permission to edit this page, for the following reason:

---

You can view and copy the source of this page.

{{NschneidWriteup|Wang_thesis_defense}} [http://amsterdam.lti.cs.cmu.edu/thesis.pdf Language-Independent Class Instance Extraction Using the Web] * '''[[Class_Meeting_for_10-707_11/16/2009|SEAL]]''': Use structured Web data to expand sets from a small list of seed examples via wrappers based on HTML source. Ranking of candidates is based on random walks in graph containing wrappers and their fillers. No tokenization required => language-independent. ** Evaluated on manually-constructed data sets, 12 sets for each of (English, Chinese, Japanese), e.g. Japanese provinces ** Constraining wrappers to be shared by multiple seeds reduces noise: e.g. seeds ''Seattle'' and ''Boston''; based on contexts of ''Seattle'' alone we might propose ''Carnegie Mellon'', but this is less likely for contexts hosting both ''Seattle'' and ''Boston''. ** Limitation: performance drops significantly with more than 5 seeds * '''iSEAL''' (iterative SEAL) ** Supervised version: at every iteration, seeds are obtained from a reliable source. Works with a fixed seed size. ** Boostrapping stage: seeds for the ''i''th stage are selected from candidate items from the previous stage. Fixed seed size doesn't work; instead, for the ''i''th iteration, randomly select (''i''+1) seeds based on the original seeds+candidates from previous stages. * '''[[Nschneid_writeup_of_Wang_ACL_2009|ASIA]]''': Finding instances from category names * '''Bilingual SEAL''': for better precision, uses agreement on category instances extracted from data in multiple languages ** ANET (Automatic Named Entity Translation system): using bilingual snippets, ranks target language ''chunks'' (segments surrounded by punctuation or text in another language) based on how closely they correlate with the input string * '''Binary SEAL''': extracting binary relations (i.e. pairs of class-instance relations) ** Learns binary wrappers, which have a middle context string as well as left and right contexts ** e.g. learn state-governor pairs; federal agency name-acronym pairs ** Bootstrapping approach for unary relations also works for binary relations * Future work ** identify category names given some instances ** ... * Question: Could these sorts of models be queried to estimate how likely a given phrase is to belong to a particular class? This could be useful when many instances of the class are known, and also if only the class name (or a few instances) are known.

Return to Nschneid writeup of Wang talk.