From ff5ab511fac31b571e6ae5dabdcd4b1cdfc2c6ff Mon Sep 17 00:00:00 2001 From: Percy Liang Date: Thu, 23 Jan 2014 12:34:38 -0800 Subject: [PATCH] add background reading --- TUTORIAL.md | 50 +++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 43 insertions(+), 7 deletions(-) diff --git a/TUTORIAL.md b/TUTORIAL.md index 8492ba3..e9a5311 100644 --- a/TUTORIAL.md +++ b/TUTORIAL.md @@ -452,14 +452,19 @@ For an example of a more complex grammar, look at `data/emnlp2013.grammar`. 1. Convert the following natural language utterances into lambda-DCS logical forms: - city with the largest area - states bordering Oregon and Washington - top 5 cities by area - countries whose capitals have area at least 500 squared kilometers - second tallest mountain in Europe - country with the most number of rivers + `city with the largest area` -You should familiarize yourself with the Freebase schema to see which + `states bordering Oregon and Washington` + + `top 5 cities by area` + + `countries whose capitals have area at least 500 squared kilometers` + + `second tallest mountain in Europe` + + `country with the most number of rivers` + +You should familiarize yourself with the [Freebase schema](http://www.freebase.com/schema) to see which predicates to use. Execute these logical forms on the `geofreebase` subset to verify your answers. @@ -468,3 +473,34 @@ Execute these logical forms on the `geofreebase` subset to verify your answers. containing the true logical form you annotated above. Train a model (remember to add features) so that the correct logical forms appear at the top of the candidate list. + +## Background reading + +So far this tutorial has provided a very operational view of semantic parsing +based on SEMPRE. The following references provide a much broader look at +the area of semantic parsing and the linguistic and statistical foundations. + +* **Natural language semantics**: The question of how to represent natural + language utterances using logical forms has been well-studied in linguistics + under formal (or compositional) semantics. Start with the + [CS224U course notes from Stanford](http://www.stanford.edu/class/cs224u/readings/cl-semantics-new.pdf) + and + [an introduction by Lappin](http://web.mit.edu/cilene/www/sema/aula1/15.pdf). + You should take away from this an appreciation for the various phenomena in + natural language. + +* **Log-linear models**: Our semantic parser is based on log-linear models, +which is a very important tool in machine learning and statistical natural +language processing. Start with [a tutorial by Michael +Collins](http://www.cs.columbia.edu/~mcollins/loglinear.pdf), which is geared towards applications +in NLP. + +* **Semantic parsing**: finally, putting the linguistic insights from formal semantics +and the computational and statistical tools from machine learning, we get +semantic parsing. There has been a lot of work on semantic parsing. +Check out the [ACL 2013 tutorial by Yoav Artzi and Luke +Zettlemoyer](http://yoavartzi.com/pub/afz-tutorial.acl.2013.pdf), +which focuses on how to build semantic parsers using Combinatory Categorical Grammar (CCG). +Our [EMNLP 2013 +paper](http://cs.stanford.edu/~pliang/papers/freebase-emnlp2013.pdf) is the +first paper based on SEMPRE.