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SEMPRE 2.0 tutorial

In this tutorial, we will provide a brief tour of SEMPRE. This tutorial is very much about the mechanics of the system, not about the linguistics or semantic parsing from a research point of view (for those, see the recommended readings at the end of this document). Once you have gone through the tutorial, you can read the full documentation.

We will construct a semantic parser to understand a toy subset of natural language. Concretely, the system we will build will have the following behavior:

  • Input: What is three plus four?
  • Output: 7

Recall that in semantic parsing, natural language utterances are mapped into logical forms (think programs), which are executed to produce some denotation (think return value).

We have assumed you have already installed SEMPRE and can open up a shell:

./run @mode=simple

This will put you in an interactive prompt where you can develop a system and parse utterances into tiny Java programs. Note: you might find it convenient to use rlwrap to get readline support.

Just to provide a bit of transparency: The run script simply creates a shell command and executes it. To see which command is run, do:

./run @mode=simple -n

This should print out:

java -cp libsempre/*:lib/* -ea edu.stanford.nlp.sempre.Main -Main.interactive

You can pass in additional options:

./run @mode=simple -Parser.verbose 3  # Turn on more verbose debugging for the parser
./run @mode=simple -help              # Shows all options and default values

Section 1: Logical forms and denotations

A logical form (class Formula in SEMPRE) is a hierarchical expression. In the base case, we have primitive logical forms for representing concrete values (booleans, numbers, strings, dates, names, and lists):

(boolean true)
(number 3)
(string "hello world")
(date 2014 12 8)
(list (number 1) (number 2))

Logical forms can be constructed recursively using call, which takes a function name followed by arguments, which are themselves logical forms:

(call + (number 3) (number 4))
(call java.lang.Math.cos (number 0))
(call .indexOf (string "what is this?") (string is))
(call .substring (string "what is this?") (number 5) (number 7))
(call if (call < (number 3) (number 4)) (string yes) (string no))

In general:

(call <function-name> <logical-form-argument-1> ... <logical-form-argument-n>)

Later, we will see other ways (besides call) of building more complex logical forms from simpler logical forms.

Note that each logical form is painfully explicit about types. You would probably not want to program directly in this language (baby Java using LISP-like notation), but that is not the point; we will later generate these logical forms automatically from natural language.

We can execute a logical form by typing the following into the interactive prompt:

(execute (call + (number 3) (number 4)))

In general:

(execute <logical-form>)

This should print out (number 7), which we refer to as the denotation (class Value in SEMPRE) of the logical form. Try to execute the other logical forms and see what you get.

Exercise 1.1: write a logical form that computes the first word ("compositionality") in the string "compositionality is key".

Lambda expressions

So far, we have been representing logical forms that produce a single output value (e.g., "compositionality"). But one of the key ideas of having programs (logical forms) is the power of abstraction — that we can represents functions that compute an output value for each input value.

For example, the following logical form denotes a function that takes a number and returns its square:

(lambda x (call * (var x) (var x)))

If you execute this logical form directly, you will get an error, because the denotation of this logical form is a function, which is not handled by the JavaExecutor. However, we can apply this function to an argument (number 3):

((lambda x (call * (var x) (var x))) (number 3))

This logical form now denotes a number. Executing this logical form should yield (number 9).

In general:

(<function-logical-form> <argument-logical-form>)

Exercise 1.2: Adapt your logical form form Exercise 1.1 to compute the first word of any string. Your answer should be (lambda x ...). Create a logical form that applies this on the argument (string "compositionality is key").

Technical note: these lambda expressions are actually just doing macro substitution, not actually representing higher-order functions; since there are no side effects here, there is no difference.

This concludes the section on logical forms and denotations. We have presented one system of logical forms, which are executed using JavaExecutor. The system supports other types of logical forms, for example, those which encode SPARQL database queries for question answering (we will get to that later). Note that there is no mention of natural language yet...

Section 2: Parsing utterances to logical forms

Having established the nature of logical forms and their denotations, let us turn to the problem of mapping a natural language utterance into a logical form. Again, the key framework is compositionality, which roughly says that the meaning of a full sentence is created by combining the meanings of its parts. For us, meanings are represented by logical forms.

We will start by defining a grammar (class Grammar in SEMPRE), which is a set of rules (class Rule in SEMPRE), which specify how to combine logical forms to build more complex ones in a manner that is guided by the natural language.

We will run through some examples to give you a feel for how things work, and then go into the details. First, let us add a rule to the grammar by typing the following into the interactive prompt:

(rule $ROOT (three) (ConstantFn (number 3)))

Now to parse an utterance, just type it in to the interactive prompt:


The parser should print out (among other information) a line that shows that the utterance was parsed successfully into a derivation (class Derivation in SEMPRE), which importantly carries the correct logical form (number 3.0):

(derivation (formula (number 3.0)) (value (number 3.0)) (type fb:type.number))

Now type in the utterance:


You should get no results because no rule matches four. To fix that, let us create a more general rule:

(rule $ROOT ($PHRASE) (NumberFn))

This rule says for any phrase (sequence of consecutive tokens), pass it to a special function called NumberFn, which will transform the phrase string into a new derivation representing a number.

Now, you can parse the following:


Note: if you now type in three, you should get two derivations that yield the same answer, one coming from each rule. Note that twenty-five million will not parse because we are using SimpleLanguageAnalyzer. Later, we can using Stanford CoreNLP to improve the basic linguistic capabilities.

So far, we have only parsed utterances using one rule, but the true power of these grammars come from combining multiple rules. Copy and paste in the following rules:

(rule $Expr ($PHRASE) (NumberFn))
(rule $Operator (plus) (ConstantFn (lambda y (lambda x (call + (var x) (var y))))))
(rule $Operator (times) (ConstantFn (lambda y (lambda x (call * (var x) (var y))))))
(rule $Partial ($Operator $Expr) (JoinFn forward))
(rule $Expr ($Expr $Partial) (JoinFn backward))
(rule $ROOT ((what optional) (is optional) $Expr (? optional)) (IdentityFn))

Now try typing in:

What is three plus four?

The output should be:

(number 7)

We can parse longer sentences. Type in:

What is three plus four times two?

There should be two derivations, yielding (number 14) and (number 11), corresponding to either combining three plus four first or four times two first. Note that this is expected because we have not encoded any order of operations anywhere.

Hopefully that should give you a sense of what parsing looks like. Let us now take a closer look. At the end of the day, a grammar declaratively specifies a mapping from utterances to a set of candidate derivations. A parser (class Parser in SEMPRE) is an actual algorithm that takes the grammar and generates those derivations. Recall that a derivation looks like this:

(derivation (formula (number 3.0)) (value (number 3.0)) (type fb:type.number))

Formally, each derivation produced by the parser has the following properties:

  1. Span i:j (e.g., 0:1): specifies the contiguous portion of the input utterance (tokens i to j-1) that the Derivation is constructed from.
  2. Category (e.g., $ROOT): categories place hard constraints on what Derivations can be combined.
  3. Type (e.g., (fb:type.number)): typically more fine-grained than the category, and is generated dynamically.
  4. Logical form (e.g., (call + (number 3) (number 4))): what we normally think of as the output of semantic parsing.
  5. Value (e.g., (number 7)): the result of executing the logical form.

There are some special categories:

  1. $TOKEN: matches a single token of the utterance. Formally, the parser builds a Derivation with category $TOKEN and logical form corresponding to the token (e.g., (string three)) for each token in the utterance.
  2. $PHRASE: matches any contiguous subsequence of tokens. The logical form created is the concatenation of those tokens (e.g., (string "twenty-five million")).
  3. $LEMMA_TOKEN: like $TOKEN, but the logical form produced is a lemmatized version of the token (for example $TOKEN would yield cows, while $LEMMA_TOKEN would yield cow).
  4. $LEMMA_PHRASE: the lemmatized version of $PHRASE.
  5. $ROOT Derivations that have category $ROOT and span the entire utterance are executed, scored, and sent back to the user.

Now let us see how a grammar specifies the set of derivations. A grammar is a set of rules, and each rule has the following form:

(rule <target-category> (<source-1> ... <source-k>) <semantic-function>)
  1. Target category (e.g., $ROOT): any derivation produced by this rule is labeled with this category. $ROOT is the designated top-level category. Derivations of type $ROOT than span the entire utterance are returned to the user.
  2. Source sequence (e.g., three): in general, this is a sequence of tokens and categories (all categories start with $ by convention). Tokens (e.g., three) are matched verbatim, and categories (e.g., $PHRASE) match any derivation that is labeled with that category and has a span at that position.
  3. Semantic function (SemanticFn): a semantic function takes a sequence of derivations corresponding to the categories in the children and produces a set of new derivations which are to be labeled with the target category. Semantic functions run arbitrary Java code, and allow the parser to integrate custom logic in a flexible modular way. In the example above, ConstantFn is an example of a semantic function which always returns one derivation with the given logical form (e.g., (number 3)). JoinFn produces a derivation whose logical form is the composition of the logical forms of the two source derivations.

Derivations are built recursively: for each category and span, we construct a set of Derivations. We can apply a rule if there is some segmentation of the span into sub-spans $s_1, \dots, s_k$ and a derivation $d_i$ on each span $s_i$ with category |source_i|. In this case, we pass the list of derivations as input into the semantic function. The output is a set of derivations (possibly zero).

The first rule is a familiar one that just parses strings such as three into the category $Expr:

(rule $Expr ($PHRASE) (NumberFn))

Specifically, one derivation with logical form (string three) is created with category $PHRASE and span 0:1. This derivation is passed into NumberFn, which returns one derivation with logical form (number 3) and category $Expr and span 0:1. The same goes for four on span 2:3.

The next two rules map the tokens plus and times to a static logical form (returned by ConstantFn):

(rule $Operator (plus) (ConstantFn (lambda y (lambda x (call + (var x) (var y))))))
(rule $Operator (times) (ConstantFn (lambda y (lambda x (call * (var x) (var y))))))

The next two rules are the main composition rules:

(rule $Partial ($Operator $Expr) (JoinFn forward))
(rule $Expr ($Expr $Partial) (JoinFn backward))

The semantic function (JoinFn forward) takes two a lambda term $Operator and an argument $Expr and returns a new derivation by forward application:

Source $Operator: (lambda y (lambda x (call + (var x) (var y))))
Source $Expr: (number 4)
Target $Partial: (lambda x (call + (var x) (number 4)))

The semantic function (Join backward) takes an argument $Expr and a lambda term $Partial and returns a new derivation by backward application:

Source $Expr: (number 3)
Source $Partial: (lambda x (call + (var x) (number 4)))
Target $Expr: (call + (number 3) (number 4))

(rule $ROOT ((what optional) (is optional) $Expr (? optional)) (IdentityFn))

We allow some RHS elements to be optional, so that we could have typed in three plus four or three plus four?. IdentityFn simply takes the logical form corresponding to $Expr and passes it up.

The complete derivation for three plus four is illustrated here:

                          $ROOT : (call + (number 3) (number 4)))
                            | [IdentityFn]
                          $Expr : (call + (number 3) (number 4)))
                            | [JoinFn backward]
  |                                                   |
  |                                               $Partial : (lambda x (call + (var x) (number 4)))
  |                                                   | [JoinFn forward]
  |                     +-----------------------------+------------------------------+
  |                     |                                                            |
$Expr : (number 3)  $Operator : (lambda y (lambda x (call + (var x) (var y))))     $Expr : (number 4)
  | [NumberFn]          | [ConstantFn]                                               | [NumberFn]
$PHRASE: three          |                                                          $PHRASE : four
  | [built-in]          |                                                            | [built-in]
three                  plus                                                         four

Exercise 2.1: write rules that can parse the following utterances into into the category $Expr:

length of hello world         # 11
length of one                 # 3

Your rules should look something like:

(rule $Function (length of) ...)
(rule $Expr ($Function $PHRASE) ...)

Exercise 2.2: turn your "first word" program into a rule so that you can parse the following utterances into $String:

first word in compositionality is key       # compositionality
first word in a b c d e                     # a

Exercise 2.3: combine all the rules that you have written to produce one grammar that can parse the following:

two times length of hello world             # 22
length of hello world times two             # (what happens here?)

To summarize, we have shown how to connect natural language utterances and logical forms using grammars, which specify how one can compositionally form the logical form incrementally starting from the words in the utterance. Note that we are dealing with grammars in the computer science sense, not in the linguistic sense, as we are not developing a linguistic theory of grammaticality; we are merely trying to parse some useful subset of utterances for some task. Given an utterance, the grammar defines an entire set of derivations, which reflect both the intrinsic ambiguity of language as well as the imperfection of the grammar. In the next section, we will show how to learn a semantic parser that can resolve these ambiguities.

Saving to a file (optional)

You can put a set of grammar rules in a file (e.g., data/tutorial-arithmetic.grammar) and load it:

./run @mode=simple -Grammar.inPaths data/tutorial-arithmetic.grammar

If you edit the grammar, you can reload the grammar without exiting the prompt by typing:


Using CoreNLP (optional)

Recall that we were able to parse four, but not twenty-five million, because we used the SimpleLanguageAnalyzer. In this section, we will show how to leverage Stanford CoreNLP, which provides us with more sophisticated linguistic processing on which we can build more advanced semantic parsers.

First, we need to do download an additional dependency (this could take a while to download because it loads all of the Stanford CoreNLP models for part-of-speech tagging, named-entity recognition, syntactic dependency parsing, etc.):

./pull-dependencies corenlp

Compile it:

ant corenlp

Now we can load the SEMPRE interactive shell with CoreNLPAnalyzer:

./run @mode=simple -languageAnalyzer corenlp.CoreNLPAnalyzer -Grammar.inPaths data/tutorial-arithmetic.grammar

The following utterances should work now (the initial utterance will take a few seconds while CoreNLP models are being loaded):

twenty-five million
twenty-five million plus forty-two

Section 3: Learning

So far, we have used the grammar to generate a set of derivations given an utterance. We could work really hard to make the grammar not overgenerate, but this will in general be hard to do without tons of manual effort. So instead, we will use machine learning to learn a model that can choose the best derivation (and thus logical form) given this large set of candidates. So the philosophy is:

  • Grammar: small set of manual rules, defines the candidate derivations
  • Learning: automatically learn to pick the correct derivation using features

In a nutshell, the learning algorithm (class Learner in SEMPRE) uses stochastic gradient descent to optimize the conditional log-likelihood of the denotations given the utterances in a training set. Let us unpack this.

Components of learning

First, for each derivation, we extract a set of features (formally a map from strings to doubles — 0 or 1 for indicator features) using a feature extractor (class FeatureExtractor in SEMPRE), which is an arbitrary function on the derivation. Given a parameter vector theta (class Params in SEMPRE), which is also a map from strings to doubles, the inner product gives us a score:

Score(x, d) = features(x, d) dot theta,

where x is the utterance and d is a candidate derivation.

Second, we define a compatibility function (class ValueEvaluator in SEMPRE) between denotations, which returns a number between 0 and 1. This allows us learn with approximate values (e.g., "3.5 meters" versus "3.6 meters") and award partial credit.

Third, we have a dataset (class Dataset in SEMPRE) consisting of examples (class Example in SEMPRE), which specifies utterance-denotation pairs. Datasets can be loaded from files; here is what data/tutorial-arithmetic.grammar looks like:

  (utterance "three and four")
  (targetValue (number 7))

Intuitively, the learning algorithm will tune the parameter vector theta so that derivations with logical forms whose denotations have high compatibility with the target denotation are assigned higher scores. For the mathematical details, see the learning section of this paper.

No learning

As a simple example, imagine that a priori, we do not know what the word and means: it could be either plus or times. Let us add two rules to capture the two possibilities (this is reflected in data/tutorial-arithmetic.grammar):

(rule $Operator (and) (ConstantFn (lambda y (lambda x (call * (var x) (var y)))) (-> fb:type.number (-> fb:type.number fb:type.number))))
(rule $Operator (and) (ConstantFn (lambda y (lambda x (call + (var x) (var y)))) (-> fb:type.number (-> fb:type.number fb:type.number))))

Start the interactive prompt:

./run @mode=simple -Grammar.inPaths data/tutorial-arithmetic.grammar

and type in:

three and four

There should be two derivations each with probability 0.5 (the system arbitrarily chooses one):

(derivation (formula (((lambda y (lambda x (call * (var x) (var y)))) (number 4.0)) (number 3.0))) (value (number 12.0)) (type fb:type.number)) [score=0, prob=0.500]
(derivation (formula (((lambda y (lambda x (call + (var x) (var y)))) (number 4.0)) (number 3.0))) (value (number 7.0)) (type fb:type.number)) [score=0, prob=0.500]

Batch learning

To perform (batch) learning, we run SEMPRE:

./run @mode=simple -Grammar.inPaths data/tutorial-arithmetic.grammar -FeatureExtractor.featureDomains rule -Dataset.inPaths train:data/tutorial-arithmetic.examples -Learner.maxTrainIters 3

The rule feature domain tells the feature extractor to increment the feature each time the grammar rule is applied in the derivation. Dataset.inPaths specifies the examples file to train on, and -Learner.maxTrainIters 3 specifies that we will iterate over all the examples three times.

Now type:

three and four

The correct derivation should now have much higher score and probability:

(derivation (formula (((lambda y (lambda x (call + (var x) (var y)))) (number 4)) (number 3))) (value (number 7)) (type fb:type.any)) [score=18.664, prob=0.941]
(derivation (formula (((lambda y (lambda x (call * (var x) (var y)))) (number 4)) (number 3))) (value (number 12)) (type fb:type.any)) [score=15.898, prob=0.059]

You will also see the features that are active for the predicted derivation. For example, the following line represents the feature indicating that we applied the rule mapping and to +:

[ rule :: $Operator -> and (ConstantFn (lambda y (lambda x (call + (var x) (var y))))) ]  1.383 = 1 * 1.383

The feature value is 1, the feature weight is 1.383, and their product is the additive contribution to the score of this derivation. You can look at the score of the other derivation:

(select 1)

The corresponding feature there is:

[ rule :: $Operator -> and (ConstantFn (lambda y (lambda x (call * (var x) (var y))))) ] -1.383 = 1 * -1.383

This negative contribution to the score is why we favored the + derivation over this * one.

We can also inspect the parameters:


Online learning

Finally, you can also do (online) learning directly in the prompt:

(accept 1)

This will accept the * derivation as the correct one and update the parameters on the fly. If you type:

three and four

again, you will see that the probability of the + derivation has decreased. If you type (accept 1) a few more times, the * derivation will dominate once more.

Section 4: Lambda DCS and SPARQL

So far, we used JavaExecutor to map logical forms to denotations by executing Java code. A major application of semantic parsing (and indeed the initial one that gave birth to SEMPRE) is where the logical forms are database queries. In this section, we will look at querying graph databases.

A graph database (e.g., Freebase) stores information about entities and their properties; concretely, it is just a set of triples $(s, p, o)$, where $s$ and $o$ are entities and $p$ is a property. For example:

fb:en.barack_obama fb:place_of_birth fb:en.honolulu

is one triple. If we think of the entities as nodes in a directed graph, the each triple is a directed edge between two nodes labeled with the property.

See freebase/data/tutorial.ttl for an example of a tiny subset of the Freebase graph pertaining to geography about California.

First, pull the dependencies needed for Freebase:

./pull-dependencies freebase

Setting up your own Virtuoso graph database

We use the graph database engine, Virtuoso, to store these triples and allow querying. Follow these instructions if you want to create your own Virtuoso instance.

First, make sure you have Virtuoso installed — see the Installation section of the readme.

Then start the server:

freebase/scripts/virtuoso start tutorial.vdb 3001

Add a small graph to the database:

freebase/scripts/virtuoso add freebase/data/tutorial.ttl 3001

Now you can query the graph (this should print out three items):

./run @mode=query @sparqlserver=localhost:3001 -formula '(fb:location.location.containedby fb:en.california)'

To stop the server:

freebase/scripts/virtuoso stop 3001

Setting up a copy of Freebase

The best case is someone already installed Freebase for you and handed you a host:port. Otherwise, to run your own copy of the entire Freebase graph (a 2013 snapshot), read on.

Download it (this is really big and takes a LONG time):

./pull-dependencies fullfreebase-vdb

Then you can start the server (make sure you have at least 60GB of memory):

freebase/scripts/virtuoso start lib/fb_data/93.exec/vdb 3093

Lambda DCS

SPARQL is the standard language for querying graph databases, but it will be convenient to use a language more tailored for semantic parsing. We will use lambda DCS, which is based on a mix between lambda calculus, description logic, and dependency-based compositional semantics (DCS).

We assume you have started the Virtuoso database:

freebase/scripts/virtuoso start tutorial.vdb 3001

Then start up a prompt:

./run @mode=simple-freebase-nocache @sparqlserver=localhost:3001

The simplest logical formula in lambda DCS is a single entity such as fb:en.california. To execute this query, simply type the following into the interactive prompt:

(execute fb:en.california)

This should return:

(list (name fb:en.california California))

The result is a list containing the single entity. Here, fb:en.california is the canonical Freebase ID (always beginning with the prefix fb:) and California is the name (look at data/tutorial.ttl to see where this comes from).

Let us try a more complex query which will fetch all the cities (in the database);

(execute (fb:type.object.type fb:location.citytown))

This should return the three cities, Seattle, San Francisco, and Los Angeles. We can restrict to cities in California:

(execute (and (fb:type.object.type fb:location.citytown) (fb:location.location.containedby fb:en.california)))

This should return the two cities satisfying the restriction: San Francisco and Los Angeles.

We can count the number of cities (should return 3):

(execute (count (fb:type.object.type fb:location.citytown)))

We can also get the city with the largest area:

(execute (argmax 1 1 (fb:type.object.type fb:location.citytown) fb:location.location.area))

Now let us take a closer look at what is going on with these logical forms under the hood. We are using a logical language called lambda DCS.

Here are the following types of logical forms:

  1. Primitive (e.g., fb:en.seattle): denotes a set containing that single entity.
  2. Intersection (and |u1| |u2|): denotes the intersection of the sets denoted by unary logical forms u1 and u2.
  3. Join (|b| |u|): denotes the set of $x$ which are connected to some $y$ via a binary $b$ and $y$ is in the set denoted by unary $u$.
  4. Count (count |u|): denotes the set containing the cardinality of the set denoted by u.
  5. Superlative (argmax |rank| |count| |u| |b|): sort the elements of z by decreasing b and return count elements starting at offset rank (1-based).
  6. Mu abstraction (mark (var |v|) |u|): same as the unary |u| denoting entities |x|, with the exception that |x| must be equal to all occurrences of the variable |v| in |u|.
  7. Lambda abstraction (lambda (var |v|) |u|): produces a binary (x,y) where x is in the set denoted by u and y is the value taken on by variable v.

See src/edu/stanford/nlp/sempre/freebase/test/ for many more examples (which only work on the full Freebase).

Exercise 4.1: write lambda DCS logical forms for the following utterances:

`city with the largest area`

`top 5 cities by area`

`countries whose capitals have area at least 500 squared kilometers`

`states bordering Oregon and Washington`

`second tallest mountain in France`

`country with the most number of rivers`

You should familiarize yourself with the Freebase schema to see which predicates to use. Execute these on the full Freebase to find out the answer!


So far, we have described the denotations of logical forms for querying a graph database. Now we focus on parsing natural language utterances into these logical forms.

The core challenge is at the lexical level: mapping natural language phrases (e.g., born in) to logical predicates (e.g., fb:people.person.place_of_birth). It is useful to distinguish between two types of lexical items:

  • Entities (e.g., fb:en.barack_obama): There are generally a huge number of entities (Freebase has tens of millions). Often, string matching gets you part of the way there (for example, Obama to fb.en:barack_obama), but there is often quite a bit of ambiguity (Obama is also a city in Japan).

  • Non-entities (e.g., fb:people.person.place_of_birth), which include unary and binary predicates: There are fewer of these, but string matching is unlikely to get you very far.

We could always add grammar rules like this:

(rule $Entity (the golden state) (ConstantFn fb:en.california))
(rule $Entity (california) (ConstantFn fb:en.california))

but grammars are supposed to be small, so this approach does not scale, so we are not going to do this. One way is to create a lexicon, which is a mapping from words to predicates (see freebase/data/tutorial-freebase.lexicon), with entries like this:

{"lexeme": "california", "formula": "fb:en.california"}
{"lexeme": "the golden state", "formula": "fb:en.california"}
{"lexeme": "cities", "formula": "(fb:type.object.type fb:location.citytown)"}
{"lexeme": "towns", "formula": "(fb:type.object.type fb:location.citytown)"}
{"lexeme": "in", "formula": "fb:location.location.containedby"}
{"lexeme": "located in", "formula": "fb:location.location.containedby"}

Then we can add the following rules (see freebase/data/tutorial-freebase.grammar):

(rule $Unary ($PHRASE) (SimpleLexiconFn (type fb:type.any)))
(rule $Binary ($PHRASE) (SimpleLexiconFn (type (-> fb:type.any fb:type.any))))
(rule $Set ($Unary) (IdentityFn))
(rule $Set ($Unary $Set) (MergeFn and))
(rule $Set ($Binary $Set) (JoinFn forward))
(rule $ROOT ($Set) (IdentityFn))

The SimpleLexiconFn looks up the phrase and returns all formulas that have the given type. To check the type, use:

(type fb:en.california)                         # fb:common.topic
(type fb:location.location.containedby)         # (-> fb:type.any fb:type.any)

MergeFn takes the two (unary) logical forms |u| and |v| (in this case, coming from $Unary and $Set), and forms the intersection logical form (and |u| |v|).

JoinFn takes two logical forms (one binary and one unary) and returns the logical form (|b| |v|). Note that before we were using JoinFn as function application. In lambda DCS, JoinFn produces an actual logical form that corresponds to joining |b| and |v|. The two bear striking similarities, which is the basis for the overloading.

Now start the interactive prompt:

./run @mode=simple-freebase-nocache @sparqlserver=localhost:3001 -Grammar.inPaths freebase/data/tutorial-freebase.grammar -SimpleLexicon.inPaths freebase/data/tutorial-freebase.lexicon

We should be able to parse the following utterances:

the golden state
cities in the golden state
towns located in california

In general, how does one create grammars? One good strategy is to start with a single rule mapping the entire utterance to the final logical form. Then decompose the rule into parts. For example, you might start with:

(rule $ROOT (cities in california) (ConstantFn (and (fb:type.object.type fb:location.citytown) (fb:location.location.containedby fb:en.california))))

Then you might factor it into two pieces, in order to generalize:

(rule $ROOT (cities in $Entity) (lambda e (and (fb:type.object.type fb:location.citytown) (fb:location.location.containedby (var e)))))
(rule $Entity (california) (ConstantFn fb:en.california))

Note that in the first rule, we are writing (lambda x ...) directly. This means, take the logical form for the source ($Entity) and substitute it in for x.

We can refactor the first rule:

(rule $ROOT ($Unary in $Entity) (lambda x (and (var u) (fb:location.location.containedby (var e)))))
(rule $Unary (cities) (ConstantFn (fb:type.object.type fb:location.citytown)))

and so on...

Exercise 4.2: Write a grammar that can parse the utterances from Exercise 4.1 into a set of candidates containing the true logical form you annotated. Of course you can trivially write one rule for each example, but try to decompose the grammars as much as possible. This is what will permit generalization.

Exercise 4.3: Train a model so that the correct logical forms appear at the top of the candidate list on the training examples. Remember to add features.


In the beginning, SEMPRE grammars can be difficult to debug. This is primarily because everything is dynamic, which means that minor typos result in empty results rather than errors.

The first you should do is to check that you do not have typos. Then, try to simplify your grammar as much as possible (comment things out) until you have the smallest example that fails. Then you should turn on more debugging output:

Only derivations that reach $ROOT over the entire span of the sentence are built. You can also turn on debugging to print out all intermediate derivations so that you can see where something is failing:

(set Parser.verbose 3)  # or pass -Parser.verbose 3 on the command-line

Often derivations fail because an intermediate combination does not type check. This option will print out all combinations which are tried. You might find that you are combining two logical forms in the wrong way:

(set JoinFn.verbose 3)
(set JoinFn.showTypeCheckFailures true)
(set MergeFn.verbose 3)
(set MergeFn.showTypeCheckFailures true)

Appendix: Background reading

So far this tutorial has provided a very operational view of semantic parsing based on SEMPRE. The following references provide a broader look at the area of semantic parsing as well as 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 to get a brief taste of the various phenomena in natural language. The Bos/Blackburn book (also see this related article) gives more details on how parsing to logical forms works (without any learning); Prolog code is given too.

  • 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, 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, we will not attempt to list fully here. Check out the ACL 2013 tutorial by Yoav Artzi and Luke Zettlemoyer, which focuses on how to build semantic parsers using Combinatory Categorical Grammar (CCG). Our EMNLP 2013 paper is the first paper based on SEMPRE. This Annual Reviews paper provides a tutorial of how to learn a simple model of compositional semantics (Python code is available) along with a discussion of compositionality and generalization.