A general introduction to the S-parser and the data structures it makes use of.

§1. At this point, the text read in by Inform is now a stream of words, each of which is identified by a pointer to a vocabulary_entry structure in its dictionary. The words are numbered upwards from 0, and we refer to any contiguous run of words as an "excerpt", often writing (w1, w2) to mean the text starting with word w1 and continuing to word w2. The stream of words has been divided further into sentences.

Inform has two mechanisms for making sense of this text, the A-parser and the S-parser.

• (A) A stands for "assertion". For instance, "Two men are in Verona." is an assertion, telling Inform that at the start of play there are to be two previously unknown men and that they begin in the room called Verona. The A-parser handles entire sentences.
• (S) S stands for "semantics", the study of how already-understood meanings correspond to excerpts of text within a sentence. The S-parser handles anything from tiny excerpts like "6" through noun phrases such as "Verona" to complicated expressions like "the number of men in Verona".

There are many similarities between the A-parser and the S-parser, partly because A makes use of S, but also because they contain parallel mechanisms which handle verbs and prepositions similarly. But there are also many differences. The A-parser will accept "On the dressing table is an amber comb." even if table and comb have never been mentioned before, whereas the S-parser can only recognise meanings already defined. On the other hand, the S-parser will accept conditions like the one in "if there are fewer than 8 men in Verona, ..." whereas the A-parser would reject the assertion "There are fewer than 8 men in Verona." as being too vague to act upon. Similarly, the A-parser works only in the present tense, whereas the S-parser can handle the past and perfect tenses. (Neither can handle any future tenses, since a computer cannot either control or definitely predict the future.)

The A-parser works by applying the S-parser to text at parse_node structures in the parse tree. So we will build the S-parser first, which won't involve the parse tree at all. We will then go back to the parse tree to write the A-parser.

§2. The S-parser is similar to the expression parser in a regular compiler. It is in some ways simpler because natural language tends not to form complex formulae, but in other ways more complicated, because performance issues are very significant when comparing excerpts of text, and because there are many more ambiguities to resolve.

Our aim is to turn any excerpt into a specification structure inside Inform. This is a universal holder for both values and descriptions of values, where "value" is interpreted very broadly. It is usually too difficult to go directly from text to a specification, so we use a two-stage process:

• (1) parse the text to a parse_node which holds all possible interpretations of it, and then
• (2) convert the most likely-looking interpretation(s) to a specification.

Thus parse_node structures are private to the S-parser, whereas specification structures appear all over Inform.

§3. Consider the following contrived example.

if Mr Fitzwilliam Darcy was carrying at least three things which are in the box, increase the score by 7;

• (1) There are excerpt_meaning structures for "Mr Fitzwilliam Darcy" and "Mr Bingham's box", which hold the wording needed to refer to these objects. In parsing the example sentence, we connect these structures to the excerpts "Mr Fitzwilliam Darcy" — an exact match — and "the box" — an abbreviated one. The excerpt_meaning structures contain pointers to further instance structures which represent the identities of these two tangible things, that is, Darcy and his friend's box.
• (2) Another excerpt_meaning holds the name "score" and points it to a nonlocal_variable structure for the relevant global variable.
• (3) And a further excerpt_meaning holds the name "things" and points it to a instance structure representing the common identity shared by all things. Inform treats individual, tangible objects such as Mr Darcy and intangible categories of objects such as thing by representing both with the same structure — instance. This mirrors the way that common and proper nouns are grammatically quite similar in natural language.
• (4) The final noun phrase in the above example is "7". There's no excerpt_meaning structure for this — it would be insanely inefficient to make such things — and instead it is parsed directly as a "literal", being converted immediately into a specification, of which more below.
• (5) Another excerpt_meaning structure holds the wording "if ... , ..." and is connected to a phrase structure for the "if" construction. Here, the wording includes flexible-sized gaps (written "...") where excerpts should appear: the S-parser will only recognise this if the excerpts make sense in themselves. The combination of a phrase plus the results of parsing these gaps is stored in a structure called an invocation.
• (6) In the example, the first gap is filled by "Mr Fitzwilliam Darcy was carrying at least three things which are in the box", which the S-parser detects as being a condition. This is translated into a pcalc_prop structure — a predicate-calculus proposition, that is, which is a representation in mathematical logic of the meaning of this sentence.
  • (a) "was carrying" is recognised as matching wording in a verb_usage structure. This points to an underlying relation, stored in a binary_predicate structure, but combines it with an indication of tense stored in a time_period. Here the binary_predicate is the carrying relation and the time_period is the past tense. (The term "binary predicate" comes from logic once again; an Inform author would call the same concept a "relation".)
  • (b) "are in" is recognised as a usage of the verb "to be" plus "in", which matches the wording of a preposition structure. Here the tense derives only from the "to be" part: which is "are", so the time_period parsed is the present tense. This makes the preposition a simpler business than the verb_usage structure — it only needs to refer to the underlying meaning, which is once again a binary_predicate structure, the one for the containment relation.
  • (c) "which" is a word introducing a relative clause. A sentence can only have one primary verb, which in this example is "was carrying". But other verbs can exist in relative clauses, and the effect of writing "X which V Y" qualifies X by saying that any noun N matching X must also satisfy "N V Y", where V is the verb. The relative-clause construction is an example of syntax built directly into the S-parser. It doesn't come from any data structures, like the meanings of "score" or "Mr Fitzwilliam Darcy".
  • (d) "at least three things" is an example of a noun phrase which has a head and a tail. The head, "at least three", is recognised as matching the wording in a determiner structure, "at least (number)", together with the literal number 3. Once again, the determiner describes textual appearances; it points to another structure, a quantifier, to hold the meaning. This is another logical term, and Inform's debugging log would write the resulting term as Card>=3 ("cardinality of at least 3"). Inform only uses determiner structures when they quantify, that is, when they talk about a possible range of objects rather than a single item. A grammar of English would probably say that the "the" in "the box" is also grammatically a determiner, but it doesn't get a determiner structure in Inform.
• (7) The second gap in the "if ... , ..." excerpt is "increase the score by 1", which the S-parser detects as a use of yet another phrase, this time referred to by the excerpt_meaning structure for "increase ... by ...". It's worth noting that the S-parser doesn't check types, so it would have been happy to match "increase 2 by 1" — an impossibility. The S-parser's job is to find all possible meanings at a textual level, sometimes producing a list of options: the type-checker will winnow these out later on.

So parsing the text "if Mr Fitzwilliam Darcy was carrying at least three things which are in the box, increase the score by 7" is going to result in a mass of pointers to different structures, and we need an umbrella structure to hold this mass together. This is what the parse_node is for, but as explained above, it's really only an intermediate state used while the S-parser is working.

§4. One obvious category of word is missing: there are no adjectives in this example. Inform currently supports many sorts of adjective — either/or properties, such as "open"; values of kinds of value which coincide with properties, such as "green" as a value of a "colour"; and adjectives defined with conditions or full phrases, such as "invisible" resulting from "Definition: a thing is invisible if...".

The S-parser treats all adjectives alike — more or less just as names. This is because "open" may mean one thing for containers and another for scenes, for example. The identification of an adjective's name with its set of possible meanings is via a structure called adjective.

§5. To sum up. If we write "text" \(\rightarrow\) structure used for parsing \(\rightarrow\) structure used to hold meaning, our example is parsed like so:

• (1) "Mr Fitzwilliam Darcy" \(\rightarrow\) excerpt_meaning \(\rightarrow\) instance
• (2) "the score" \(\rightarrow\) excerpt_meaning \(\rightarrow\) nonlocal_variable
• (3) "things" \(\rightarrow\) excerpt_meaning \(\rightarrow\) instance
• (4) "7" \(\rightarrow\) ...none... \(\rightarrow\) specification
• (5) "if Mr Fitzwilliam Darcy was carrying at least three things which are in the box, increase the score by 7" \(\rightarrow\) excerpt_meaning \(\rightarrow\) invocation (incorporating a phrase)
• (6) "Mr Fitzwilliam Darcy was carrying at least three things which are in the box" \(\rightarrow\) ...many... \(\rightarrow\) pcalc_prop
  • (a) "was carrying" \(\rightarrow\) verb_usage \(\rightarrow\) binary_predicate plus time_period
  • (b) "are in" \(\rightarrow\) preposition \(\rightarrow\) binary_predicate plus time_period
  • (c) "at least three" \(\rightarrow\) determiner \(\rightarrow\) quantifier plus literal number
• (7) "increase the score by 7" \(\rightarrow\) excerpt_meaning \(\rightarrow\) invocation (incorporating a phrase)
• (8) Adjectives like the "closed" in "three closed doors" are identified by name only, with little attempt to detect which sense is meant, so they pass straight through the S-parser as pointers to adjective structures.

§6. To sum up further still, excerpt_meaning structures are used to parse simple nouns and imperative phrases, whereas other specialist structures (preposition, determiner, etc.) are used to parse the hinges which hold sentences together. Once parsed, individual excerpts tend to have meanings which might be pointers to a bewildering range of structures (instance, quantifier, binary_predicate, adjective, etc.) but these pointers are held together inside the S-parser by a single unifying construction: the parse_node. And we will eventually turn the whole thing into a specification for the rest of Inform to use.