Changes between Version 3 and Version 4 of ast

Timestamp:

Mar 14, 2019, 4:05:41 PM (5 years ago)

Author:

xmedved1

Comment:

--

ast

@@ -58 +58 @@
 
 Besides the tree nodes and edges, the tree contains morphological information about each word: a lemma and a PoS tag, which are used by AST for
-deriving implicit out-of-vocabulary type information
+deriving implicit out-of-vocabulary type information.
+
+Actual AST implementation is ale to process inputs form [https://nlp.fi.muni.cz/trac/synt SYNT] ad [https://nlp.fi.muni.cz/trac/set SET] parsers. The previous example of syntactic tree is from output of SYNT parser. 
+
+The example of SET tree in textual form  for sentence "Tom wants to buy a new car but he will not buy it.":
+
+{{{
+id word:nterm lemma tag pid til schema
+0 N:Tom Tom k1gMnSc1;ca14 p
+1 V:chce chtít k5eAaImIp3nS 15
+2 V:koupit koupit k5eAaPmF 16
+3 ADJ:nové nový k2eAgNnSc4d1 17
+4 N:auto auto k1gNnSc4 17
+5 PUNCT:, , kIx 10
+6 CONJ:ale ale k8xC 10
+7 V:nekoupí koupit k5eNaPmIp3nS 13
+8 PRON:je on k3xPp3gNnSc4 13
+9 PUNCT:. . kIx. 10
+10 <CLAUSE> k5eNaPmIp3nS 12 vrule_sch ( $$ $@ )
+11 <CLAUSE> k5eAaImIp3nS 12 vrule_sch ( $$ $@ )
+12 <SENTENCE> -1
+13 <VP> koupit k5eNaPmIp3nS 10 vrule_sch_add ( $$ $@ "#1H (#2)" )
+14 <VP> chtít k5eAaImIp3nS 11 vrule_sch_add ( $$ $@ "#2H (#1)" )
+15 <VP> chtít k5eAaImIp3nS 14 vrule_sch_add ( $$ $@ "#1H (#2)" )
+16 <VP> koupit k5eAaPmF 15 vrule_sch_add ( $$ $@ "#1H (#2)" )
+17 <NP> auto k1gNnSc4 16 rule_sch ( $$ $@ "[#1,#2]" )
+}}}
+
+Visual representation of SET structural tree tree:
+
+[[Image(set_tree.png, 700px)]]
+
 
 
@@ -101 +132 @@
 the implicit definition. A lexical item example for the verb "jíst" (eat) is:
 
-[[Image(jist.png, 700px)]]
+[[Image(jist.png, 300px)]]
 
 The exact format of the lexical item in the input file is as follows: the lemma
@@ -107 +138 @@
 (optional) POS tag filter precedes the resulting object schema (here otriv, i.e.
 o-trivialisation) and TIL type (here verbal object with one ι-argument).
+
+'''Verb Valencies''': the next language dependent file is a file that defines verb
+valencies and schema and type information for building the resulting construction from the corresponding valency frame. An example for the verb “jíst” (eat)
+is as follows:
+
+{{{
+jíst
+hPTc4 :exists:V(v):V(v):and:V(v)=[[#0,try(#1)],V(w)]
+}}}
+
+This record defines the valency of <somebody> eats <something>, given by the
+brief valency frame hPTc4 of the object (an animate or inanimate noun phrase in
+accusative), and the resulting construction of the verbal object (V(v)) derived as
+an application of the verb (!#0) to its argument (the sentence object) with possible
+extensification (try(!#1)) and the appropriate possible world variable (V(w)).
+
+'''Prepositional Valency Expressions''': the last file that has to be specified for
+each language is a list of semantic mappings of prepositional phrases to
+valency expressions based on the head preposition. The file contains for each
+combination of a preposition and a grammatical case of the included noun
+phrase all possible valency slots corresponding to the prepositional phrase. For
+instance, the record for the preposition "k" (to) is displayed as
+
+{{{
+k
+3 hA hH
+}}}
+
+saying that "k" can introduce prepositional phrase of a where-to direction hA
+(e.g. "k lesu" – "to a forest"), or a modal how/what specification hH (e.g. "k večeři"
+– "to a dinner").
+
+= System Parts =
+The AST system is implemented in the Python 2.7 programming language and
+consists of six main parts:
+* the input parser: reads standard input, extracts tree structures and creates tree object for each tree from input,
+* the grammar parser: reads the grammar file and assigns a grammar rule and appropriate actions to each node inside the tree,
+* the lexical item parser: reads the file with lexical item schemata and TIL types and assigns the type to each leaf in the tree structure,
+* the schema parser: according to a logical construction schema coming with a semantic action, this module creates a construction from sub-constructions,
+* the verb valency parser: picks up the correct valency for given sentence and triggers the schema parser on sub-constructions according to the schema coming with the valency, and
+* the prepositional valency expression parser: reads the possible valency expressions assigned to prepositional phrases used as (optional) valency slots in the actual sentence valency frame.
+
+