1. NL Processing and Fact Extraction 11th May 2013
International Technology Alliance
In Network & Information Sciences
NL Processing and Fact Extraction 11th May 2013
David Mott (IBM UK) Stephen Poteet, Ping Xue, Anne Kao (Boeing)

2. Summary
This document summarises the state of the NL processing and fact extraction as at the 11th May 2013.
Key areas addressed:
significant extensions to the work demonstrated at ACITA:
extending the lexicon, based upon WordNet and VerbNet resources, which have been turned into CE
generalising the semantic rules, allowing automatic processing based upon the linguistic structures for verbs defined in VerbNet
extending the analysis of complex verb phrases, including auxillary verbs (have/be etc), providing information about tenses, aspect and active/passive voice
extending CE by use of “linguistic frames”, allowing the structure of CE to be changed by configuration
initial analysis of the LKB structures and how they might be represented in CE, including consideration of “complements” to verb phrases

3. References WordNet VerbNet
George A. Miller (1995). WordNet: A Lexical Database for English. Communications of the ACM Vol. 38, No. 11:
Christiane Fellbaum (1998, ed.) WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.
VerbNet
Karin Kipper, Anna Korhonen, Neville Ryant, Martha Palmer, A Large-scale Classification of English Verbs, Language Resources and Evaluation Journal, 42(1), pp , Springer Netherland, 2008.
Karin Kipper Schuler, Anna Korhonen, Susan W. Brown, VerbNet overview, extensions, mappings and apps, Tutorial, NAACL-HLT 2009, Boulder,Colorado.

4. Progress since ACITA12 Extensions to CE Exploring the LKB
More comprehensive common model of language
Handling compound verb phrases:
“is being loved”
attempting to get tense, voice, aspect, case
constructing temporal relations between situations and utterance
Using Wordnet
defining lexicon of nouns and senses
adding in inflections
adding in links to Domain Specific concepts
Using Verbnet
defining lexicon of verbs with typical grammatical patterns (NP V NP) and senses
Extensions to CE
based on semantic chart parser and linguistic frames
Exploring the LKB
based on linguistic frames

5. Architecture CEStore Message PreProcessor Stanford Parser Entity
Domain
Noun
Sense –
Concept
Domain
Verb
Sense –
Concept
SYNCOIN
Reports
Reference
(places,
orgs)
WordNet
VerbNet
Message
PreProcessor
Stanford
Parser
Entity
Extractor
Situation
Extractor
CEStore
Domain
Specific
Reasoning CE
“Styliser”
Analysts
“Helper”
Missing
word-concept
links

6. Common Model of language

7. Purpose
A comprehensive CE model of how language works, consistent with linguistic practice
allows us to build fact extraction applications configured by CE
Allows specification of a rich lexicon (or dictionary)
how words express concepts in the CE domain model
how different forms of words are related (eg singular/plural)
representing semantic information to guide parsing and handle ambiguities
To be consistent in the future with existing lexical resources
Allows us to use Cambridge technology and lexicon (ERG, LKB)
To facilitate use of shallow and deep semantics in a consistent manner
Can build “simple” lexical processing but using the same model as more complex lexical processing

8. Some Key Concepts in the Lexicon
as written on the page
word
words seen as inflections, and parts of speech
the word |hits|
is written as
grammatical form
unique meanings
the plural noun |hits_NNS|
is a form of
is an inflection of
word sense
concepts
the singular noun |hit_NN|
the noun sense |HIT_n_1|
expresses
the verb sense |HIT_v_8|
the present third singular verb |hits_VBZ|
CE concept
is an inflection of
the entity concept hit
the entity concept ‘hit situation’
the base form verb |hit_VB|
conceptualise
a ~ hit ~ H that is a force.
a ~ hit situation ~ S that is an attack.

9. Building Meaning Grammatical disambiguation Semantic disambiguation
(POS tagging)
word
Semantic disambiguation
(context,
phrase structures,
constraints, avoiding inconsistencies)
the word |hits|
is written as
grammatical form
the plural noun |hits_NNS|
is a form of
is an inflection of
word sense
the singular noun |hit_NN|
the noun sense |HIT_n_1|
expresses
the verb sense |HIT_v_8|
the present third singular verb |hits_VBZ|
CE concept
is an inflection of
the entity concept hit
the entity concept ‘hit situation’
the base form verb |hit_VB|
conceptualise
a ~ hit ~ H that is a force.
a ~ hit situation ~ S that is an attack.

10. Other Concepts in the Lexical Model
Parts of Speech
Phrases
head/dependent
Features
tense/aspect/voice
number/person
Roles
agent/patient/theme/location…
Selectional Restrictions (as rules)
Ambiguities
assumption based reasoning
Linguistic Frames

11. Information Flow NPs VPs PPs parse tree noun verb chain dependency
wordnet
conceptual
model
“expresses”
reference
entities
parse tree
NPs
verb chain
noun
dependency
fragment
typed entities
specific entities
VPs
PPs
verb
dependency
fragment
situations
and generic
roles
specific
roles and relations
tenses
verbnet
conceptual
model
“expresses”
temporal
relations
dialog context
containment
relations

12. Analysing Tense, Aspect, Voice, Mood, Case
Tense: the relationship between a situation and the time of utterance
before,after,during
Aspect: an indication of the time “profile”, eg:
an instant in time (punctual) vs a duration
completed or ongoing
Combining Tense and Aspect:
past simple (tense = before, aspect = punctual or habitual)
he drank decaffinated coffee
Voice: active or passive
Mood: should/must/would/can/could
Case: singular/plural male/female 1st/2nd/3rd
Negation? : “not loved”.
Complex Area!
just starting here

13. Represent as features the verb phrase #1
has the person category ‘first’ as feature and
has the number category ‘singular’ as feature and
has the tense category ‘past’ as feature and
has the aspect category ‘progressive’ as feature …
Actually we will assign features to verb chains not individual VPs, see later

14. Detecting chains of nested VPs
“are conducting” …
the verb phrase #101
has as head
has as dependent
the present tense verb
|are_VBP|
the verb phrase #102
( there is a verb phrase chain named #27 that
has '2' as length and
has the verb phrase #101 as first phrase and
has the verb phrase #102 as last phrase and
has the present tense verb |are_VBP| as first item and
has the gerund participle |conducting_VBG| as main verb ). …
has as head
the gerund participle
|conducting_VBG|

15. Assigning features to chains
if ( the verb phrase chain F has '2' as length and
has the verb |are_VBP| as first item and
has the gerund participle GP as main verb )
then
( the verb phrase chain F has the tense category 'present' as feature and
has the aspect category 'progressive' as feature and
has the voice category 'active' as feature ).
Note we cannot tell the person or number features, since could be “you-sg/you-pl/they”
Should we also assign features to all/some VPs in the chain?

16. Sample Rules if ( the verb phrase P1 if
“has …ed” is active and past
find a 2 verb phrase chain if
( the verb phrase P1
has the verb phrase P2 as dependent and
has the verb V1 as head ) and
( the verb phrase P2 has the verb V2 as head ) and
( it is false that
there is a verb phrase named P0 that
has the verb phrase P1 as dependent ) and
( it is false that the verb phrase P2 has the verb phrase P3 as dependent )
then
( there is a verb phrase chain named F that
has '2' as length and
has the verb phrase P1 as first phrase and
has the verb phrase P2 as last phrase and
has the verb V1 as first item and
has the verb V2 as main verb ). if
( the verb phrase chain F
has '2' as length and
has the verb |has_VBZ| as first item and
has the past participle PP as main verb )
then
( the verb phrase chain F has the tense category 'past' as feature and
has the voice category 'active' as feature ).

17. Timing constraints in CE
We already have a conceptualisation of time in CE from the CPM work.
There are a set of temporal constraints that can be applied to temporal entities based on Allen’s interval logic
A situation is a type of temporal entity
TIME
the situation s1 occurs after the situation s2 s2 s1
unfortuately this reverses the time layout – maybe occurs before is better
the situation s1 occurs immediately after the situation s2 s2 s1 s1
the situation s1 occurs within the situation s2 s2 s1
the situation s1 is ended by the situation s2 s2
etc etc.

18. Assign timing constraints to situations based on the utterance
Utterance: they are conducting surveys
TIME
conduct situation
utterance
occurs within
stands for
utters
is referenced in
verb phrase
sentence
(is parsed from)
“they are conducting surveys”
has as feature
tense category
‘past’ if
( the verb phrase VP has the tense category 'past' and
stands for the situation SIT ) and
( the situation SIT is referenced in the sentence S ) and
( the utterance UT utters the sentence S )
then
( the utterance UT occurs after the situation S ).
This is over simplified

19. More complex case! Utterance:he said the train would come TIME
say situation s1
utterance
occurs after
with
stands for
utters
is referenced in
verb phrase v1
sentence
(is parsed from)
simultaneously
“he said the train would come”
features
tam category
‘past simple’
MAGIC HERE
occurs
utterance
come situation s2
occurs after
how do we get from “he said the train would come” to “the train will come”?
utters
sentence
stands for
“the train will come”
(is parsed from)
verb phrase v2
tam category
‘future simple’
features

20. Timings are Useful Given the temporal relations we can now:
calculate ordering of events
calculate precise times of events
Useful for:
story telling
“forensic” analysis

21. Dependency structures

22. Verb/Noun dependency fragments
Packaging up all of the grammatical “positions” from the parse tree into a single structure:
specifier
complement
modifier
For nouns:
the noun dependency fragment #1
has the singular noun |lack_NN| as noun and
has the prepositional phrase #2 as first complement phrase.
For verbs:
the verb dependency fragment #3
has the gerund participle |conducting_VBG| as verb and
has the noun phrase #208_NP as subject noun phrase and
has the verb phrase #210_VP as verb phrase and
has the noun phrase #211_NP as first object noun phrase and
has the prepositional phrase #218_PP as first complement prepositional phrase.
is it better to just have “first complement phrase” etc?

23. Building a CE lexicon from Verbnet and Wordnet

24. but which sense? could use gloss to tell user which is which
Using WordNet
Generate list of nouns and their inflections:
the plural noun |surveys_NNS| is an inflection of the singular noun |survey_NN|.
the singular noun |survey_NN| is an inflection of the singular noun |survey_NN|.
Create links to noun sense:
the singular noun |survey_NN| is a form of the noun sense |SURVEY_N_1|.
Link from noun sense to conceptual model must be done by user:
the noun sense |SURVEY_N_1| expresses the entity concept ‘survey’.
Analyst Helper can suggest based on NPs with missing semantics
but which sense? could use gloss to tell user which is which
work done in the past to construct synsets, hyponyms etc to help better suggestions via Analyst Helper

25. Using VerbNet Generate list of verbs and their inflections:
the past tense verb |conducted_VBD| is an inflection of the base form verb |conduct_VB|. …
Create links to verb sense:
the base form verb |conduct_VB| is a form of the verb sense |CONDUCT_V_1|.
Link from verb sense to conceptual model must be done by user:
the verb sense |CONDUCT_V_1| expresses the entity concept ‘conduct situation’.
Analyst Helper can suggest based on VPs with missing semantics

26. VerbNet for computing roles
We can compute situation ROLES from information in VerbNet together with parse structure
Use Verbnet to provide the grammatical patterns and roles for each verb:
the verbnet frame 'hit-18.1_1'
has 'NP V NP' as grammatical description and
has the verbnet noun pattern 'NP' as specifier pattern and
has the attribute concept 'agent role' as specifier role and
has the verbnet noun pattern 'NP' as first complement pattern and
has the attribute concept 'patient role' as first complement role.
For example, the verb “hit” in one sense:
the verb sense |HIT_V_1| is constrained by the verbnet frame ‘hit-18.1_1’.
Use this to calculate the roles for things described in parse trees, based upon the verb.
for each grammatical “position” define the type and the role

27. Steps Grammatical patterns Finding the Verb Sense for a given verb
rules to turn specific parse tree patterns into “fragments” eg a NP_V_NP fragment
Finding the Verb Sense for a given verb
the base form verb V expresses the verb sense VS
Verbnet Frames
the verbnet frame 'hit-18.1_1'
has 'NP V NP' as grammatical description and
has the verbnet noun pattern 'NP' as specifier pattern and
has the attribute concept 'agent role' as specifier role and
has the verbnet noun pattern 'NP' as first complement pattern and
has the attribute concept 'patient role' as first complement role.
Linking Verb Sense to Verbnet Frame
the verb sense |HIT_V_1| is constrained by the verbnet frame ‘hit-18.1_1’.
Mapping from Verb Sense to situation type
the verb sense VS expresses the entity concept ‘XXX’
Mapping to situation attributes and relations
the hit situation S
agentifies the entity concept hitter and
patientifies the entity concept ‘thing hit’ and
is viewed relationally as the relation concept hits.
Just the template, to be overridden by domain user
RESULT
the thing T1 hits the thing T2.
the hit situation S1
has the thing T1 as hitter and
has the thing T2 as thing hit.
“ifications” open to discussion!

28. specialising the roles into domain specific roles and relations
In more detail SP NP VP V NP
the verb dependency fragment f1 has
the past tense verb |hit_VBD| as verb and
has the noun phrase NP1 as subject noun phrase and
has the verb phrase VP as verb phrase and
has the noun phrase NP2 as first object noun phrase.
hit
fragment patterns in rules
the past tense verb |hit_VBD| is an
inflection of the verb |hit_VB|.
the verb |hit_VB| is a form of the
verb sense |HIT_V_1|.
the noun phrase NP1 stands for the thing T1.
the noun phrase NP2 stands for the thing T2.
the verb phrase VP stands for the situation S1.
the verb sense |HIT_V_1| is constrained by
the verbnet frame ‘hit-18.1_1’.
LEXICAL CURVE
matching the grammatical “positions” against the verbnet frame patterns
the verbnet frame hit-18.1_1’ has
the verbnet pattern ‘NP’ as specifier pattern and
has ‘agent role‘ as specifier role and
has the verbnet noun pattern 'NP' as first complement
pattern and
has ‘patient role’ as first complement role.
the situation S1
has the thing T1 as agent role and
has the thing T2 as patient role.
the noun sense |HIT_N_1| nominalises the verb sense |HIT_V_1|.
DOMAIN CURVE
specialising the roles into domain specific roles and relations
the noun sense |HIT_N_1| expresses
the entity concept ‘hit situation’.
the hit situation S
agentifies the entity concept ‘hitter’ and
patientifies the entity concept ‘thing hit’ and
is viewed relationally as the relation concept ‘hits’.
the hit situation S1
has the thing T1 as hitter and
has the thing T2 as thing hit.
the thing T1 hits the thing T2.

29. Sample Rules – Lexical Curve
find a fragment with a subject from active voice verb chain in context of a sentence phrase
for a given active verb fragment, get a role of a situation from the verb sense associated with the subject verb phrase if
( the sentence phrase SP
has the noun phrase NP as dependent and
has the verb phrase VP as head ) and
( the verb phrase chain VBC
has the verb phrase VP as first phrase and
has the verb phrase VP1 as last phrase and
has the verb V as main verb and
has the voice category 'active' as feature ) and
then
( there is a verb dependency fragment named F that
has the verb V as verb and
has the noun phrase NP as subject noun phrase and
has the verb phrase VP1 as verb phrase and
has the voice category 'active' as feature ) . if
( the verb dependency fragment F
has the noun phrase NP as subject noun phrase and
has the verb phrase VP as verb phrase and
has the voice category 'active' as feature) and
( the noun phrase NP stands for the thing T ) and
( the verb phrase VP
is associated with the verb sense VS and
stands for the situation S ) and
( the verb sense VS
is constrained by the verbnet frame VNF ) and
( the verbnet frame VNF
has the verbnet pattern 'NP' as specifier pattern and
has the value ROLE as specifier role )
then
( the situation S has the thing T as #ROLE ) .

30. Sample Rule – Domain curve
apply the domain specific role name to the agent of a situation if
( the situation S is an #EC and
has the thing A as agent role ) and
( the situation concept EC agentifies the attribute concept AC )
then
( the situation S has the thing A as #AC ) ).

31. Issue Some verbs (eg “hit” ) have different patterns in Verbnet:
some are grammatical alternatives:
John hit the dog
John hit the dog with the stick
some are under different senses:
John hit the dog (filed under “hit”)
John hit the hill (filed under “reach”)
Do we:
just encode the basic ones for now
handle this using assumptions?
add selectional restrictions to remove/reduce alternatives?
eg agent of the sense “|HIT_V_1|” must be volitional
Need to involve both the lexicon and the domain in applying selectional restrictions
We can extract more from Verbnet, eg selectional restrictions, event semantics
This is the current approach, although other work has been reported to suggest how assumptions could be used

32. from verbnet NP V NP, roles + domain
Sample Sentence
HTT are conducting surveys in Adhamiya to judge the level of support for Bath’est return.
the organisation #64 known as |Htt| conducts the survey #59.
from reference entity
from verbnet NP V NP, roles + domain
from wordnet + domain

33. Summary of Generic Semantic Processing

34. Basic Semantics - Nouns
Syntax
Conceptual Model
Via
Noun phrase
thing
“an NP stands for a thing”
specific type
head noun, inflections, noun sense [WN]
expresses
reference entities (places, organisations etc)
“same as” based on proper names, propagation of information

35. Basic Semantics - Verbs
Syntax
Conceptual Model
Via
Verb Phrase
situation
“a VP stands for situation”
specific situation (e.g. “conduct situation”
head verb, inflections, verb sense [VN]
expresses
timing information
compound verb analysis
general roles
grammatical patterns (eg NP V NP) [VN],
compound verb analysis (active/passive),
limited selectional restriction
specific relations
“agentifies”, “patientifies” etc

36. Basic Semantics - Prepositions
Syntax
Conceptual Model
Via
Prepositions
relations
specific rules eg “in” is a container”
too simplistic!

37. Not yet handled mutual exclusivity of alternatives
full selectional restrictions
full set of VerbNet:
grammatical patterns
selectional restrictions
event semantics
more types of prepositional phrases
adjectives
relational clauses, subclauses

38. Domain Reasoning Using the facts extracted to perform reasoning tasks
This section is provided to show the complete picture of the current work, but was taken from the ACITA12 paper

39. Domain Semantics “Communications”
02/24/10 - Cell call is monitored between unknown caller ( ) in Amin to Amir Mahallati ( ) in Bayaa. The unidentified caller stated: "The team is a failure! The carpet doesn't match! The carpet maker needs to be replaced." The recipient said: "The measurements were perfect, the installers must have failed.”
“Communications”
SYNCOIN reports speak about monitoring communications between people together with the things that were said
conceptualise
a ~ communication ~ C that
has the agent A as ~ caller ~ and
has the agent B as ~ recipient ~ and
has the value D as ~ date ~ and
has the value T as ~ time ~ and
has the value V1 as ~ caller utterance ~ and
has the value V2 as ~ recipient utterance ~.
the communication C ~ is from ~ the place FROM and ~ is to ~ the place TO.
Could be used to analyse connections between agents over time and over geography

40. Specific communications examples
“the thing being “done to” in a communications monitoring is actually a communication”
if ( the situation S is a communications monitoring and has the thing T as patient role )
then
( the communications monitoring S monitors the communication T ).
“the communication comes from the place where the caller is” if
( the communication C has the agent A as caller ) and
( the agent A is located in the place P )
( the communication C is from the place P ).
No mention of syntax or phrase structure, you don’t need to be a linguist!
Interpreting the situation
This is where rationale comes in
Each of these are simple steps, but require information derived from other steps:
How do we know the caller agent in a communication?
How do we know where the agent is located?

41. Rationale shows the steps leading to a fact
Looks complex, but each step is simple. It is possible to keep everything in mind by focussing on a small area at once.
To get diagram: run qk_debug. Load Model (basic) load inSen3, runrules. Describe th_cestore , find the is from fact. Select. Then switch to poshce, then r^^

42. Example communication
there is a call named '#3' that
has the agent #5 as caller and
has the agent #15 known as |Amir Mahallati| as recipient and
has '02/24/10' as date and
has 'The team is a failure! The carpet doesn\'t match! The carpet maker needs to be as caller utterance and
has 'The measurements were perfect, the installers must have as recipient utterance and
is from the place #13 known as |Amin| and
is to the place #21 known as |Bayaa|.
CE Store can display communications on maps, from CE extracted facts

43. The analyst has a new idea!
Why don’t we look for “back to back communications with same middle man”?
Analyst creates a new concept
conceptualise
a ~ replay conversation ~ RC that is a thing and
has the agent A as ~ first caller ~ and
has the agent B as ~ middle man ~ and
has the agent C as ~ second recipient ~ and
has the communication COM1 as ~ first communication ~ and
has the communication COM2 as ~ second communication ~ and
has the sequence ( the value ~ first communication ~ and
the value ~ second communication ~ ) as identifier.
Analyst creates a new rule to define them
if ( there is a communication COM1 that has the agent CALLER1 as caller and
has the agent RECIPIENT1 as recipient ) and
( there is a communication COM2 that has the agent CALLER2 as caller and
has the agent RECIPIENT2 as recipient ) and
( the agent RECIPIENT1 is the same as the agent CALLER2 ) and
then
( there is a replay conversation named RC that
has the communication COM1 as first communication and
has the communication COM2 as second communication and
has the agent CALLER1 as first caller and
has the agent RECIPIENT1 as middle man and
has the agent RECIPIENT2 as second recipient ).
Are they passing on the same information to each other?
Perhaps we can track linkages between groups?
first caller
second recipient
middleman
<> Means concatenation. Here the id of the replay conversation is formed from the ids of the comms. This is unique
First communication
Second communication

44. Exploring the analyst’s concepts
“add the utterances of the caller and the recipient to see what they are saying”
Now we can create the concept of a code with code words, and rules to infer information:
Communications contain code words
More possibilities:
Showing the spread of codewords on a map
Use of codes to suggest organisational structure
Looks like some form of code: carpet = device?

45. Holistic View of rules
Multiple skills of language, semantics, and domain specific reasoning involved

46. We can use “linguistic frames” to define CE extensions
Extensions to CE
Need to extend the CE language for greater expressibility:
adjectives
prepositions
more readable names in context …
We can use “linguistic frames” to define CE extensions

47. Investigating “Linguistic Frames”
A linguistic frame is a CE-based structure that defines a step in the NL processing by specifying:
the syntax pattern
the preconditions/constraints
the resulting semantics
By applying all of the steps to a sentence we can generate a parse tree and construct the semantics of the sentence:
semantics of one part is composed from the semantics of its subcomponents
using a chart parser
This can be applied to parsing both CE and NL
can define CE extensions
language complexity
Basic CE
Extended CE
Natural Language

48. Structure of Linguistic Frame
the frame finds an instance of this component in the sentence
WHEN
there is a linguistic frame named F that
defines the <phrase type> S and
has the sequence SEQ as syntax and
has the statement that CE_STATEMENT as preconditions and
has the statement that CE_STATEMENT as semantics and
has the thing T as portal variable.
this sequence of words and phrases is present in the sentence
AND
these statements are true
IN WHICH CASE
these statements define the semantics of the component
and the result is passed back up the tree though this variable

49. Defining Basic CE in linguistic frames!
there is a linguistic frame named np1 that
defines the noun phrase NP and
has the sequence
( the determiner '|the_DT|' , the singular noun COMMON ,
and the proper noun PN )
as syntax and
has the statement that
( the proper noun PN has the value NAME as written form ) and
( the singular noun COMMON is a form of the noun sense NS ) and
( the noun sense NS expresses the entity concept EC )
as preconditions and
( there is a thing named NAME that is an #EC )
as semantics and
has the thing NAME as portal variable.
syntax
semantics

50. Extensions to CE can be defined…
Intuition: we can represent adjectives as “XXX thing”,
eg convert “John is red” to “John is a red thing”
there is a linguistic frame named adjective_predicate that
defines the verb phrase VP and
has the sequence
( the present third singular verb '|is_VBZ|' , and the adjective ADJ )
as syntax and
has the statement that
( the adjective ADJ has the value WF as written form ) and
( the value ECTERM = the value WF <> the constant ‘ thing’) and
( the entity concept EC has the value ECTERM as concept name )
as preconditions and
( the thing X is a #EC )
as semantics and
has the thing X as portal variable.
clunky way to say that the new concept is called, for example ‘red thing’

51. Representing the LKB in CE
The Linguistic Knowledge Builder (LKB) is a syntactic/semantic system developed by Prof Ann Copestake, University of Cambridge
In BPP13 we are planning to use resources from the LKB, such as lexicons, parsing rules and semantic processing
as an initial step, we are exploring the use of linguistic frames to represent LKB grammars in CE

52. LKB “Rules” s_rule := phrase & [ CATEG s, NUMAGR #1,
ARGS [ FIRST [ CATEG np,
NUMAGR #1 ],
REST [ FIRST [ CATEG vp,
REST *null* ]]] .
np_rule := phrase &
[ CATEG np,
ARGS [ FIRST [ CATEG det,
REST [ FIRST [ CATEG n,
some simple rules in LKB to force agreement of number between a subject noun phrase and the verb, as in “the dogs sleep” (number = plural)

53. Equivalent in CE Linguistic frames
there is a linguistic frame named s1 that
defines the sentence phrase S and
has the sequence
( the noun phrase NP , and the verb V )
as syntax and
has the statement that
the noun phrase NP has the agreement AGR
as feature and
the verb V has the agreement AGR
as feature
as precondition and
the sentence phrase S has the agreement AGR
as semantics.
there is a linguistic frame named np1 that
defines the noun phrase NP and
has the sequence
( the determiner DT , and the noun N )
as syntax and
has the statement that
the determiner DT has the agreement AGR as feature and
the noun N has the agreement AGR
as feature
as precondition and
the noun phrase NP has the agreement AGR
as semantics.
This is just a start, more complex LKB grammars must be addressed

54. Converging NL and CNL
The work on CE extensions and on the representation of the LKB in CE is starting a convergence of Natural Language and Controlled Natural Language parsing, as per the BPP13 proposal…

55. Converging NL and CNL parsers
Natural Language
Extended CE
Reference
English
Grammar
CNL
parser NL
parser
lexicon
Semantic
Theory
conceptual
model
Basic CE
Is the lexicon just a set of linguistic frames?
Better understanding of linguistics
Increase stylistic expressibility of CE