This file contains a set of sentences that were part of the test data for
the algorithms described in the paper
{\em The Acquisition of Common Sense Knowledge by Being Told: an Application
of NLP to Itself}. The entry ``Comment:'' explains some outputs
of the algorithm for some sentences.
The target word followed by a colon precedes the
sentences for that word. The senses in this file are WordNet 1.6 senses. 
These are the target words:

conductor, striker, operation, arm, nail, table, rally, plant, chair, cell
beam, blow, brig, dish, spring, pot, bed, ball, colony, sign
demonstrator, plot, crane, port, pen, star, paddle, mast, article, dam
bat, pocket, coat, bay, cabin 

pot:
(p "Cyclamens are cultivated indoors in pots")

Comment: Output of the semantic interpreter:
(CLAUSE CL41 (P "Cyclamens are cultivated indoors in pots")
 (SUBJ ((NOUN CYCLAMENS)) ((FLOWER1 CYCLAMEN1)) (THEME))
 (VERB CULTIVATED
       ((AUX (ARE)) (MAIN-VERB CULTIVATE CULTIVATED) (VOICE PASSIVE))
       CULTIVATE (CULTIVATE1 CULTIVATE2) SUPPORTED BY 1 SRS)
 (ADVERB ((ADVERB INDOORS)) ((INDOORS INDOORS)) (AT-LOC))
 (PREP IN
       (PREP-NP ((NOUN POTS))
        ((INSTRUMENTALITY POT1 POT4) (TOILET2 POT2) (MARIJUANA1 POT7)
         (BELLY2 POT8) (POTENTIOMETER2 POT9) (CANNABIS2 POT10))
        (AT-LOC) ((MEASURE-QUANTUM POT3 POT5) (STAKE4 POT6))
        (AT-LOC-ABSTRACTION))
       (ATTACH VERB CONFIDENCE WEAK)))

(ANSWER-ALGORITHM1=
 ((POT4 CULTIVATE (THEME CYCLAMEN1) (AT-LOC POT4))
  (CYCLAMEN1 CULTIVATE (THEME CYCLAMEN1) (AT-LOC POT4)))) 

Comment: Two interpreted glosses solved the noun sense of "pot:"
one stored in pot4, and another one in plant2. Cyclament1 inherited
from plant2.

(p "the farmer likes the plants in the pot")
(ANSWER-ALGORITM2= (PLANT2 POT4))
Comment: Solution of [NP PP] segment "the plants in the pot"

(p "they boil potatoes in a covered pot")
(ANSWER-ALGORITHM1=
 ((POT1 COOK (AGENT THEY) (THEME  POTATO1)  (IN-COOKING-UTENSIL POT1)))) 

(p " pots can also be planted with bulbs")
(ANSWER-ALGORITHM1= ((POT4 PUT (THEME BULB1) (GOAL POT4)))) 

sign:
(p " signs  along downtown streets flash advertisements")
 (ANSWER-ALGORITHM2= (SIGN2 STREET2 STREET1))
 Comment: Solution of [NP PP] segment "signs along downtown streets"

 (p "the sign outside the church offended some people") 
 (ANSWER-ALGORITHM2= (SIGN2 CHURCH2)) 

 (p " The ancient Greeks and Romans also hung  signs outside their shops")
(ANSWER-ALGORITHM1=
 ((SIGN2 PUT (THEME SIGN2) (OUTSIDE-OF-LOCATION SHOP1)))) 
Comment: The interpreted gloss in sign2 has the verb predicate
put, and the verb predicate of the sentence being interpreted is
HANG-SOMETHING which has PUT as a super-predicate. This is the
hierarchy: HANG-SOMETHING -> FASTEN -> ATTACH -> PUT -> CAUSE-TO-CHANGE-LOCATION

striker:
 (p "the strikers demanded more pay") 
(ANSWER-ALGORITHM1= ((STRIKER3 REQUEST (AGENT STRIKER3) (THEME PAY1)))) 

(p "anarchists were protesting police tactics against strikers at an
          industrial plant")
(ANSWER-ALGORITHM1= ((STRIKER3 GO-ON-STRIKE (AT-LOC INDUSTRIAL_PLANT1))))

operation:
(p "This operation frees the stapes from the bony growth")
(ANSWER-ALGORITHM1= ((OPERATION7 REMOVE (THEME STAPES1)))) 

(p "But in this operation, Barnard did not remove the patient's damaged heart")
(ANSWER-ALGORITHM1=
 ((OPERATION7 REMOVE (AT-ACTIVITY OPERATION7) (THEME HEART2)))) 

 (p "A woman may have to undergo an operation called a Caesarean section
                    to deliver a baby.")
(ANSWER-ALGORITHM1=
 ((OPERATION7 UNDERGO-EXPERIENCE (EXPERIENCER WOMAN3 WOMAN4 WOMAN1)
  (EXPERIENCE OPERATION7)))) 

arm:
 (p "the inflammation spread to the arms")
(ANSWER-ALGORITHM1=
 ((INFLAMMATION1 SPREAD-A-DISEASE (THEME INFLAMMATION1) (GOAL ARM1) ))) 

 (p "Germany sold arms to Italy")
(ANSWER-ALGORITHM1=
 ((ARM3 SELL (AGENT GERMANY1) (THEME ARM3) (TO-POSS ITALY1)))) 

(p "The kings used the money to buy arms")
(ANSWER-ALGORITHM1=
 ((ARM3 SELL (AGENT KING1 KING2 KING3) (THEME ARM3))))
Comment: The gloss has "SELL" and the sentence "BUY" which belong
to the same hierarchy TRANSFER-OF-POSSESSION.

(p "The arm had to be cut off in a crude amputation ")
(ANSWER-ALGORITHM1-B=
 ((AMPUTATION2 NIL (AT-ACTIVITY AMPUTATION2) (THEME ARM1))))
Comment: There are no glosses for "amputation." However,
AMPUTATION2 is inheriting glosses from OPERATION7. No gloss
in OPERATION7 uses he verb "cut off." The verb predicate for "cut off"
is SEPARATE which belongs to a different hierarchy as "REMOVE." Hence,
the verb predicate does not match, only the semantic roles match,
and ALGORITHM2 finds the solution.

(p "United States supplied Israel with arms")
(ANSWER-ALGORITHM1-B=
 ((ARM3 NIL (AGENT UNITED_STATES1) (THEME ARM3) (TO-POSS ISRAEL1))))
Comment: Same as the immediately preceding sentence mutatis mutandis.

 (p "he was smuggling arms to Serbs")
(ANSWER-ALGORITHM1=  ( (ARM3 SELL (AGENT HE) (THEME ARM3))))


(p "in this game, they hit the ball with the arms")
(ANSWER-ALGORITHM1= 
 ((BALL1 HIT-SOMETHING (AT-ACTIVITY GAME2) (THEME BALL1)  (INSTRUMENT ARM1))
  (GAME2 HIT-SOMETHING (AT-ACTIVITY GAME2) (THEME BALL1)   (INSTRUMENT ARM1))))
Comment: Two glosses solve the sense of "arm," one stored in BALL1 and
another one in GAME2.

 (p "they wear guns under the arm")
(ANSWER-ALGORITHM1=
 ((ARM1 PUT (AGENT THEY) (THEME GUN1 GUN1) (GOAL ARM1)))) 

star:
(p "he tore the stars apart")
(ANSWER-ALGORITHM1= ((STAR5 BREAK-SEPARATE (AGENT HE) (THEME STAR5)))) 

(p "she decorated the car with stars")
 (STAR5 DECORATE (AGENT SHE) (GOAL CAR5 CAR4 CAR3 CAR2 CAR1)
   (THEME STAR5) YES))) 

nail:
(p "she hammered the nail into the table")
(ANSWER-ALGORITHM1=
 ((NAIL2 HAMMER-PUSH (AGENT SHE) (THEME NAIL2) (GOAL TABLE3 TABLE2)
 )))

(p "she hammered the nail into the tree")
(ANSWER-ALGORITHM1=
 ((NAIL2 HAMMER-PUSH (AGENT SHE) (THEME NAIL2) (GOAL TREE1)))) 

(p "They fasten plywood sheets with nails") 
(ANSWER-ALGORITHM1=
 ((NAIL2 ATTACH (AGENT THEY) (THEME PLYWOOD1) (INSTRUMENT NAIL2)))) 

(p "The violin maker glues the violin parts together, using no nails)
 (ANSWER-ALGORITHM1= ((NAIL2 ATTACH (AGENT MAKER1)))) 
Comment: Answer obtained by the minimum: the verb predicate and one semantic
role.

 (p "he knocked the nail through the pipe")
 (ANSWER-ALGORITHM1= ((NAIL2 HIT-SOMETHING (AGENT HE) (THEME NAIL2)))) 

chair:
(p "Executioners seated the prisoner in a  chair")
(ANSWER-ALGORITHM1=
 ((CHAIR1 PUT (AGENT EXECUTIONER1) (THEME PRISONER1) (GOAL CHAIR1 CHAIR4)
 )))

 (p "they make chairs with three legs")
(ANSWER-ALGORITHM1=
 ((CHAIR1 MAKE-OR-CREATE-SOMETHING (THEME CHAIR1 CHAIR4))))

conductor:
(p "the conductor collected one dollar from the commuters")

(ANSWER-ALGORITHM1=  
 ((CONDUCTOR3 COLLECT-MONEY (AGENT CONDUCTOR3) (THEME DOLLAR1)
  (FROM-POSS COMMUTER2)))) 

 (p "the conductor directs the orchestra with a baton")
(ANSWER-ALGORITHM1=
 ((BATON1 DIRECT-FILMS-ACTORS-PERFOMANCES-ORGANIZATION
   (AGENT CONDUCTOR1) (THEME ORCHESTRA1) (INSTRUMENT BATON1))
  (CONDUCTOR1 MANAGE (AGENT CONDUCTOR1) (THEME ORCHESTRA1)))) 

rally:
(p "he addressed the strikers at the rally")
(ANSWER-ALGORITHM1= ((RALLY1 SPEAK-TALK (AT-EVENT RALLY1) (AGENT HE)))) 

bed:
 (p "hyacinth can be planted in open beds")
(ANSWER-ALGORITHM1= ((BED2 PLANT-A-PLANT (THEME HYACINTH2) (GOAL BED2)))) 

spring:
 (p "Mary carried water in a pitcher from a nearby spring to her husband")
(ANSWER-ALGORITHM1-B=
 ((SPRING3 NIL (AGENT MARY1) (THEME WATER1) (SOURCE SPRING3))))

(p "many birds live in springs") 
(ANSWER-ALGORITHM1=
 ((LIVE-IN-A-PLACE LIVE-IN-A-PLACE (AGENT BIRD1) (AT-LOC SPRING3)))) 

(p "the birds drank in the spring")
(ANSWER-ALGORITHM1= ((SPRING3 DRINK (AGENT BIRD1) (SOURCE SPRING3)))) 

(p "white-tailed deer pause to  drink from  springs ")
(ANSWER-ALGORITHM1=
 ((SPRING3 DRINK (AGENT WHITE-TAILED_DEER1) (SOURCE SPRING3)))) 

(p "she put the springs on the table")
(ANSWER-ALGORITHM1=
 ((TABLE2 PUT (AGENT SHE) (THEME SPRING2) (GOAL TABLE2)))) 

 (p "during the drought, the springs dry up")
(ANSWER-ALGORITHM1=
 ((DROUGHT1 DRY (DURING-STATE DROUGHT1) (THEME SPRING3)))) 

(p "the springs on the mountains disappeared")
(ANSWER-ALGORITHM2= (SPRING3 MOUNTAIN1)) 

dish:
 (p "the dish consists of red chilies")
(ANSWER-ALGORITHM1=
 ((DISH2 MAKE-OR-CREATE-SOMETHING (THEME DISH2) (OF-STUFF CHILE2)))) 

 (p " Some people add fiery spices to their <<  dishes ")

  (SPICE2 ADD-SOMETHING-TO-SOMETHING (AGENT PEOPLE1) (THEME SPICE2)
   (GOAL DISH2))
  (DISH2 ADD-SOMETHING-TO-SOMETHING (AGENT PEOPLE1) (THEME SPICE2)
   (GOAL DISH2))))

(p " A traditional Jordanian   dish   features lamb ")
(ANSWER-ALGORITHM1=
 ((LAMB4 INCLUDE (THING-DESCRIBED DISH2) (ATTRIBUTE-DESCRIBED LAMB4))
  (DISH2 INCLUDE (THING-DESCRIBED DISH2) (ATTRIBUTE-DESCRIBED LAMB4)
  ))))
Comment: No glosses for "lamb."  LAMB4 inherits from FOOD1

 (p "Fish  is a favorite  dish  for lunch")
(ANSWER-ALGORITHM1=
 ((DISH2 IS-A (THING-DESCRIBED FISH2) (ATTRIBUTE-DESCRIBED DISH2))
  (FISH2 IS-A (THING-DESCRIBED FISH2) (ATTRIBUTE-DESCRIBED DISH2)))) 

(p "they wash the dishes in the morning")
(ANSWER-ALGORITHM1= ((DISH1 CLEAN (AGENT THEY) (THEME  DISH1)))) 

 (p "Other popular   dishes   include paella ")
(ANSWER-ALGORITHM1=
 ((DISH2 INCLUDE (THING-DESCRIBED DISH2) (ATTRIBUTE-DESCRIBED PAELLA1)
  ))) 

(p " the pasta can be made up quickly into dishes without waste")
(ANSWER-ALGORITHM1=
 ((PASTA1 MAKE-OR-CREATE-SOMETHING (THEME PASTA1) (ENDING-STATE DISH2)
 ))) 

(p "he broke the dish containing the chile")
Comment: Output of the semantic interpreter

(CLAUSE CL73 (P "he broke the dish containing the chile")
 (SUBJ ((PRON HE)) ((PERSON HE)) (AGENT))
 (VERB BROKE ((MAIN-VERB BREAK BROKE)) BREAK-PHYSICAL-THING
       (BREAK2 BREAK4 BREAK5) SUPPORTED BY 2 SRS)
 (OBJ ((DFART THE) (NOUN DISH))
      ((CROCKERY1 DISH1) (CONTAINER1 DISH1)
       (DIRECTIONAL_ANTENNA1 DISH5))
      (THEME)
      (RELATIVE CLAUSE CL77
                (SUBJ ((DFART THE) (NOUN DISH))
                      ((CROCKERY1 DISH1) (CONTAINER1 DISH1)
                       (DIRECTIONAL_ANTENNA1 DISH5))
                      (THING-DESCRIBED))
                (VERB CONTAINING ((MAIN-VERB CONTAIN CONTAINING))
                      INCLUDE (CONTAIN1 CONTAIN2) SUPPORTED BY 2 SRS)
                (OBJ ((DFART THE) (NOUN CHILE))
                     ((SOUTH_AMERICAN_COUNTRY1 CHILE1) (CHILI2 CHILE2))
                     (ATTRIBUTE-DESCRIBED)))))
Comment: The semantic interpreter selects: dish1 and dish5
(ANSWER-ALGORITHM1 = NIL)
(ANSWER-ALGORITHM1-B= NIL)
Comment: No answer from algorithms 1 and 1-B

(ANSWER-ALGORITHM3= (DISH1 CHILE2)) )
Comment: ALGORITHM3 will be explained in a forthcoming paper.

ball:

(p "In squash, players hit the ball with a squash racket")
(ANSWER-ALGORITHM1=
 ((SQUASH_RACKET1 HIT-SOMETHING (AT-ACTIVITY SQUASH3) (AGENT PLAYER1)
  (THEME BALL1) (INSTRUMENT SQUASH_RACKET1))))

colony:
 (p "rats live in underground colonies")
(ANSWER-ALGORITHM1=
 ((COLONY2 LIVE-IN-A-PLACE (AGENT RAT1) (IN-GROUP COLONY2)))) 

 (p "Mexican free-tailed bats live in colonies, mostly in caves")
(ANSWER-ALGORITHM1=
 ((COLONY2 LIVE-IN-A-PLACE (AGENT BAT2) (IN-GROUP COLONY2)))) 

cell:
 (p "In the fourth cell there is a man with long dishevelled hair")

(ANSWER-ALGORITHM1-B=
 ((CELL5 NIL (THEME MAN9 MAN5 MAN4 MAN2 MAN8 MAN7 MAN6 MAN3 MAN1)
                 (AT-LOC CELL5) YES)
  (CELL6 NIL (THEME MAN9 MAN5 MAN4 MAN2 MAN8 MAN7 MAN6 MAN3 MAN1)
      (AT-LOC CELL6) YES)))
 
Comment: It could not determine if "cell" refers to a room where a
monk or nun lives (cell5), or a jail cell (cell6).

(p "he slept in the cell")
(ANSWER-ALGORITHM1= 
 ((SLEEP-REPOSE SLEEP-REPOSE (THING-DESCRIBED HE) (AT-LOC CELL5 CELL6))))

Comment: The gloss has been stored in the verb predicate, SLEEP-REPOSE. The
gloss says  "People sleep in rooms."

plant:
(p "they make vehicles in this plant")
(ANSWER-ALGORITHM1=
 ((PLANT1 MAKE-OR-CREATE-SOMETHING (THEME VEHICLE1) (AT-LOC PLANT1) )))

 (p "they make beams in this plant")
(ANSWER-ALGORITHM1=
 ((PLANT1 MAKE-OR-CREATE-SOMETHING (THEME BEAM6 BEAM2) (AT-LOC PLANT1))))

(p "they make bats in this plant")
(ANSWER-ALGORITHM1=
 ((PLANT1 MAKE-OR-CREATE-SOMETHING (THEME BAT6 BAT4 BAT5 BAT1)
  (AT-LOC PLANT1))

(p "she carried the plants on her hands")
(ANSWER-ALGORITHM1= ((PLANT2 TRANSPORT (AGENT SHE) (THEME PLANT2)))) 

 (p "she put the plants on the table")
(ANSWER-ALGORITHM1=
 ((TABLE2 PUT (AGENT SHE) (THEME PLANT2) (GOAL TABLE2)))) 

(p "the mosaic destroyed the plants")
(ANSWER-ALGORITHM1=
 ((MOSAIC2 DESTROY (THEME PLANT2) (INANIMATE-CAUSE MOSAIC2)))) 

bat:
 (p "they make bats from this tree")
(ANSWER-ALGORITHM1=
 ((BAT1 MAKE-OR-CREATE-SOMETHING (THEME BAT1) (OF-STUFF TREE1)))) 

beam:
(p "the lamp can give a very defined beam")
(ANSWER-ALGORITHM1-B= 
 ((LAMP1 NIL (INANIMATE-CAUSE LAMP1) (THEME BEAM4))
  (BEAM4 NIL (INANIMATE-CAUSE LAMP1) (THEME BEAM4)))) 
Comment: Solved by a gloss stored in lamp1, and another gloss stored in
beam4.

 (p "the oil lamp was hanging from a chain attached to a very black beam
                        above the fireplace")
(ANSWER-ALGORITHM1=
  ((BEAM2 HANG-SOMETHING (THEME OIL_LAMP1) NO))) 

(p " the tunnel was shored up with beams")
(ANSWER-ALGORITHM1=
 ((BEAM2 SUPPORT-PHYSICAL-THING (INANIMATE-CAUSE BEAM2) (THEME TUNNEL1)))) 

(p "she hanged the lamp from the beam")
(ANSWER-STEP2=
 ((BEAM2 HANG-SOMETHING (AGENT SHE) (THEME LAMP2 LAMP1) (GOAL BEAM2))))

 (p "a chain was hanging from a beam")
(ANSWER-ALGORITHM1=
 ((BEAM2 HANG-SOMETHING (THEME CHAIN9 CHAIN8 CHAIN5 CHAIN3)
  (GOAL BEAM2)))) 

(p "she climbed to a beam")
(ANSWER-ALGORITHM1=
((BALANCE6 WALK (AGENT SHE) (AT-LOC BEAM6))
  (BEAM2 WALK (AGENT SHE) (AT-LOC BEAM2))))

Comment: "Climb" belongs to the same hierarchy as "walk." Unable to
distinguish between beam2 ("long thick piece of wood or metal or concrete"),
and "balance beam" ("used by women gymnasts").


(p "the hams hung from the blackened beams")
(ANSWER-ALGORITHM1=
 ((BEAM2 HANG-SOMETHING (THEME HAM2) (GOAL BEAM2))
 (BEAM2 HANG-SOMETHING (THEME HAM1) (GOAL BEAM2)))) 

demonstrator:
 (p " troops of Czar Nicholas II killed  unarmed  demonstrators
                 in front of the Winter Palace ")
(ANSWER-ALGORITHM1=
  ((DEMONSTRATOR3 KILL (THEME DEMONSTRATOR3))))

(p " The   demonstrators  protested against the efforts of white officials ")
(ANSWER-ALGORITHM1=
 ((DEMONSTRATOR3 PROTEST (AGENT DEMONSTRATOR3)
   (AGAINST-SOMETHING EFFORT3 EFFORT4 EFFORT2 EFFORT1)))) 

plot:

 (p " All his plays have a mythical, Biblical, or historical   plot ")
(ANSWER-ALGORITHM1=
 ((PLOT3 HAVE-SOMETHING-DEFAULT (THING-DESCRIBED PLAY1)
   (ATTRIBUTE-DESCRIBED PLOT3)))) 

(p "she bought a plot in the valley")
(ALGORITM2= (PLOT2 VALLEY1)) 

(p "they cultivate beans in the plot")
(ANSWER-ALGORITHM1=
 ((PLOT2 GROW-PLANTS (AGENT THEY) (THEME BEAN3) (AT-LOC PLOT2))))
 
 (p "The typical farm family has several widely scattered plots outside the
village")
(ALGORITM2= (PLOT2 VILLAGE2))

(p "she wrote the play in his plot near the lake")
(ALGORITM2= (PLOT2 LAKE1))
                      
crane:
 (p "cranes make their nests on brushes")

(ANSWER-ALGORITHM1=
 ((NEST1 MAKE-OR-CREATE-SOMETHING (AGENT CRANE2) (THEME NEST1)
  (ON-AT-LOC BRUSH1)))) 

Comment: Solved by a gloss stored on "nest1," which says "animals make
nests on natural objects and natural-locations.''

(p "major ports have special cranes that lift containers off ships")
(ANSWER-ALGORITHM1=
 ((PORT1 HAVE-SOMETHING-DEFAULT (THING-DESCRIBED PORT1)
      (ATTRIBUTE-DESCRIBED CRANE1)))) 

(ANSWER-ALGORITHM1=
 ((CRANE1 LIFT (INANIMATE-CAUSE CRANE1) (THEME CONTAINER1))))

(p "the crane hauls the weighty beams into place")
ANSWER-ALGORITHM1-B=
 ((CRANE1 NIL (INANIMATE-CAUSE CRANE1) (THEME BEAM6 BEAM2)))) 

pen:
 (p "A cross-bred boar was reared and fed indoors in a pen ")
(ANSWER-ALGORITHM1=
 ((PEN2 FEED-HUMAN-OR-ANIMAL (TO-POSS BOAR2 BOAR1) (AT-LOC PEN2)))) 

 (p "Dolphins continued to die in the pen in Shakang Harbour.")
(ANSWER-ALGORITHM1-B=
 ((PEN2 NIL (THEME DOLPHIN2 DOLPHIN1) (AT-LOC PEN2))))

 (p "the chicken flew away from the pen")
(ANSWER-ALGORITHM1= ((PEN2 ESCAPE (AGENT CHICKEN2) (FROM-LOC PEN2)))) 

 (p "as he was writing, the pen fell")
(ANSWER-ALGORITHM1= ((PEN1 WRITE-COMMUNICATE (AGENT HE)))) 

paddle:
 (p "The  paddle is drawn through the water ")
(ANSWER-ALGORITHM1=
 ((PADDLE4 DRAW-PULL (THEME PADDLE4) (THROUGHOUT-GOAL WATER1)))) 

mast:
(p " Most galleys had two masts")
(ANSWER-ALGORITHM1=
 ((MAST1 HAVE-SOMETHING-DEFAULT (THING-DESCRIBED GALLEY2 GALLEY1)
  (ATTRIBUTE-DESCRIBED MAST1))))

article:
(p "This article deals with domestic cats")

(ANSWER-ALGORITHM1=
 ((ARTICLE1 SPEAK-TALK (FORM-OR-MEDIUM-OF-EXPRESSION ARTICLE1))))

 (p "The two species of North American arborvitaes are described in the
                         article  on cedar")

(ANSWER-ALGORITHM1=
 ((ARTICLE1 SPEAK-TALK (FORM-OR-MEDIUM-OF-EXPRESSION ARTICLE1)  )
  (ARTICLE1 TRANS-INFOR
   (FORM-OR-MEDIUM-OF-EXPRESSION ARTICLE1)))) 

(p "he had written the   articles ")

(ANSWER-ALGORITHM1=
 ((ARTICLE1 APPEAR-BE-PUBLISHED-ISSUED (THEME ARTICLE1))
  (ARTICLE1 APPEAR-BE-PUBLISHED-ISSUED (THEME ARTICLE1))
  (ARTICLE1 PUBLICIZE-OR-PUBLISH (THEME ARTICLE1)))) 


blow:
 (p " Most bruises are caused by a sudden  blow ")
(ANSWER-ALGORITHM1=
 ((BLOW1 CAUSE-SOMETHING (INANIMATE-CAUSE BLOW1) (THEME BRUISE1)))) 

 (p "the explosive is acted upon by a blow")
(ANSWER-ALGORITHM1=
 ((BLOW1 CAUSE-CHANGE-OF-STATE (INANIMATE-CAUSE BLOW1)))) 

(p "the Portuguese empire suffered a major  blow ")
(ANSWER-ALGORITHM1= ((BLOW3 EXPERIENCE (EXPERIENCE BLOW3)))) 

dam:
 (p "a large  dam  was built on the Volga River ")
(ANSWER-ALGORITHM1=
 ((DAM1 CONSTRUCT-SOMETHING-PHYSICAL (THEME DAM1) (GOAL  VOLGA_RIVER1)))) 

bowl:
(p "The bowl contained punch")
(ANSWER-ALGORITHM1=
 ((BOWL1 INCLUDE (THING-DESCRIBED BOWL1) (ATTRIBUTE-DESCRIBED PUNCH2) )))

pocket:
 (p "she put the pistol in his pocket")
(ANSWER-ALGORITHM1=
 ((POCKET1 PUT (AGENT SHE) (THEME PISTOL1) (GOAL POCKET1))))

(p "The reticulum has tiny  pockets ")
 (ANSWER-ALGORITHM1=
   ((POCKET5 HAVE-SOMETHING-DEFAULT (THING-DESCRIBED RETICULUM1)
     (ATTRIBUTE-DESCRIBED POCKET5)))) 

 (p " The  vest had pockets ")
 ((POCKET1 HAVE-SOMETHING-DEFAULT (THING-DESCRIBED VEST2 VEST1)
  (ATTRIBUTE-DESCRIBED POCKET1))

 (p "she got a pencil from his pocket")
(ANSWER-ALGORITHM1-B=
 ((POCKET1 NIL (AGENT SHE) (THEME PENCIL1) (GOAL POCKET1))))

coat:
(p " The Malinois has a short-haired coat")
(ANSWER-ALGORITHM1-B=
 ((COAT3 NIL (THING-DESCRIBED MALINOIS1) (ATTRIBUTE-DESCRIBED COAT3)
 ))) 

 (p " these water birds have a thick coat ")
(ANSWER-ALGORITHM1-B=
 ((COAT3 NIL (THING-DESCRIBED WATER_BIRD1) (ATTRIBUTE-DESCRIBED COAT3)
 ))) 

 (p "Some coats are made of a heavy cloth ")
(ANSWER-ALGORITHM1=
 ((COAT1 MAKE-OR-CREATE-SOMETHING (THEME COAT1) (OF-STUFF CLOTH1)))) 

brig:
(p "he was confined into the brig")
(ANSWER-ALGORITHM1=
 ((BRIG2 CONFINE (THEME HE) (AT-LOC BRIG2) (THEME HE))))

Comment: brig2 inherits glosses from  penal_institution1

port:
(p "they make port in Portugal from grapes")
(ANSWER-ALGORITHM1=
 ((PORT2 MAKE-OR-CREATE-SOMETHING (THEME PORT2) (OF-STUFF GRAPE1)))) 

Comment: port2 is inheriting glosses from wine1.

(p "they brought the grapes to the port in a boat")
(ANSWER-ALGORITHM1= 
 ((PORT1 BRING-THINGS (THEME GRAPE2 GRAPE1) (GOAL PORT1)
   (INSTRUMENT BOAT1))))

 (p "the ship brought the whale back to the port ")
(ANSWER-ALGORITHM1=
 ((PORT1 BRING-THINGS (THEME WHALE2 WHALE1) (GOAL PORT1) NO))) 

bay:
(p "a sawmill was built near the  bay")
(ANSWER-ALGORITHM1=
 ((SAWMILL2 BUILD-CONSTRUCT-SOMETHING-PHYSICAL (THEME SAWMILL2))
 (BAY1 BUILD-CONSTRUCT-SOMETHING-PHYSICAL (THEME SAWMILL2)
  (AT-LOC BAY1))))

Comment: Solved by one gloss for plant1 (sawmill2 inherits from plant1)
and also by a gloss for bay1.

 (p " Important fisheries on the bay provide many jobs")
(ANSWER-ALGORITM2= (FISHERY1 BAY1)) 

 (p "These churches also featured a square central bay topped by a dome")
(ANSWER-ALGORITHM1=
 ((BAY6 HAVE-SOMETHING-DEFAULT (THING-DESCRIBED CHURCH2)
                    (ATTRIBUTE-DESCRIBED BAY6)))) 

cabin:
(p "he build a cabin in the forest near the shore")
(ANSWER-ALGORITHM1=
 ((BUILD-CONSTRUCT-SOMETHING-PHYSICAL (AGENT HE) (THEME CABIN2)
  (GOAL FOREST2))
 (CABIN2 BUILD-CONSTRUCT-SOMETHING-PHYSICAL (THEME CABIN2)
  (AT-LOC FOREST2)))) 

Comment: Solved by 2 glosses, one stored in the verb predicate
 BUILD-CONSTRUCT-SOMETHING-PHYSICAL and another one in cabin2.

shore:
 (p "they built a school in the shore")

(ANSWER-ALGORITHM1=
 ( BUILD-CONSTRUCT-SOMETHING-PHYSICAL (AGENT THEY) (THEME SCHOOL2)
  (GOAL SHORE1)))) 

 (p "we saw a little city on the shore")
(ANSWER-ALGORITHM2= (CITY1 SHORE1))

 (p "the city is on the western shore")
 (ANSWER-ALGORITHM1=(SHORE1 LIE-SOMEWHERE (THING-DESCRIBED  CITY1)
    (AT-LOC SHORE1) ))

cabinet:
 (p "Some collectors put their coins in cabinets")
(ANSWER-ALGORITHM1=
 ((CABINET3 PUT (AGENT COLLECTOR2 COLLECTOR1) (THEME COIN1)
  (GOAL CABINET3))
 (CABINET1 PUT (AGENT COLLECTOR2 COLLECTOR1) (THEME COIN1)
  (GOAL CABINET1)))) 

(p "The prime minister names a cabinet")
(ANSWER-ALGORITHM1=
 ((CABINET2 APPOINT-SOMEBODY (RECIPIENT CABINET2)
  (AGENT PRIME_MINISTER2 PRIME_MINISTER1)))) 

(p "she put some plants in the cabinet")
(ANSWER-ALGORITHM1=
 ((CABINET3 PUT (AGENT SHE) (THEME PLANT2) (GOAL CABINET3))
  (CABINET1 PUT (AGENT SHE) (THEME PLANT2) (GOAL CABINET1))))