Examples
Here are some examples of linguistic analyses using MeaningCloud:
Syntactic analysis of a sentence
To show how to carry out a syntactic analysis extracting topics we will use the following text as an example:
"Robert Downey Jr has topped Forbes magazine's annual list of the highest paid actors for the second year in a row."
- Use the
txtparameter to submit the text. - Choose with
langthe language in which the text is going to be analyzed, in this case English, en. - Include your MeaningCloud license key as value for
keyparameter. - Choose an output format, for instance json as value for
of. - Change the default value of the unknown words parameter,
uwso the engine tries to find possible analysis when there are typos in the text. - Send
ttwith the value e to obtain the entities detected in the text in the corresponding token.
Example using curl
curl -XPOST "https://api.meaningcloud.com/parser-2.0?key=<<YOUR OWN KEY>>&of=json&lang=en&txt=Robert%20Downey%20Jr%20has%20topped%20Forbes%20magazine%27s%20annual%20list%20of%20the%20highest%20paid%20actors%20for%20the%20second%20year%20in%20a%20row.&uw=y&tt=e"
MeaningCloud API output
{
"status": {
"code": "0",
"msg": "OK",
"credits": "1"
},
"token_list": [
{
"type": "sentence",
"id": "29",
"inip": "0",
"endp": "113",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "A",
"quote_level": "0",
"affected_by_negation": "no",
"token_list": [
{
"type": "phrase",
"form": "Robert Downey Jr has topped Forbes magazine's annual list of the highest paid actors for the second year in a row",
"id": "44",
"inip": "0",
"endp": "112",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "_",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "Z-----------",
"lemma": "*",
"original_form": "Robert Downey Jr has topped Forbes magazine's annual list of the highest paid actors for the second year in a row"
}
],
"token_list": [
{
"type": "phrase",
"form": "Robert Downey Jr",
"id": "34",
"inip": "0",
"endp": "15",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "_",
"quote_level": "0",
"affected_by_negation": "no",
"head": "31",
"syntactic_tree_relation_list": [
{
"id": "28",
"type": "isSubject"
}
],
"analysis_list": [
{
"tag": "GNMS3S--",
"lemma": "Robert Downey Jr",
"original_form": "Robert Downey Jr"
},
{
"tag": "GNFS3S--",
"lemma": "Robert Downey Jr",
"original_form": "Robert Downey Jr"
}
],
"token_list": [
{
"type": "multiword",
"form": "Robert Downey Jr",
"id": "31",
"inip": "0",
"endp": "15",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "_",
"quote_level": "0",
"affected_by_negation": "no",
"head": "27",
"analysis_list": [
{
"tag": "NPMS-N-",
"lemma": "Robert Downey Jr",
"original_form": "Robert Downey Jr",
"sense_id_list": [
{
"sense_id": "__12123288058840445720"
}
]
},
{
"tag": "NPMP-N-",
"lemma": "Robert Downey Jr",
"original_form": "Robert Downey Jr",
"sense_id_list": [
{
"sense_id": "__12123288058840445720"
}
]
},
{
"tag": "NPFS-N-",
"lemma": "Robert Downey Jr",
"original_form": "Robert Downey Jr",
"sense_id_list": [
{
"sense_id": "__12123288058840445720"
}
]
},
{
"tag": "NPFP-N-",
"lemma": "Robert Downey Jr",
"original_form": "Robert Downey Jr",
"sense_id_list": [
{
"sense_id": "__12123288058840445720"
}
]
}
],
"sense_list": [
{
"id": "__12123288058840445720",
"form": "Robert Downey Jr",
"info": "sementity/class=instance@type=Top@confidence=unknown"
}
],
"topic_list": {
"entity_list": [
{
"form": "Robert Downey Jr",
"id": "__12123288058840445720",
"sementity": {
"class": "instance",
"type": "Top",
"confidence": "unknown"
}
}
]
}
}
]
},
{
"type": "multiword",
"form": "has topped",
"id": "28",
"inip": "17",
"endp": "26",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "4",
"syntactic_tree_relation_list": [
{
"id": "34",
"type": "iof_isSubject"
},
{
"id": "43",
"type": "iof_isDirectObject"
},
{
"id": "40",
"type": "iof_isComplement"
}
],
"analysis_list": [
{
"tag": "VI-S3PPA-N-N9",
"lemma": "top",
"original_form": "has topped"
}
]
},
{
"type": "phrase",
"form": "Forbes magazine's annual list of the highest paid actors for the second year",
"id": "43",
"inip": "28",
"endp": "103",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "36",
"syntactic_tree_relation_list": [
{
"id": "28",
"type": "isDirectObject"
}
],
"analysis_list": [
{
"tag": "GN-S3D--",
"lemma": "list",
"original_form": "Forbes magazine's annual list of the highest paid actors for the second year"
}
],
"token_list": [
{
"type": "phrase",
"form": "Forbes magazine",
"id": "35",
"inip": "28",
"endp": "42",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "7",
"analysis_list": [
{
"tag": "GN-S3---",
"lemma": "magazine",
"original_form": "Forbes magazine"
}
],
"token_list": [
{
"form": "Forbes",
"id": "6",
"inip": "28",
"endp": "33",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "NP-S-N-",
"lemma": "Forbes",
"original_form": "Forbes",
"sense_id_list": [
{
"sense_id": "db0f9829ff"
}
]
},
{
"tag": "NP-S-N-",
"lemma": "Forbes",
"original_form": "Forbes",
"sense_id_list": [
{
"sense_id": "9752b8b5ee"
}
]
},
{
"tag": "NPMS-N-",
"lemma": "Forbes",
"original_form": "Forbes",
"sense_id_list": [
{
"sense_id": "4a3369b337"
}
]
},
{
"tag": "NPFS-N-",
"lemma": "Forbes",
"original_form": "Forbes",
"sense_id_list": [
{
"sense_id": "4a3369b337"
}
]
}
],
"sense_list": [
{
"id": "4a3369b337",
"form": "Forbes",
"info": "sementity/class=instance@fiction=nonfiction@id=ODENTITY_LAST_NAME@type=Top>Person>LastName\tsemld_list=sumo:LastName"
},
{
"id": "9752b8b5ee",
"form": "Forbes",
"info": "sementity/class=instance@fiction=nonfiction@id=ODENTITY_RIVER@type=Top>Location>GeographicalEntity>WaterForm>River\tsemld_list=sumo:River"
},
{
"id": "db0f9829ff",
"form": "Forbes",
"info": "sementity/class=instance@fiction=nonfiction@id=ODENTITY_MAGAZINE@type=Top>Product>CulturalProduct>Printing>Magazine\tsemgeo_list/continent=America#id:33fc13e6dd@country=United States#id:beac1b545b#ISO3166-1-a2:US#ISO3166-1-a3:USA\tsemld_list=sumo:Magazine\tsemtheme_list/id=ODTHEME_ECONOMY@type=Top>SocialSciences>Economy"
}
],
"topic_list": {
"entity_list": [
{
"form": "Forbes",
"id": "4a3369b337",
"sementity": {
"class": "instance",
"fiction": "nonfiction",
"id": "ODENTITY_LAST_NAME",
"type": "Top>Person>LastName"
},
"semld_list": [
"sumo:LastName"
]
},
{
"form": "Forbes",
"id": "9752b8b5ee",
"sementity": {
"class": "instance",
"fiction": "nonfiction",
"id": "ODENTITY_RIVER",
"type": "Top>Location>GeographicalEntity>WaterForm>River"
},
"semld_list": [
"sumo:River"
]
},
{
"form": "Forbes",
"id": "db0f9829ff",
"sementity": {
"class": "instance",
"fiction": "nonfiction",
"id": "ODENTITY_MAGAZINE",
"type": "Top>Product>CulturalProduct>Printing>Magazine"
},
"semgeo_list": [
{
"continent": {
"form": "America",
"id": "33fc13e6dd"
},
"country": {
"form": "United States",
"id": "beac1b545b",
"standard_list": [
{
"id": "ISO3166-1-a2",
"value": "US"
},
{
"id": "ISO3166-1-a3",
"value": "USA"
}
]
}
}
],
"semld_list": [
"sumo:Magazine"
],
"semtheme_list": [
{
"id": "ODTHEME_ECONOMY",
"type": "Top>SocialSciences>Economy"
}
]
}
]
}
},
{
"form": "magazine",
"id": "7",
"inip": "35",
"endp": "42",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "NC-S-N5",
"lemma": "magazine",
"original_form": "magazine",
"sense_id_list": [
{
"sense_id": "a0a1a5401f"
}
]
}
],
"sense_list": [
{
"id": "a0a1a5401f",
"form": "magazine",
"info": "sementity/class=class@fiction=nonfiction@id=ODENTITY_MAGAZINE@type=Top>Product>CulturalProduct>Printing>Magazine\tsemld_list=sumo:Magazine"
}
]
}
]
},
{
"form": "'s",
"id": "26",
"inip": "43",
"endp": "44",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "A",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "WN-",
"lemma": "'s",
"original_form": "'s"
}
]
},
{
"type": "phrase",
"form": "annual list of the highest paid actors for the second year",
"id": "36",
"inip": "46",
"endp": "103",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "11",
"analysis_list": [
{
"tag": "GN-S3---",
"lemma": "list",
"original_form": "annual list"
}
],
"token_list": [
{
"form": "annual",
"id": "10",
"inip": "46",
"endp": "51",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "AP-N5",
"lemma": "annual",
"original_form": "annual"
}
]
},
{
"form": "list",
"id": "11",
"inip": "53",
"endp": "56",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "NC-S-N5",
"lemma": "list",
"original_form": "list"
}
]
},
{
"type": "phrase",
"form": "of the highest paid actors for the second year",
"id": "42",
"inip": "58",
"endp": "103",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "12",
"analysis_list": [
{
"tag": "GY------",
"lemma": "of",
"original_form": "of the highest paid actors for the second year"
}
],
"token_list": [
{
"form": "of",
"id": "12",
"inip": "58",
"endp": "59",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "YN9",
"lemma": "of",
"original_form": "of"
}
]
},
{
"type": "phrase",
"form": "the highest paid actors for the second year",
"id": "37",
"inip": "61",
"endp": "103",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "16",
"analysis_list": [
{
"tag": "GN-P3---",
"lemma": "actor",
"original_form": "the highest paid actors"
}
],
"token_list": [
{
"form": "the",
"id": "13",
"inip": "61",
"endp": "63",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "TD-PN9",
"lemma": "the",
"original_form": "the"
}
]
},
{
"form": "highest",
"id": "14",
"inip": "65",
"endp": "71",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "AS-N5",
"lemma": "high",
"original_form": "highest"
}
]
},
{
"form": "paid",
"id": "15",
"inip": "73",
"endp": "76",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "VP---ASA-N-N5",
"lemma": "pay",
"original_form": "paid",
"sense_id_list": [
{
"sense_id": "ODENTITY_TRANSACTION"
},
{
"sense_id": "ODENTITY_COMMUNICATION_PROCESS"
},
{
"sense_id": "ODENTITY_CHANGE_OF_POSSESSION"
},
{
"sense_id": "ODENTITY_INTENTIONAL_PROCESS"
},
{
"sense_id": "ODENTITY_INTENTIONAL_PSYCHOLOGICAL_PROCESS"
},
{
"sense_id": "ODENTITY_MEETING"
}
]
}
],
"sense_list": [
{
"id": "ODENTITY_CHANGE_OF_POSSESSION",
"form": "pay",
"info": "sementity/id=ODENTITY_CHANGE_OF_POSSESSION@type=Top>Process>IntentionalProcess>SocialInteraction>ChangeOfPossession\tsemld_list=sumo:IntentionalProcess"
},
{
"id": "ODENTITY_COMMUNICATION_PROCESS",
"form": "pay",
"info": "sementity/id=ODENTITY_COMMUNICATION_PROCESS@type=Top>Process>ContentBearingProcess>CommunicationProcess\tsemld_list=sumo:Entity"
},
{
"id": "ODENTITY_INTENTIONAL_PROCESS",
"form": "pay",
"info": "sementity/id=ODENTITY_INTENTIONAL_PROCESS@type=Top>Process>IntentionalProcess\tsemld_list=sumo:IntentionalProcess"
},
{
"id": "ODENTITY_INTENTIONAL_PSYCHOLOGICAL_PROCESS",
"form": "pay",
"info": "sementity/id=ODENTITY_INTENTIONAL_PSYCHOLOGICAL_PROCESS@type=Top>Process>IntentionalProcess>IntentionalPsychologicalProcess\tsemld_list=sumo:IntentionalProcess"
},
{
"id": "ODENTITY_MEETING",
"form": "pay",
"info": "sementity/id=ODENTITY_MEETING@type=Top>Process>IntentionalProcess>SocialInteraction>Meeting\tsemld_list=sumo:IntentionalProcess"
},
{
"id": "ODENTITY_TRANSACTION",
"form": "pay",
"info": "sementity/id=ODENTITY_TRANSACTION@type=Top>Process>DualObjectProcess>Transaction\tsemld_list=sumo:Entity"
}
]
},
{
"form": "actors",
"id": "16",
"inip": "78",
"endp": "83",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "NC-P-N5",
"lemma": "actor",
"original_form": "actors",
"sense_id_list": [
{
"sense_id": "4014a7dc12"
}
]
}
],
"sense_list": [
{
"id": "4014a7dc12",
"form": "actor",
"info": "sementity/class=class@fiction=nonfiction@id=ODENTITY_VOCATION@type=Top>OtherEntity>Vocation\tsemld_list=sumo:Position\tsemtheme_list/id=ODTHEME_ARTS@type=Top>Arts"
}
]
},
{
"type": "phrase",
"form": "for the second year",
"id": "41",
"inip": "85",
"endp": "103",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "17",
"analysis_list": [
{
"tag": "GY------",
"lemma": "for",
"original_form": "for the second year"
}
],
"token_list": [
{
"form": "for",
"id": "17",
"inip": "85",
"endp": "87",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "YN5",
"lemma": "for",
"original_form": "for"
}
]
},
{
"type": "phrase",
"form": "the second year",
"id": "38",
"inip": "89",
"endp": "103",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "20",
"analysis_list": [
{
"tag": "GN-S3T--",
"lemma": "year",
"original_form": "the second year"
}
],
"token_list": [
{
"form": "the",
"id": "18",
"inip": "89",
"endp": "91",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "TD-SN9",
"lemma": "the",
"original_form": "the"
}
]
},
{
"form": "second",
"id": "19",
"inip": "93",
"endp": "98",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "NC-S-N5",
"lemma": "second",
"original_form": "second",
"sense_id_list": [
{
"sense_id": "9a8f54cd04"
}
]
}
],
"sense_list": [
{
"id": "9a8f54cd04",
"form": "second",
"info": "sementity/class=class@fiction=nonfiction@id=ODENTITY_PERIOD@type=Top>Timex>Period\tsemld_list=sumo:TimeInterval"
}
]
},
{
"form": "year",
"id": "20",
"inip": "100",
"endp": "103",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "NC-S-N7",
"lemma": "year",
"original_form": "year",
"sense_id_list": [
{
"sense_id": "782b286b79"
},
{
"sense_id": "50b37cb80f"
}
]
}
],
"sense_list": [
{
"id": "50b37cb80f",
"form": "year",
"info": "sementity/class=class@fiction=nonfiction@id=ODENTITY_DATE@type=Top>Timex>Date\tsemld_list=sumo:DatePeriod"
},
{
"id": "782b286b79",
"form": "year",
"info": "sementity/class=class@fiction=nonfiction@id=ODENTITY_PERIOD@type=Top>Timex>Period\tsemld_list=sumo:TimeInterval"
}
]
}
]
}
]
}
]
}
]
}
]
}
]
},
{
"type": "phrase",
"form": "in a row",
"id": "40",
"inip": "105",
"endp": "112",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "21",
"syntactic_tree_relation_list": [
{
"id": "28",
"type": "isComplement"
}
],
"analysis_list": [
{
"tag": "GY---C--",
"lemma": "in",
"original_form": "in a row"
}
],
"token_list": [
{
"form": "in",
"id": "21",
"inip": "105",
"endp": "106",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "YN6",
"lemma": "in",
"original_form": "in"
}
]
},
{
"type": "phrase",
"form": "a row",
"id": "39",
"inip": "108",
"endp": "112",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"head": "23",
"analysis_list": [
{
"tag": "GN-S3---",
"lemma": "row",
"original_form": "a row"
}
],
"token_list": [
{
"form": "a",
"id": "22",
"inip": "108",
"endp": "108",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "QD-SPN9",
"lemma": "a",
"original_form": "a"
}
]
},
{
"form": "row",
"id": "23",
"inip": "110",
"endp": "112",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "1",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "NC-S-N2",
"lemma": "row",
"original_form": "row",
"sense_id_list": [
{
"sense_id": "c5fd6e4956"
}
]
}
],
"sense_list": [
{
"id": "c5fd6e4956",
"form": "fight",
"info": "sementity/class=class@fiction=nonfiction@id=ODENTITY_EVENT@type=Top>Event\tsemld_list=sumo:Meeting"
}
]
}
]
}
]
}
]
},
{
"form": ".",
"id": "24",
"inip": "113",
"endp": "113",
"style": {
"isBold": "no",
"isItalics": "no",
"isUnderlined": "no",
"isTitle": "no"
},
"separation": "A",
"quote_level": "0",
"affected_by_negation": "no",
"analysis_list": [
{
"tag": "1D--",
"lemma": ".",
"original_form": "."
}
]
}
]
}
]
}
Morphological analysis of a sentence
To show how to work with the output to obtain the morphological analysis of a sentence we will use the following text as an example:
"Robert Downey Jr has topped Forbes magazine's annual list of the highest paid actors for the second year in a row."
We will:
- Use the
txtparameter to submit the text. - Choose with
langthe language in which the text is going to be analyzed, in this case English, en. - Include your MeaningCloud license key as value for
keyparameter. - Choose an output format, for instance json as value for
of. - Activate the
verboseparameter to obtain the morphological tag explained.
The following code contains a request to the API using this parameters, and processes the output to obtain an array (morpho) with the tokens with the morphological analysis. After that we will print them
The code would be called like this:
php parser-2.0-gist-morpho.php
And this will be the output for the example sentence:
Tokens:
==============
Robert Downey Jr
NPMS-N-: noun, proper, masculine, singular
NPMP-N-: noun, proper, masculine, plural
NPFS-N-: noun, proper, feminine, singular
NPFP-N-: noun, proper, feminine, plural
has topped
VI-S3PPA-N-N9: verb, indicative, singular, 3rd person, present, perfect, active, maximum frequency word
Forbes
NP-S-N-: noun, proper, singular
NP-S-N-: noun, proper, singular
NPMS-N-: noun, proper, masculine, singular
NPFS-N-: noun, proper, feminine, singular
magazine
NC-S-N5: noun, common, singular, medium frequency word
's
WN-: saxon genitive
annual
AP-N5: adjective, positive, medium frequency word
list
NC-S-N5: noun, common, singular, medium frequency word
of
YN9: preposition, maximum frequency word
the
TD-PN9: article, determiner, plural, maximum frequency word
highest
AS-N5: adjective, superlative, medium frequency word
paid
VP---ASA-N-N5: verb, participle, past, simple, active, medium frequency word
actors
NC-P-N5: noun, common, plural, medium frequency word
for
YN5: preposition, medium frequency word
the
TD-SN9: article, determiner, singular, maximum frequency word
second
NC-S-N5: noun, common, singular, medium frequency word
year
NC-S-N7: noun, common, singular, high frequency word
in
YN6: preposition, medium-high frequency word
a
QD-SPN9: quantifier, determiner, singular, positive, maximum frequency word
row
NC-S-N2: noun, common, singular, very low frequency word
.
1D: punctuation, other
Lemmatization of a sentence
To show how to work with the output to lemmatize a sentence we will use the following text as an example:
"Robert Downey Jr has topped Forbes magazine's annual list of the highest paid actors for the second year in a row."
We will:
- Use the
txtparameter to submit the text. - Choose with
langthe language in which the text is going to be analyzed, in this case English, en. - Include your MeaningCloud license key as value for
keyparameter. - Choose an output format, for instance json as value for
of.
The following code contains a request to the API using this parameters, and processes the output to obtain an array (morpho) with the tokens with the morphological analysis. After that we will print them
The code would be called like this:
php parser-2.0-gist-lemma.php
And this will be the output for the example sentence, where for each token the form followed by all the possible lemmas will be printed, and the different levels in the tree will be marked with curly brackets:
Tokens:
=============
{{{{Robert Downey Jr|Robert Downey Jr|Robert Downey Jr|Robert Downey Jr|Robert Downey Jr}}{has topped|top}{{{Forbes|Forbes|Forbes|Forbes|Forbes}{magazine|magazine}}{'s|'s}{{annual|annual}{list|list}{{of|of}{{the|the}{highest|high}{paid|pay}{actors|actor}{{for|for}{{the|the}{second|second}{year|year}}}}}}}{{{in|in}{{a|a}{row|row}}}}{.|.}}
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