Topics Extraction Response [2.0]

Response

The output contains information about the status of the request and about all the topics that have been configured to be extracted in it. In other words, if a topic type has been requested, the corresponding element will appear in the output, independently of whether any have been found or not.

The information provided is the same for the different output formats and the naming convention used for all fields is lowercase_separated_by_underscore.

The following table shows the fields included in the response object.

Response output

Name	Description
`status`	Contains information about the extraction process and whether it has finished successfully. It is formed by four different values: `code`: a natural number that identifies the status returned. `msg`: a string describing the status. `credits`: contains a natural number that indicates the credits consumed in the request. `remaining_credits`: shows the number of credits left to the user to reach the credit limit. Out of these four fields, `code` and `msg` will always appear in the response. You can find all the possible status codes returned by the API with an explanation and tips on how to manage them in our error codes catalog. A request is any HTTP request done to the API to analyze less than 500 words. If the text sent is longer than that, then it will be considered that more than a request is made, more specifically, as many requests as we would need if the text were divided in chunks of 500 words. For instance, an HTTP request with 1013 words, will count as three requests, so 3 `credits` will be consumed; an HTTP request with a text 25648 words long would count as 25648/500 = 51.296 => 52 `credits`, and so on. Did you know...? Only the successful requests to the API will consume credits. In other words, the `credits` specified in the answer will only count as credits consumed when `code=0`.
`entity_list`	This element is a list of named entities found in the input text, each one represented by an entity object.
`concept_list`	This element is a list of concepts found in the input text, each one represented by a concept object.
`time_expression_list`	Lists of the time expressions found in the input text and represented as time_expression objects.
`money_expression_list`	Lists of money expressions found in the text and represented as money expression objects.
`quantity_expression_list` [beta]	List of quantities found in the text, represented by quantity expression objects.
`other_expression_list`	List of unknown alphanumeric patterns found in the text, represented by the other expression object.
`quotation_list`	List of quotations found in a text and represented by the quotation object.
`relation_list`	List of syntactic triples (subject-action-object) found in the text, represented by relation objects.

Entity/concept object

Both entities and concepts have the same basic structure even if some of the specific values found in each field are different. In the following explanation element will refer to both entity and concept objects.

Each element found will be a node in our ontology. There are two types of information associated to each element:

Basic element information, that is, the information specific to the element found. It tells which element it is (form), how many times and in which form it appears in the text (variant_list), its global relevance, if it belongs to a specific dictionary, and in the cases where it is a known element, its unique identifier, id in the ontology and known standards (standard_list).
Semantic information, or the different nodes to which the element node is related to. There are different aspects of semantic information: type of entity (sementity), geographical and thematic information (semgeo_list and semtheme_list) and other, more generic types (semrefer_list).

sementity will be the only semantic aspect of the element that will be mandatory, as it will be associated to the sense of the element found and each sense translates into an entity/concept object in the output. In terms of the ontology, sementity contains information from the node in the ODENTITY_TOP branch to which the element node found is related to. For example, London has two senses, last name and city, so in a scenario with no disambiguation, this will mean two entities will be found, each one with a different sementity object, one with the id ODENTITY_LAST_NAME and the other with the id ODENTITY_CITY.

The sementity element contains a field called type with the expanded hierarchy of the entity type that provides a much more intuitive grasp of the sense associated to the element. Each level of the hierarchy will follow a notation a bit more user-friendly than the node names seen until now: the entity type id will lose the prefix ODENTITY_, the underscores will be deleted and the capitalization will follow the upper CamelCase style. Using the previous examples:

ODENTITY_CITY -- City
ODENTITY_LAST_NAME -- LastName

sementity will also include an attribute called class, which will indicate if the element in question is an instance of the entity type, or if it is a class. In the case of an entity, this value will always be an instance, as a named entity is always an example of the class the node sementity represents. Elements with class=class will appear as concept objects.

semtheme_list is conformed by semtheme objects. semtheme is quite similar to sementity, instead of refering to the entity type (a node in the ODENTITY_TOP branch of the ontology), it points to the theme or themes the node belongs to (a node in the ODTHEME_TOP branch of the ontology). semtheme also contains a type field with the expanded version of the hierarchy; it follows the same pattern mentioned in sementity before but with ODTHEME_ as prefix:

ODTHEME_BASIC_SCIENCES -- BasicSciences
ODTHEME_MYTHOLOGY -- Mythology

There will be as many semtheme elements as themes the node relates to.

Both sementity and semtheme are characterized by always refering to class nodes. The rest of the semantic information associated to the entity will refer to instance nodes. The main difference this will show in the output is that classes will be identified by their name (e.g. ODENTITY_CITY) while instances will be referred to by a unique alphanumeric string that univocally identifies the node in the ontology (id).

Similarly to sementity and semtheme, semgeo (each element contained in semgeo_list) provides information on the node's hierarchy, although in this case the hierarchy corresponds to a geopolitical criteria. Instead of including the values in a single field and taking into account that some cases may be multiple inheritance (for instance, a mountain chain that belongs to two different countries), there will be specific object for each level which will be identified by its form and its node id.

semrefer_list will contain other references between the entity/concept node and other instances in the ontology. There are currently two types: organization, which links an instance of the ODENTITY_ORGANIZATION type (or its descendants), and affinity which shows an affinity relationship between the entity node and another instance in the ontology. Each object in semrefer will be represented but its form and its node id.

The last field included in an entity/concept, semld, is a mix of the two types of information described: it contains information specific to the node but said information are links to external ontologies such as SUMO, Wikipedia or YAGO.

The following table contains the fields that will appear in an entity and concept objects.

Entity/Concept object

Name Description

form

Form of the entity/concept (representative of the sense associated to it) in the language specified in the ilang parameter.

official_form

Official form of the entity/concept, that is, its official name in cases when it's different from the form. For instance, "United States" vs "United States of America". Same as form, it will be in the language specified in the ilang parameter.

dictionary

Source dictionary of the element. If the entity/concept comes from a user dictionary, the name of said dictionary will appear in this field, otherwise it will be empty.

id

Alphanumeric string that univocally identifies the entity/concept. This ID will correspond to the entity/concept senseID in resources (which includes user dictionaries). If the entity/concept is not in any of the resources but has been detected in the analysis, the ID will be specifically created for that analysis and will begin by two underscores.

sementity

sementity: Type of entity/concept

class: indicates if the node is a class or an instance in the ontology.
fiction: indicates if the element is fictional(fiction) or non-fictional (nonfiction).
id: identifier of the node associated to the entity type.
type: provides a more user-friendly notation for the type classification hierarchy of the entity. It will start with the highest node (Top) and each level will separated by '>'. Top will always appear.
confidence: it will use the values unknown and uncertain to denote entity types infered from heuristic rules and ambiguous classifications, respectively.

semgeo_list

Geographical information the entity is associated to.

Each level of the hierarchy will be represented by its form, form and its id node. Some of the fields may contain additional information in the form of the standard_list element and form will be in the language specified in the ilang parameter.

The possible levels in the hierarchy are the following: continent, country, adm1, adm2, adm3, city and district.

semld_list

Provides a list of gateways to different open data sources. These gateways will be provided in two different formats: though a link or by providing an identifier to access the information.

The following table includes the gateways associated to an identifier, and how to use it:

Source	Format	How to use it
SUMO	sumo:xxxxx	http://sigma-01.cim3.net:8080/sigma/Browse.jsp?kb=SUMO&term=xxxxx
Twitter	@xxxxx	http://twitter.com/xxxxx

semrefer_list

Includes references to other nodes in the ontology (instance type nodes). There are two possible types:

organization: organizational relationships with the node specified (through form and id). An example of this type of relationship would be a company and its subsidiary.
affinity: affinity relationships with the node specified (through form and id). An example of this type of relationship would be a politician and the political party he/she belongs to.

semtheme_list

List of thematic classifications. It has two fields:

id: identifier of the node associated to the theme the entity belongs to.
type: provides a more user-friendly name of all the levels of the theme classification hierarchy. It will start with the highest node (Top) and each level will separated by '>'.

standard_list

Includes international standards relevant to the sense associated to the element. Each element will have two fields id, with the identifier of the standard and value, with the specific value of said standart for the entity/concept. For example, the ISO3166-1 standard for countries will be identified as ISO3166-1-a2 when it refers to the two letters that identify each country and as ISO3166-1-a3 for the three-letter id. NYSE will be the value used to identify the ticker of a company that trades in the NY stock exchange.

These are all the values that may appear in id:

ID	Description
ISO3166-1-a2, ISO3166-1-a3	Country codes
BEL20, BMAD, BUENOSAIRES, BVL, CAC_40, CARACAS, CORROELECTRONICO, DAX_30, EURO_STOXX50, Euronext, FTSE_100, FTSE_LATIBEX, IBEX35, LSE, LuxSE, MAB, MEXICO, MIB, NASDAQ, NYSE, OMXH25, OMXS30, SANTIAGO, SMI, SP100	Stock exchanges
ISO4217	Currency codes
ISO639-1, ISO639-2, ISO639-3, ISO639-5	Languages codes
ISO8601	Dates standard

variant_list

Variants of the element, or in other words, the different appearances of the entity/concept in the text.

variant: each variant is an appearance in the text, so there must be at least one.
- form: how the entity/concept appears in the text
- inip: initial position of the appearance
- endp: end position of the appearance

relevance

Relative relevance of the entity in the text compared to the other entities found.

subentity_list

This element is composed of subentity elements that have exactly the same structure as entity. It applies only to entity objects.

Time expression object

For time expressions that refer to a specific date, the following format will be used to represent its associated value:

These are the values each field may have:

century, year, month, day, hour, minutes, seconds: numeric values
era: after Christ (aC), before Christ (dC)
season: spring (s), summer (v), autumn (a), winter (w)
weekday: Monday (m), Tuesday (t), Wednesday (w), Thursday (j), Friday (f), Saturday (s), Sunday (d)
timezone: must be specified either by using the standard timezones designations (CET, EST, etc.) or with the offset with respect to GMT, e.g.: GMT+02:00
+/- indicate references after/before the returned value (e.g. +2 days)
~ indicates approximate values

If an expression has no value for one of the positions, it will be empty.

These would be some examples of how this would look:

It's 7:30 in the evening -- |||||||19|30||
27th February at 3pm -- |||||2|27|15|||
5th june 2008 -- 21||||2008|6|5||||

This representation of the time will be used to calculate the value in actual_time, which will use as reference timeref and will return a date value in one of the following three formats: YYYY-MM-DD hh:mm:ss GMT±HH:MM, YYYY-MM-DD and hh:mm:ss GMT±HH:MM. For the examples seen and using as reference 2013-01-01 12:12:12 GMT+01:00, the result would be:

It's 7:30 in the evening -- 19:30:00 GMT+01:00
27th February at 3pm -- 2013-01-27 15:00:00 GMT+01:00
5th june 2008 -- 2008-06-05

In some cases, actual_time returns values that are not certain (for example, minutes and seconds in the second example), so a precision value is added to filter these out. The values for precision are the positions of the normalized_form field plus hourAMPM, minutesAMPM and secondsAMPM. This will result in obtaining different objects for it's 7:30 and it's 7:30 in the evening.

Each time_expression may contain the following fields:

Time expression object

Name	Description
`form`	Form of the time expression.
`normalized_form`	Normalized form associated to the time expression.
`actual_time`	Actual time relative to the given time reference, based on the normalized form.
`precision`	Level of precision for `actual_time`.
`inip`	Initial position of the time expression.
`endp`	End position of the time expression.

Money expression object

Lists of money expressions found in the text and represented as money_exppression objects.

It will be considered that there is a money expression when there is both a currency and an amount in a valid structure. The currency will be expressed using the ISO4217 and in the cases where more than one currency may apply, all the possible values will be returned separated by '|' and ordered alphabetically.

The fields in the money_expression object are:

Money expression object

Name	Description
`form`	Form of money expression.
`amount_form`	Amount associated to the money expression as it appears in the text.
`numeric_value`	Equivalent numeric value of the amount of money.
`currency`	ISO4217 value associated to the currency in the money expression. Different values are separated by the character '\|'.
`inip`	Initial position of the money expression.
`endp`	End position of the money expression.

Quantity expression object

List of quantity expressions found in the text, represented by quantity objects.

The fields in the quantity_expression object are:

Quantity object

Name	Description
`form`	Form of quantity expression.
`amount_form`	Amount associated to the quantity expression as it appears in the text.
`numeric_value`	Equivalent numeric value of the amount detected.
`unit`	Standarized value of the unit detected for the expression.
`inip`	Initial position of the quantity expression.
`endp`	End position of the quantity expression.

Other expression object

Some specific patterns will be considered known ones, and identified as such through the field type. The patterns detected are the following:

Spanish:

bank account number: 20 digits with the format xxxx xxxx xx xxxxxxxxxx
license plate: Spanish license plate with two formats: dddd-LLL and ddddLLL (where d are digits and L are capital letters)
id: national id document: ddddddddL or dddddddd-L (with d digits, L a capital letter)

All languages:

flight number: detects flight numbers with the format LLdddd (where d are digits and L are capital letters)

The fields in the other_expression object are:

Other expression object

Name	Description
`form`	Form of expression.
`type`	Type of expression (default: unknown)
`inip`	Initial position of the expression.
`endp`	End position of the expression.

Quotations object

The fields in the quotation object are:

Quotation object

Name	Description
`form`	Content of the quote as it appears in the text.
`who`	Who the quote is attributed to. It will have two fields, the `form`, and the `lemma`.
`verb`	Verb associated to the quotation. It will have two fields, the `form`, and the `lemma`.
`inip`	Initial position of the expression.
`endp`	End position of the expression.

Quotations in direct speech will not always include information regarding who they are attributed to; in those cases the fields who and verb will not appear.

Relations object

The syntactic triples will be defined by subject-verb pairs, and all the complements associated to that verb. There are two possible exceptions to this:

Cases where the existing relation has an omitted verb (for example, appositions). In this case, the verb is assumed to be "to be" (or its equivalent, depending on the language), and its form will appear between parentheses.
The subject is omitted (very common in some languages such as Spanish), in which the subject will not appear.

The fields in the relation object are:

Relation object

Name	Description
`form`	Sentence in which the relation appears.
`inip`	Initial position of the sentence the relation appears in.
`endp`	End position of the sentence the relation appears in.
`subject`	Subject of the relation. In the cases where the `subject` is an anaphora, the anaphora will be solved and the details that will appear will be those of the element that solves it. `form`: how it appears in the text. `lemma_list`: list of lemma/s of the element. Coordinated elements by definition don't have a lemma, so the field will not appear. `sense_id_list`: `id`/s associated to the `concept` or `entity` the subject refers to.
`verb`	Verb of the relation `form`: how it appears in the text. `lemma_list`: list of lemmas of the verb. `sense_id_list`: `id` associated to the verb. `semantic_lemma_list`: list of semantic lemmas associated to the verb. It will only be included when its values are different than the ones in `lemma_list`.
`complement_list`	list of complements of the `verb` represented by the `complements` objects. The fields included in the `complement` element are: `form`: how it appears in the text. Anaphoras will be solved to obtain this value. `type`: type of complement. The different types of syntactic relations detected are included in the response of the Lemmatization, PoS and Parsing, specifically in the section regarding `syntactic_tree_relation` elements.
`degree`	Degree of proximity of the relation, that is if the relation included is in the same sentence as the `subject` (in the cases where an anaphora has been solved, it won't be).

If a subject-verb pair appears several times in the same text, they will only appear once associated to the sentence they first appear in; the complement_list of the following appearances will be included in that relation.

Response examples

The format in which this information will be shown will depend on the value of the of parameter.

Arsene Wenger’s side sit third in the Premier League for the first time since September 22.

{
  "status": {
    "code": "0",
    "msg": "OK",
    "credits": "1"
  },
  "entity_list": [
    {
      "form": "Arsène Wenger",
      "id": "22c0bec63e",
      "sementity": {
        "class": "instance",
        "fiction": "nonfiction",
        "id": "ODENTITY_FULL_NAME",
        "type": "Top>Person>FullName"
      },
      "semgeo_list": [
        {
          "adm1": {
            "form": "England",
            "id": "98db781864"
          },
          "continent": {
            "form": "Europe",
            "id": "0404ea4d6c"
          },
          "country": {
            "form": "United Kingdom",
            "id": "d29f412b4b",
            "standard_list": [
              {
                "id": "ISO3166-1-a2",
                "value": "GB"
              },
              {
                "id": "ISO3166-1-a3",
                "value": "GBR"
              }
            ]
          }
        }
      ],
      "semld_list": [
        "http://en.wikipedia.org/wiki/Arsène_Wenger",
        "http://es.wikipedia.org/wiki/Arsène_Wenger",
        "http://fr.wikipedia.org/wiki/Arsène_Wenger",
        "http://it.wikipedia.org/wiki/Arsène_Wenger",
        "http://ca.wikipedia.org/wiki/Arsène_Wenger",
        "http://pt.wikipedia.org/wiki/Arsène_Wenger",
        "http://zh.wikipedia.org/wiki/阿尔塞纳·温格",
        "http://ar.wikipedia.org/wiki/أرسين_فينغر",
        "http://gl.wikipedia.org/wiki/Arsène_Wenger",
        "sumo:Human"
      ],
      "semtheme_list": [
        {
          "id": "ODTHEME_FOOTBALL",
          "type": "Top>Sport>Football"
        }
      ],
      "variant_list": [
        {
          "form": "Arsene Wenger",
          "inip": "0",
          "endp": "12"
        }
      ],
      "relevance": "100"
    },
    {
      "form": "Premier League",
      "id": "aa013efa70",
      "sementity": {
        "class": "instance",
        "fiction": "nonfiction",
        "id": "ODENTITY_GAMES",
        "type": "Top>Event>Occasion>Games"
      },
      "semgeo_list": [
        {
          "continent": {
            "form": "Europe",
            "id": "0404ea4d6c"
          },
          "country": {
            "form": "United Kingdom",
            "id": "d29f412b4b",
            "standard_list": [
              {
                "id": "ISO3166-1-a2",
                "value": "GB"
              },
              {
                "id": "ISO3166-1-a3",
                "value": "GBR"
              }
            ]
          }
        }
      ],
      "semld_list": [
        "sumo:GameEvent"
      ],
      "semtheme_list": [
        {
          "id": "ODTHEME_FOOTBALL",
          "type": "Top>Sport>Football"
        }
      ],
      "variant_list": [
        {
          "form": "Premier League",
          "inip": "38",
          "endp": "51"
        }
      ],
      "relevance": "100"
    }
  ],
  "time_expression_list": [
    {
      "form": "the first time",
      "inip": "57",
      "endp": "70"
    },
    {
      "form": "September 22",
      "normalized_form": "|||||9|22||||",
      "actual_time": "2015-09-22",
      "precision": "day",
      "inip": "78",
      "endp": "89"
    }
  ]
}

A thousand dollars could be spent trying to tackle a parking problem.

{
  "status": {
    "code": "0",
    "msg": "OK",
    "credits": "1"
  },
  "concept_list": [
    {
      "form": "dollar",
      "official_form": "United States dollar",
      "id": "7b6858c50a",
      "dictionary": "-",
      "sementity": {
        "class": "class",
        "fiction": "nonfiction",
        "id": "ODENTITY_CURRENCY",
        "type": "Top>Unit>Currency"
      },
      "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:UnitOfCurrency"
      ],
      "semtheme_list": [
        {
          "id": "ODTHEME_ECONOMY",
          "type": "Top>SocialSciences>Economy"
        }
      ],
      "standard_list": [
        {
          "id": "ISO4217",
          "value": "USD"
        }
      ],
      "variant_list": [
        {
          "form": "dollars",
          "inip": "11",
          "endp": "17"
        }
      ],
      "relevance": "100"
    },
    {
      "form": "parking",
      "id": "5ca7bc2365",
      "dictionary": "-",
      "sementity": {
        "class": "class",
        "fiction": "nonfiction",
        "id": "ODENTITY_STATION_TOP",
        "type": "Top>Location>Facility>StationTop"
      },
      "semld_list": [
        "sumo:TransitTerminal"
      ],
      "variant_list": [
        {
          "form": "parking",
          "inip": "53",
          "endp": "59"
        }
      ],
      "relevance": "100"
    }
  ],
  "money_expression_list": [
    {
      "form": "A thousand dollars",
      "amount_form": "A thousand",
      "numeric_value": "1000",
      "currency": "USD",
      "inip": "0",
      "endp": "17"
    }
  ]
}

To cancel your flight, go to our web site www.example.com. If you do not see the option to revoke your flight online, call at 1 877 781 3229 to cancel your flight giving us your flight number (e.g: AA5683). Cancellations can be done until twenty four hours before flight.

{
  "status": {
    "code": "0",
    "msg": "OK",
    "credits": "1"
  },
  "entity_list": [
    {
      "form": "www.example.com",
      "id": "__12997512342771391110",
      "sementity": {
        "class": "instance",
        "type": "Top>Id>Url"
      },
      "variant_list": [
        {
          "form": "www.example.com",
          "inip": "42",
          "endp": "56"
        }
      ],
      "relevance": "100"
    },
    {
      "form": "1 877 781 3229",
      "id": "__6241182866321175286",
      "sementity": {
        "class": "instance",
        "type": "Top>Id>PhoneNumber"
      },
      "variant_list": [
        {
          "form": "1 877 781 3229",
          "inip": "126",
          "endp": "139"
        }
      ],
      "relevance": "100"
    }
  ],
  "quantity_expression_list": [
    {
      "form": "until twenty four hours",
      "amount_form": "twenty four",
      "numeric_value": "0",
      "unit": "hour",
      "inip": "233",
      "endp": "255"
    }
  ],
  "other_expression_list": [
    {
      "form": "AA5683",
      "type": "flight number",
      "inip": "198",
      "endp": "203"
    }
  ]
}

The child said that his brother was at Harvard University.

{
  "status": {
    "code": "0",
    "msg": "OK",
    "credits": "1"
  },
  "quotation_list": [
    {
      "form": "his brother was at Harvard University.",
      "who": "The child",
      "who_lemma": "child",
      "verb": "said",
      "verb_lemma": "say",
      "inip": "20",
      "endp": "57"
    }
  ],
  "relation_list": [
    {
      "form": "The child said that his brother was at Harvard University.",
      "inip": "0",
      "endp": "56",
      "subject": {
        "form": "The child",
        "lemma_list": [
          "child"
        ],
        "sense_id_list": [
          "4629d1f492",
          "88b99f4a4d"
        ]
      },
      "verb": {
        "form": "said",
        "lemma_list": [
          "say"
        ],
        "sense_id_list": [
          "ODENTITY_COMMUNICATION_PROCESS",
          "ODENTITY_LINGUISTIC_COMMUNICATION",
          "ODENTITY_PROCESS"
        ]
      },
      "complement_list": [
        {
          "form": "that his brother was at Harvard University",
          "type": "isDirectObject"
        }
      ],
      "degree": "1"
    },
    {
      "form": "The child said that his brother was at Harvard University.",
      "inip": "15",
      "endp": "56",
      "subject": {
        "form": "his brother",
        "lemma_list": [
          "brother"
        ],
        "sense_id_list": [
          "ac9a5a05e2"
        ]
      },
      "verb": {
        "form": "was",
        "lemma_list": [
          "be"
        ]
      },
      "complement_list": [
        {
          "form": "at Harvard University",
          "type": "isComplement"
        }
      ],
      "degree": "1"
    }
  ]
}

Arsene Wenger’s side sit third in the Premier League for the first time since September 22.

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    <![CDATA[OK]]>
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        <![CDATA[Arsène Wenger]]>
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          <![CDATA[http://en.wikipedia.org/wiki/Arsène_Wenger]]>
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          <![CDATA[http://gl.wikipedia.org/wiki/Arsène_Wenger]]>
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          <![CDATA[sumo:Human]]>
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            <![CDATA[Top>Sport>Football]]>
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            <![CDATA[Arsene Wenger]]>
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          <![CDATA[ODENTITY_GAMES]]>
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          <![CDATA[Top>Event>Occasion>Games]]>
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              </standard>
            </standard_list>
          </country>
        </semgeo>
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          <![CDATA[sumo:GameEvent]]>
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            <![CDATA[ODTHEME_FOOTBALL]]>
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            <![CDATA[Top>Sport>Football]]>
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            <![CDATA[Premier League]]>
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    </entity>
  </entity_list>
  <time_expression_list>
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        <![CDATA[the first time]]>
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      <inip>57</inip>
      <endp>70</endp>
    </time_expression>
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      <form>
        <![CDATA[September 22]]>
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      <normalized_form>
        <![CDATA[|||||9|22||||]]>
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      <actual_time>2015-09-22</actual_time>
      <precision>day</precision>
      <inip>78</inip>
      <endp>89</endp>
    </time_expression>
  </time_expression_list>
</response>

A thousand dollars could be spent trying to tackle a parking problem.

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  <status code="0" credits="1">
    <![CDATA[OK]]>
  </status>
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      <form>
        <![CDATA[dollar]]>
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      <official_form>
        <![CDATA[United States dollar]]>
      </official_form>
      <id>7b6858c50a</id>
      <dictionary>-</dictionary>
      <sementity>
        <class>
          <![CDATA[class]]>
        </class>
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          <![CDATA[nonfiction]]>
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        <id>
          <![CDATA[ODENTITY_CURRENCY]]>
        </id>
        <type>
          <![CDATA[Top>Unit>Currency]]>
        </type>
      </sementity>
      <semgeo_list>
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              <![CDATA[America]]>
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            <id>
              <![CDATA[33fc13e6dd]]>
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            <id>
              <![CDATA[beac1b545b]]>
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          <![CDATA[sumo:UnitOfCurrency]]>
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            <![CDATA[ODTHEME_ECONOMY]]>
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      <standard_list>
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          <id>
            <![CDATA[ISO4217]]>
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          <value>
            <![CDATA[USD]]>
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      <variant_list>
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            <![CDATA[dollars]]>
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          <endp>17</endp>
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      <relevance>100</relevance>
    </concept>
    <concept>
      <form>
        <![CDATA[parking]]>
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      <id>5ca7bc2365</id>
      <dictionary>-</dictionary>
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        <class>
          <![CDATA[class]]>
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          <![CDATA[nonfiction]]>
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        <id>
          <![CDATA[ODENTITY_STATION_TOP]]>
        </id>
        <type>
          <![CDATA[Top>Location>Facility>StationTop]]>
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      <semld_list>
        <semld>
          <![CDATA[sumo:TransitTerminal]]>
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            <![CDATA[parking]]>
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      <form>
        <![CDATA[A thousand dollars]]>
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      <amount_form>
        <![CDATA[A thousand]]>
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      <currency>
        <![CDATA[USD]]>
      </currency>
      <inip>0</inip>
      <endp>17</endp>
    </money_expression>
  </money_expression_list>
</response>

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<response>
  <status code="0" credits="1">
    <![CDATA[OK]]>
  </status>
  <entity_list>
    <entity>
      <form>
        <![CDATA[www.example.com]]>
      </form>
      <id>
        <![CDATA[__12997512342771391110]]>
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      <sementity>
        <class>
          <![CDATA[instance]]>
        </class>
        <type>
          <![CDATA[Top>Id>Url]]>
        </type>
      </sementity>
      <variant_list>
        <variant>
          <form>
            <![CDATA[www.example.com]]>
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          <inip>42</inip>
          <endp>56</endp>
        </variant>
      </variant_list>
      <relevance>100</relevance>
    </entity>
    <entity>
      <form>
        <![CDATA[1 877 781 3229]]>
      </form>
      <id>
        <![CDATA[__6241182866321175286]]>
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        <class>
          <![CDATA[instance]]>
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        <type>
          <![CDATA[Top>Id>PhoneNumber]]>
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            <![CDATA[1 877 781 3229]]>
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          <inip>126</inip>
          <endp>139</endp>
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      </variant_list>
      <relevance>100</relevance>
    </entity>
  </entity_list>
  <quantity_expression_list>
    <quantity_expression>
      <form>
        <![CDATA[until twenty four hours]]>
      </form>
      <amount_form>
        <![CDATA[twenty four]]>
      </amount_form>
      <numeric_value>0</numeric_value>
      <unit>
        <![CDATA[hour]]>
      </unit>
      <inip>233</inip>
      <endp>255</endp>
    </quantity_expression>
  </quantity_expression_list>
  <other_expression_list>
    <other_expression>
      <form>
        <![CDATA[AA5683]]>
      </form>
      <type>flight number</type>
      <inip>198</inip>
      <endp>203</endp>
    </other_expression>
  </other_expression_list>
</response>

The child said that his brother was at Harvard University.

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<response>
  <status code="0" credits="1">
    <![CDATA[OK]]>
  </status>
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        <![CDATA[his brother was at Harvard University.]]>
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      <who>
        <![CDATA[The child]]>
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      <who_lemma>
        <![CDATA[child]]>
      </who_lemma>
      <verb>
        <![CDATA[said]]>
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      <verb_lemma>
        <![CDATA[say]]>
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      <inip>20</inip>
      <endp>57</endp>
    </quotation>
  </quotation_list>
  <relation_list>
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      <form>
        <![CDATA[The child said that his brother was at Harvard University.]]>
      </form>
      <inip>0</inip>
      <endp>56</endp>
      <subject>
        <form>
          <![CDATA[The child]]>
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        <lemma_list>
          <lemma>
            <![CDATA[child]]>
          </lemma>
        </lemma_list>
        <sense_id_list>
          <sense_id>
            <![CDATA[4629d1f492]]>
          </sense_id>
          <sense_id>
            <![CDATA[88b99f4a4d]]>
          </sense_id>
        </sense_id_list>
      </subject>
      <verb>
        <form>
          <![CDATA[said]]>
        </form>
        <lemma_list>
          <lemma>
            <![CDATA[say]]>
          </lemma>
        </lemma_list>
        <sense_id_list>
          <sense_id>
            <![CDATA[ODENTITY_COMMUNICATION_PROCESS]]>
          </sense_id>
          <sense_id>
            <![CDATA[ODENTITY_LINGUISTIC_COMMUNICATION]]>
          </sense_id>
          <sense_id>
            <![CDATA[ODENTITY_PROCESS]]>
          </sense_id>
        </sense_id_list>
      </verb>
      <complement_list>
        <complement>
          <form>
            <![CDATA[that his brother was at Harvard University]]>
          </form>
          <type>isDirectObject</type>
        </complement>
      </complement_list>
      <degree>1</degree>
    </relation>
    <relation>
      <form>
        <![CDATA[The child said that his brother was at Harvard University.]]>
      </form>
      <inip>15</inip>
      <endp>56</endp>
      <subject>
        <form>
          <![CDATA[his brother]]>
        </form>
        <lemma_list>
          <lemma>
            <![CDATA[brother]]>
          </lemma>
        </lemma_list>
        <sense_id_list>
          <sense_id>
            <![CDATA[ac9a5a05e2]]>
          </sense_id>
        </sense_id_list>
      </subject>
      <verb>
        <form>
          <![CDATA[was]]>
        </form>
        <lemma_list>
          <lemma>
            <![CDATA[be]]>
          </lemma>
        </lemma_list>
      </verb>
      <complement_list>
        <complement>
          <form>
            <![CDATA[at Harvard University]]>
          </form>
          <type>isComplement</type>
        </complement>
      </complement_list>
      <degree>1</degree>
    </relation>
  </relation_list>
</response>

Response

Response

Response output

Did you know...?

Entity/concept object

Entity/Concept object

Time expression object

Time expression object

Money expression object

Money expression object

Quantity expression object

Quantity object

Other expression object

Other expression object

Quotations object

Quotation object

Relations object

Relation object

Response examples