Attend the Women in ML Symposium on December 7 Register now

multi_booked

References:

ca

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:multi_booked/ca')
  • Description:
MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.

The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project.
  • License: CC-BY 3.0
  • Version: 0.0.0
  • Splits:
Split Examples
'train' 567
  • Features:
{
    "text": {
        "feature": {
            "wid": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "sent": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "para": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "word": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "terms": {
        "feature": {
            "tid": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "lemma": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "morphofeat": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "pos": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "target": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "opinions": {
        "feature": {
            "oid": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "opinion_holder_target": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "opinion_target_target": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "opinion_expression_polarity": {
                "num_classes": 4,
                "names": [
                    "StrongNegative",
                    "Negative",
                    "Positive",
                    "StrongPositive"
                ],
                "names_file": null,
                "id": null,
                "_type": "ClassLabel"
            },
            "opinion_expression_target": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

eu

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:multi_booked/eu')
  • Description:
MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.

The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project.
  • License: CC-BY 3.0
  • Version: 0.0.0
  • Splits:
Split Examples
'train' 343
  • Features:
{
    "text": {
        "feature": {
            "wid": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "sent": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "para": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "word": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "terms": {
        "feature": {
            "tid": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "lemma": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "morphofeat": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "pos": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "target": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "opinions": {
        "feature": {
            "oid": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "opinion_holder_target": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "opinion_target_target": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "opinion_expression_polarity": {
                "num_classes": 4,
                "names": [
                    "StrongNegative",
                    "Negative",
                    "Positive",
                    "StrongPositive"
                ],
                "names_file": null,
                "id": null,
                "_type": "ClassLabel"
            },
            "opinion_expression_target": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}