First version of quoteli3 dataset

QuoteLi3.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+from nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils
+
+import xml.etree.ElementTree as ET
+import re
+
+_CITATION = """\
+@inproceedings{muzny2017two,
+  title={A two-stage sieve approach for quote attribution},
+  author={Muzny, Grace and Fang, Michael and Chang, Angel and Jurafsky, Dan},
+  booktitle={Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
+  pages={460--470},
+  year={2017}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset is a representation of Muzny et al.'s QuoteLi3 dataset as a Huggingface dataset
+"""
+
+_HOMEPAGE = "https://nlp.stanford.edu/~muzny/quoteli.html"
+
+_LICENSE = ""
+
+_URL = 'http://downloads.cs.stanford.edu/nlp/data/quoteattribution/'
+_URLs = {
+    'train': {'pp': _URL + 'pp_full.xml'},
+    'test': {'pp': 'https://nlp.stanford.edu/~muzny/data/pp_test.xml',
+            'emma': _URL + 'austen_emma_full.xml',
+            'steppe': _URL + 'chekhov_steppe_full.xml'}
+}
+
+class QuoteLi3(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="quotes", version=VERSION, description="Returns Quotes"),
+        datasets.BuilderConfig(name="characters", version=VERSION, description="Returns Characters")
+    ]
+
+    DEFAULT_CONFIG_NAME = "quotes"
+
+    def _info(self):
+        if self.config.name == "quotes":  #returns quotes
+            features = datasets.Features(
+                {
+                    "mention": datasets.Value("string"),
+                    "oid": datasets.Value("string"),
+                    "speaker": datasets.Value("string"),
+                    "connection": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "answer_mention": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "context": datasets.Value("string")
+                }
+            )
+        else: #returns characters
+            features = datasets.Features(
+                {
+                    "aliases": datasets.Sequence(datasets.Value("string")),
+                    "description": datasets.Value("string"),
+                    "gender": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                    "name": datasets.Value("string")
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_files = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"],
+                                                                           "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"],
+                                                                           "split": "test"}),
+        ]
+
+    def _generate_examples(
+        self, filepath, split
+    ):
+        """ Yields examples as (key, example) tuples. """
+        for key in filepath:
+            path = filepath[key]
+            with open(path, encoding="utf-8") as f:
+                quote_list = []
+                file_tree = ET.parse(f)
+                base_tree = file_tree.getroot()
+                chapter_list = base_tree.find('text').findall('chapter')
+                if len(chapter_list) != 0:
+                    for chapter in chapter_list:
+                        quotes = chapter.findall('quote')
+                        for quote in quotes:
+                            quote_list.append(quote)
+                else:
+                    quote_list = base_tree.find('text').findall('quote')
+
+                if self.config.name == "quotes":
+                    for quote in quote_list:
+                        quote_key = key + '_' + quote.attrib['id']
+                        x = self.find_mention(quote, path)
+                        if x == 'NO_MENTION':
+                            keys = []
+                            data = ''
+                            for for_key in quote.attrib.keys():
+                                keys.append(for_key)
+                                data += f'{for_key}: {quote.attrib[for_key]} _'
+                        yield quote_key, {
+                            "mention": quote.attrib["mention"] if 'mention' in quote.attrib else 'no_mention',
+                            "oid": quote.attrib["oid"] if 'oid' in quote.attrib else 'no_oid',
+                            "speaker": quote.attrib["speaker"] if 'speaker' in quote.attrib else 'no_speaker',
+                            "connection": quote.attrib["connection"] if 'connection' in quote.attrib else 'no_connection',
+                            "id": quote.attrib["id"] if 'id' in quote.attrib else 'no_id',
+                            "answer": "" if split == "test" else quote.attrib["speaker"],
+                            "answer_mention": self.find_mention(quote, path),
+                            "question": "Who says 'QUOTE'",
+                            "context": self.get_context(quote, path),
+                        }
+                else:
+                    character_list = base_tree.find('characters').findall('character')
+                    for character in character_list:
+                        character_key = key + '_' + character.attrib['id']
+                        yield character_key, {
+                            "aliases": character.attrib["aliases"].split() if 'aliases' in character.attrib else 'no_aliases',
+                            "description": character.attrib["description"] if 'description' in character.attrib else 'no_description',
+                            "gender": character.attrib["gender"] if 'gender' in character.attrib else 'no_gender',
+                            "name": character.attrib["name"] if 'name' in character.attrib else 'no_name',
+                            "id": character.attrib["id"] if 'id' in character.attrib else 'no_id',
+                        }
+
+
+    def find_mention(self, quote_element, filename):
+        connection = quote_element.attrib['connection']
+        file_tree = ET.parse(filename)
+        base_tree = file_tree.getroot()
+        mentions_list = []
+        text = base_tree.find('text')
+        chapters = text.findall('chapter')
+        if len(chapters) > 0:
+            for chapter in chapters:
+                mentions = chapter.findall('mention')
+                for mention in mentions:
+                    mentions_list.append(mention)
+
+                # if the mention is inside a quote
+                quotes = chapter.findall('quote')
+                for quote in quotes:
+                    mentions_in_quotes = quote.findall('mention')
+                    for mention in mentions_in_quotes:
+                        mentions_list.append(mention)
+        else:
+            mentions_list = base_tree.find('text').findall('mention')
+            #if the mention is inside a quote
+            quotes = text.findall('quote')
+            for quote in quotes:
+                mentions_in_quotes = quote.findall('mention')
+                for mention in mentions_in_quotes:
+                    mentions_list.append(mention)
+        mention_tail = ''
+        mention_text = ''
+        for mention in mentions_list:
+            current_id = mention.attrib['id']
+            if type(current_id) == str:
+                if mention.attrib['id'] in connection:
+                    mention_text = mention.text
+                    mention_tail = mention.tail
+                    break
+            else:
+                for single_id in current_id:
+                    if single_id in connection:
+                        mention_text = mention.text
+                        mention_tail = mention.tail
+                        break
+        if len(mention_tail) > 25:
+            mention_tail = mention_tail[:25]
+        search_text = mention_text + mention_tail
+        if mention_tail == '':
+            return 'NO_MENTION'
+        return mention_text
+
+    def get_context(self, quote_element, filename):
+        chapter_text = self.get_texts_by_file(filename)
+        #maximum range for the context in characters
+        max_range = 1000
+        quote = self.get_quote_content(quote_element)
+        start_index = chapter_text.find(quote)
+
+        pre = 500
+        post = max_range - pre
+        if start_index < pre:
+            start = 0
+            end = max_range
+        else:
+            start = int(start_index - pre)
+            end = int(start_index + post)
+
+        chapter_text = chapter_text.replace(quote, '"QUOTE"').replace('\n', ' ')
+        context = chapter_text[start:end]
+        return context
+
+
+    def get_texts_by_file(self, filename):
+        file_tree = ET.parse(filename)
+        base_tree = file_tree.getroot()
+        text_with_tags = ET.tostring(base_tree, encoding='unicode', method='xml')  # unicode -> utf8
+        text_without_tags = re.sub('<.*?>', '', text_with_tags)  # delete all tags
+        return text_without_tags
+
+    def get_quote_content(self, quote):
+        quote_text_tags = ET.tostring(quote, encoding='unicode', method='xml')
+        quote_text = re.sub('<quote.*?>', '', quote_text_tags)
+        end_of_quote = quote_text.find('</quote>')
+        quote_text = quote_text[:end_of_quote]
+        quote_text = re.sub('<.*?>', '', quote_text)
+        return quote_text