Datasets:
| from __future__ import annotations | |
| import re | |
| from dataclasses import dataclass | |
| from typing import Literal | |
| AspectWordIndices = tuple[int, ...] | |
| OpinionWordIndices = tuple[int, ...] | |
| Sentiment = Literal["NEG", "NEU", "POS"] | |
| Triplet = tuple[AspectWordIndices, OpinionWordIndices, Sentiment] | |
| word_pattern = re.compile(r"\S+") | |
| class WordSpan: | |
| start_index: int | |
| end_index: int # this is the letter after the end | |
| class WordSpans: | |
| spans: list[WordSpan] | |
| def make(text: str) -> WordSpans: | |
| spans = [ | |
| WordSpan(start_index=match.start(), end_index=match.end()) | |
| for match in word_pattern.finditer(text) | |
| ] | |
| return WordSpans(spans) | |
| def to_indices(self, span: tuple[int, ...]) -> tuple[int, int]: | |
| word_start = span[0] | |
| word_start_span = self.spans[word_start] | |
| word_end = span[-1] | |
| word_end_span = self.spans[word_end] | |
| return word_start_span.start_index, word_end_span.end_index - 1 | |
| class CharacterIndices: | |
| aspect_start_index: int | |
| aspect_end_index: int | |
| aspect_term: str | |
| opinion_start_index: int | |
| opinion_end_index: int | |
| opinion_term: str | |