tieval: An Evaluation Framework for Temporal Information Extraction Systems

要約

タイトル：tieval：時間情報抽出システムの評価フレームワーク
要約：

– 過去20年間で、時間情報抽出（TIE）は多大な関心を集め、膨大なデータセットの開発につながった。
– 大量のコーパスにアクセスできることは利点であるが、競合他社との比較を妨げるため、異なるデータセットには異なる注釈スキームがある。
– 各コーパスが通常異なる形式で配信されるため、すべてのコーパスのパーサーを開発するには相当なエンジニアリング努力が必要である。
– これらの問題がシステムの公平な比較を制限し、したがって時間的抽出システムの開発を妨げた。
– tievalは、異なるコーパスをインポートするための簡潔なインターフェースを提供し、システムの評価を容易にするPythonライブラリとして開発された。
– 本稿では、tievalの最初の公開版を紹介し、その最も重要な機能を強調する。

要約(オリジナル)

Temporal information extraction (TIE) has attracted a great deal of interest over the last two decades, leading to the development of a significant number of datasets. Despite its benefits, having access to a large volume of corpora makes it difficult when it comes to benchmark TIE systems. On the one hand, different datasets have different annotation schemes, thus hindering the comparison between competitors across different corpora. On the other hand, the fact that each corpus is commonly disseminated in a different format requires a considerable engineering effort for a researcher/practitioner to develop parsers for all of them. This constraint forces researchers to select a limited amount of datasets to evaluate their systems which consequently limits the comparability of the systems. Yet another obstacle that hinders the comparability of the TIE systems is the evaluation metric employed. While most research works adopt traditional metrics such as precision, recall, and $F_1$, a few others prefer temporal awareness — a metric tailored to be more comprehensive on the evaluation of temporal systems. Although the reason for the absence of temporal awareness in the evaluation of most systems is not clear, one of the factors that certainly weights this decision is the necessity to implement the temporal closure algorithm in order to compute temporal awareness, which is not straightforward to implement neither is currently easily available. All in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems. To mitigate these problems, we have developed tieval, a Python library that provides a concise interface for importing different corpora and facilitates system evaluation. In this paper, we present the first public release of tieval and highlight its most relevant features.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI