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Classifying Temporal Relations by
Bidirectional LSTM over Dependency Paths
長岡技術科学大学 自然言語処理研究室
修士1年 西山 浩気
1
Fei Cheng and Y...
 時間的な関係を分類する研究
 多くの方法で外部知識に依存している
 知識を特徴として加えるだけでは不十分
 関係抽出の最先端の手法
 従属パスに沿った Bidirectional LSTM (Bi-LSTM)
 人手で注釈を付けら...
 時間情報を利用したタスク
✓質問応答 (QA)
✓情報抽出 (IE)
 時間情報を付与したコーパス
✓TimeBank1[Pustejovsky et al., ‘03]
2.1 時間情報抽出タスクの応用例 はじめに
3
 時間的関係を持つ事象あるいは表現を識別
 時間的関係を以下の3つに分類
1. DURING (Event - Event)
• There was no hint of trouble in the last conversation b...
 [Marcu and Echihabi ‘02]
 単語ベースの対を特徴量に加える
 [Laokulrat et al., ‘13; Chambers et al., ‘14; Mani et al, ’06;
D’Souza and ...
 関係抽出タスク
 [Bunescu and Mooney, ‘05]
• 依存関係グラフを作成
• 2つのエンティティ間の最短移動パス(SDP)
 [Plank and Moschitti, ‘13]
• 構文木で構文と意味情報を抽出
...
 時間的関係分類タスク
 [Laokulrat et al., ’13;Chamber et al., ‘14]
• WordNetのsynsetから語彙的な特徴を利用
 [Mani et al., ’06; D’Souza and Ng...
 依存関係パス(SDP)を時間的分類タスクに導入
3.1 Cross-sentence Dependency Paths 提案手法
8
TimeBankコーパス
Stanford CoreNLP4
を利用して関係を抽出
 Bi-LSTMをを用いて時間的関係クラスを分類
 word2Vec [Mikolov et al., ‘13a,b]を利用して
200次元の単語ベクトルを入力
3.2 Temporal Relation Classifires 提案手法
9
Baseline
 単語, 品詞を入力して、依存関係の情報無しに推定するモデル
Mirza [Mirza and Tonelli, ’16]
 低次元の単語ベクトル + WordNetなどの意味情報を表すベクトル
CAEVO
 人手...
✓ システム全体では提案手法が最も優れる
✓ E-D, E-TにおいてはCAEVOが優れる
 提案手法は人手で付与された規則は利用していない
外部資源・人手での規則付与無しに
2つの最先端手法と同程度の性能を実現
4.2 提案手法の性能評価 ...
 従属経路ベースのニューラルネットワークを
関係分類に利用
 Bi-LSTMを用いて順/逆方向の情報を学習
 外部リソースを使用せずに2つの最先端システム
と同程度の性能を達成
まとめ
12
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Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths

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Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths

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Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths

  1. 1. Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths 長岡技術科学大学 自然言語処理研究室 修士1年 西山 浩気 1 Fei Cheng and Yusuke Miyao, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 1–6 Vancouver, Canada, July 30 - August 4, 2017
  2. 2.  時間的な関係を分類する研究  多くの方法で外部知識に依存している  知識を特徴として加えるだけでは不十分  関係抽出の最先端の手法  従属パスに沿った Bidirectional LSTM (Bi-LSTM)  人手で注釈を付けられた情報を使用することなく、 従来と同等の性能を実現 1. 概要 2
  3. 3.  時間情報を利用したタスク ✓質問応答 (QA) ✓情報抽出 (IE)  時間情報を付与したコーパス ✓TimeBank1[Pustejovsky et al., ‘03] 2.1 時間情報抽出タスクの応用例 はじめに 3
  4. 4.  時間的関係を持つ事象あるいは表現を識別  時間的関係を以下の3つに分類 1. DURING (Event - Event) • There was no hint of trouble in the last conversation between controllers and TWA pilot Steven Snyder. 2. INCLUDES (Event - Time) • In Washington today, the Federal Aviation Administration released air traffic control tapes. 3. BEFORE (Event - Document) • The U.S. Navy has 27 ships in the maritime barricade of Iraq. 2.2 時間情報抽出タスクについて はじめに 4
  5. 5.  [Marcu and Echihabi ‘02]  単語ベースの対を特徴量に加える  [Laokulrat et al., ‘13; Chambers et al., ‘14; Mani et al, ’06; D’Souza and Ng, 2013]  Word Net[Miller ‘95]  Verb Ocean [Chklovski and Pantel, ‘04] などの語彙・構文情報 これらの知識資源を高精度に獲得することは困難 2.3 従来手法 はじめに 5
  6. 6.  関係抽出タスク  [Bunescu and Mooney, ‘05] • 依存関係グラフを作成 • 2つのエンティティ間の最短移動パス(SDP)  [Plank and Moschitti, ‘13] • 構文木で構文と意味情報を抽出  [Socher et al., ‘11, Xu et al., ‘15a,b] state-of-the-art • 依存関係経路上にニューラルネットワークを構築 2.4 関連研究 はじめに 6
  7. 7.  時間的関係分類タスク  [Laokulrat et al., ’13;Chamber et al., ‘14] • WordNetのsynsetから語彙的な特徴を利用  [Mani et al., ’06; D’Souza and Ng., ‘13] • VerbOceanで動詞間の意味的関係を利用  どちらの研究においてもTimeML2コーパスを利用 ✓ 本研究では 単語, 品詞, 依存性解析情報のみを使用 2.4 関連研究 はじめに 7
  8. 8.  依存関係パス(SDP)を時間的分類タスクに導入 3.1 Cross-sentence Dependency Paths 提案手法 8 TimeBankコーパス Stanford CoreNLP4 を利用して関係を抽出
  9. 9.  Bi-LSTMをを用いて時間的関係クラスを分類  word2Vec [Mikolov et al., ‘13a,b]を利用して 200次元の単語ベクトルを入力 3.2 Temporal Relation Classifires 提案手法 9
  10. 10. Baseline  単語, 品詞を入力して、依存関係の情報無しに推定するモデル Mirza [Mirza and Tonelli, ’16]  低次元の単語ベクトル + WordNetなどの意味情報を表すベクトル CAEVO  人手で付与されたルールを利用 4.1 提案手法の性能評価 実験 10
  11. 11. ✓ システム全体では提案手法が最も優れる ✓ E-D, E-TにおいてはCAEVOが優れる  提案手法は人手で付与された規則は利用していない 外部資源・人手での規則付与無しに 2つの最先端手法と同程度の性能を実現 4.2 提案手法の性能評価 実験 11
  12. 12.  従属経路ベースのニューラルネットワークを 関係分類に利用  Bi-LSTMを用いて順/逆方向の情報を学習  外部リソースを使用せずに2つの最先端システム と同程度の性能を達成 まとめ 12

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