nlp14 tutorial
nlp14 tutorial
Steps 3, 4 の重要性
❖
次はStep 4 (
かStep 3)
から判るがStep 2
から判る保証なし❖
「撤廃」は<
廃止>
の特殊な場合を意味するが，その格パ ターン“
動作主(x)
が 場所(l)
から モノ(y)
をV”
が<
除去>
の メタファーに由来❖
「明らかにした」の字義通りの意味<
明示化>
がこの文脈 で<
公表>
になる❖ これは「
<
団体>
が(<
部外者>
に対して) <
行動>
の方針を明らかに する」という連語の効果37
nlp14 tutorial
精度と被覆率のジレンマ
❖
フレームが決まっても述語は一つには決まらない❖
フレームと述語の対応は一対多❖
だが，述語を特定しないと#
要素(
定項)
の値が決まらない❖
述語と表層格パターンの対応は一対他❖
フレーム記述の一般性=
被覆率の低下=>
精度の向上Managing Annotation
39
nlp14 tutorial
精度保証のためのチェック
❖
文生成プログラム(+sgen)
の出力を見てタグづけの妥当性 を評価❖
定項と変項の実現値をフレームごとに自動連結したもの が自然な日本語文にならないなら，記述は妥当性を欠く❖
フレーム要素への区切り方と#
要素の値の妥当性は検査 できるが，フレーム要素名の妥当性は検査できないnlp14 tutorial
理論
❖ MSFA Lite
のフレーム記述を非線型表現/
パターン(
池原ら2005, 2006)
と同一視しするなら，MSFA Lite
を使った文s
の分析は，
s
の複数の非線型表現/
パターンp
1, p
2, ..., p
n への 分解だと見なせる❖
元のs
はp
1, p
2, ..., p
n の行ごとの“
重ね合わせ”
で得られる❖
これが正しいなら，p
i がおのおの非線型な単文を記述して いるはずなので，p
i の妥当性評価がMSFA
の間接的な妥当 性評価になる41
nlp14 tutorial
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1
2
3
4 5
6 7
8 9
10
+sgen の動作
❖
#
で始まる要素(e.g., #
が)
を定項，#
で始まらない 要素(e.g,
採用者)
を変項❖ 列を縦にスキャンし，範 囲が定項ならその値を，
変項ならその実現値を一 列目の形態素列に取りに 行く．
f.GOV, f.X=PRED
の場合だけ，フレーム名 かPredicate for GOV
の値 を参照42
nlp14 tutorial
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43
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nlp14 tutorial
粒度コントロール
❖
フレーム要素が認定される粒度は自明でも一定でもないの で，管理が必要❖
統語解析との対応づけの良し悪しは，この分析の粒度に強 く依存する45
nlp14 tutorial
粒度の異なり
❖ 左に行く粗い
❖ 右に行くと細かい
❖ 細かくなるほど，作業 者への要求大
❖
L2
ぐらいが最適?
❖
MSFA
粒度の統一の予定はないが，シートに 粒度指標を明示化する 予定
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nlp14 tutorial
Granularity Levels
❖ L0
ではフレーム要素(f.GOV
を含む)
とMARKER
の記述❖ Berkeley FrameNet
のアノテーションはL0
と同等で意外とスカスカ
❖ L1
ではL0
にフレームの喚起要素(= f.EVO)
とe.SUP
と埋め こみなしのe.MOD
の記述を追加し，“=PRED”
を許容❖ L2
ではL1
に埋めこみありのe.MOD, e.EXT
の記述を追加❖ L3
ではL2
にe.ARG
の記述を追加し，強制置換“=>”
を許容47