ガイド連動クイズ · 中 レベル
品詞タグ付けクイズ
POS カテゴリ、文脈上のあいまいさ、Penn Treebank タグ、HMM 遷移とエミッション、VBD、NLP 機能、UD カテゴリ、および構文関係を確認します。
質問 1 の 8
Which type of annotation does POS tagging assign to a token in context?
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- Which type of annotation does POS tagging assign to a token in context?
- A document-level spam label only
- A grammatical category such as noun, verb, or adjective
- A translation into a required target language
- A guarantee of the token's real-world referent
- POS タグ付けにコンテキストが不可欠なのはなぜですか?
- すべての単語には可能なタグが 1 つだけあるため、
- タグ付けは文章を完全に無視するため
- 長い文のほうがずっと楽だから
- 多くの単語は使用法に応じて品詞が異なるため、
- How should the core Penn Treebank POS inventory be distinguished from its additional punctuation annotations?
- It contains exactly two labels and no punctuation annotations
- It is a database containing only book reviews
- It has 36 core POS tags, with punctuation tags also used
- It provides a different trained neural model for every tag
- Which probabilities does a classic HMM POS tagger combine when scoring a tag sequence?
- Tag-to-tag transitions and word emissions given tags
- Only word filenames and image brightness
- Only probabilities of a tag given a word with no sequence model
- A requirement that every tag have equal probability
- In the Penn Treebank POS convention, which grammatical property does VBD mark?
- A singular noun
- An adjective
- A coordinating conjunction
- A past-tense verb
- Which language-processing experiment can use POS tags as additional input features?
- 画像ファイルの圧縮
- 固有表現の認識
- Audio amplifier volume control
- Disk encryption
- How many universal POS categories does the stated Universal Dependencies version 2 inventory define?
- Seventeen
- 2
- Thirty-six core Penn categories
- One hundred compulsory English-only categories
- In Universal Dependencies, how does NOUN differ from nsubj?
- Both are required names for the same trained model
- NOUN marks sentence sentiment; nsubj marks image size
- NOUN is a word category; nsubj is a nominal-subject relation
- Both necessarily mean that a word is a past-tense verb