Bibliographic record
Abstract
本研究の究極的な目的は, 研究代表者が構築した認知言語学に基づく詳細な意味情報を付与した言語資源「日本語フレームネット」(JFN)を, 言語研究のみならず自然言語処理にとってもこれまで以上に有益な言語資源とする方策を検討することである。そのためには, JFNの規模を大きくさせることと, JFNの持つ豊富な意味情報に加え統語情報をも含める, という2つの方向性が考えられる。本研究では特に, 国立国語研究所で構築中のNINJAL Parsed Corpus of Modern Japanese (NPCMJ)とリンクさせることを検討した。NPCMJは文の統語情報のみで意味情報は含まないが規模は大きいのに対JFNは詳細な意味情報を含むが規模が小さく, 両者を統合することで深い意味処理を行う自然言語処理アプリケーションのための言語資源とすることができると考えたからである。 当初計画したJFNとNPCMJのリンク方法は2つあった。一つ目は, JFNで意味情報付与の際の入力データとしてNPCMJの統語情報付きデータを用いることである。二つ目は, JFNとNPCMJを統合させ, 認知科学的にも妥当な詳細な意味情報と文の統語情報の両方を含んだコーパスとすることである。前者に関しては, ツールを開発した。後者については実現までの目処がたった。 本研究の経過・成果は, 国際認知言語学会, マルチリンガルフレームネット会議, 言語処理学会第24回年次大会にて発表した。 本研究によりNPCMJとJFNの統合は実現可能であることが明らかとなった。今後はJFNを深い意味処理を行う自然言語処理アプリケーションにとってさらに有益な資源とするため, より規模の大きく自然言語処理分野で定評のある言語資源とのリンクを検討したい。 The principal investigator (PI) of this research project has been developing Japanese FrameNet (JFN), a language resource that contains rich semantic information which is based on the theory of Frame Semantics. The ultimate goal of this research project is to enable JFN to be useful for natural language processing (NLP) applications, especially for those that involve deep semantic processing. In order to achieve this goal, there are two directions. One is to make the size of JFN larger. The other is to add syntactic information to JFN, in addition to the existing semantic information. This research project pursued the latter approach and focused on ways to link the JFN data with the NINJAL (National Institute for Japanese Language and Linguistics) Parsed Corpus of Modern Japanese (NPCMJ). NPCMJ does not contain semantic information at all but includes detailed syntactic information of sentences and its size is larger than that of JFN. Therefore, it was expected that the combined language resource would be useful for various NLP applications. There are two ways to link the JFN data with that of NPCMJ. One is to use the NPCMJ data as input to the JFN annotation (tagging) process. The other is to integrate JFN and NPCMJ to build a combined corpus. As for the former, a tool was implemented to allow the NPCMJ data as input to the JFN annotation process. As for the latter, how to combine the two resources was investigated. The accomplishments of the project were presented at ICLC14 (Tartu, Estonia ; July 2017), the Multilingual FrameNet meeting (Vancouver, Canada ; Aug. 2017), and NLP2018 (Okayama, Mar. 2018). Through this research project, we were able to confirm that NPCMJ and JFN can be combined. Future plans include attempting to link JFN with a language resource which is more suitable for semantic-processing NLP applications and which is larger than NPCMJ.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".