2 0 0 0 OA 専門用語の定義と説明の条件
2 0 0 0 柔軟な意味解析のための概念空間の定量化
- 著者
- 石崎 俊 田中 茂範 今井 むつみ
- 出版者
- 一般社団法人情報処理学会
- 雑誌
- 情報処理学会研究報告自然言語処理(NL)
- 巻号頁・発行日
- vol.1994, no.28, pp.17-24, 1994-03-17
- 被引用文献数
- 6
自然な発話状況における対話では、従来の文法に基づいて判断するとおかしいが、意味は自然でよく分かる文が多く見られる。そのような対話文では意味を主体にした解析手法が望ましい。しかし、従来の意味解析手法や辞書の機能では「固い」ため、そのような意味解析を実行するには不十分なことが多い。そこで、本研究では、従来よりも柔軟な意味解析機構をもつモデルの構築を目標とし、それに適した意味解析法と、概念辞書における距離空間の導入について基礎的な検討を行う。概念辞書において概念間の距離を、従来のように階層構造をたどるのではなく、多次元尺度法を用いて定量的に距離を測ることによって距離空間を構成することを検討する。次に、状態を表す概念を指標としたSD法を用いて意味空間における距離を計算し、動的な意味の変化について基礎的な検討を行う。In spontaneous conversation, we have many ungrammatical but semantically natural sentences. Analysis based on semantics or context seems appropriate for such conversational texts. The traditional methods for semantic analysis or structures of concept dictionaries are not enough to treat with such texts. This paper discusses, therefore, a computational model which has a flexible mechanism for semantic analysis and discusses functions and a structure which concept dictionaries should have. For example, using cognitive psychological experiments, MDS method, a metric space among concepts can be organized. Then, SD method using concepts of state as parameters introduces a metric space where dynamic semantic analysis can be formalized. This paper discusses possibilities of these new approaches.
1 0 0 0 OA 味覚表現における音象徴語の使用原理
- 著者
- 福島 宙輝 田中 茂範
- 出版者
- 一般社団法人 人工知能学会
- 雑誌
- 人工知能学会論文誌 (ISSN:13460714)
- 巻号頁・発行日
- vol.31, no.6, pp.AI30-N_1-8, 2016-11-01 (Released:2017-12-20)
- 参考文献数
- 15
The perceptual domain of taste presents a symbol-grounding problem in which language does not capture the target domain straightforwardly. We suggest that analogy, defined as a process of finding relevance in a metaphorical expression, is a key to handling the problem in the domain of taste. In this study, we took up sound symbolism as a case of expressing the tastes of wine and sake, and did a quantitative text analysis to demonstrate how sound symbolic expressions function in the description of wine and sake (word count of wine text: 201,294; word count of sake: 50,147). The text analysis showed both similarities and differences between the two texts. In both texts, sound symbolism is used as a means of increasing the expressive power of general and abstract words referring to the tastes of wine and sake. In the sake text, however, the use of sound symbolism was dominant in the pre-taste and post-taste stages, while in the wine text, expressions of complex tastes were highly sound symbolic. The different functions of sound symbolism presumably correspond to the differences between wine and sake in terms of the manner of tasting.