自然言語処理編

This long-awaited revision offers a comprehensive introduction to natural language understanding with developments and research in the field today. Building on the effective framework of the first edition, the new edition gives the same balanced coverage of syntax, semantics, and discourse, and offers a uniform framework based on feature-based context-free grammars and chart parsers used for syntactic and semantic processing. Thorough treatment of issues in discourse and context-dependent interpretation is also provided. In addition, this title offers coverage of two entirely new subject areas. First, the text features a new chapter on statistically-based methods using large corpora. Second, it includes an appendix on speech recognition and spoken language understanding. Also, the information on semantics that was covered in the first edition has been largely expanded in this edition to include an emphasis on compositional interpretation. 0805303340B04062001 1. Introduction to Natural Language Understanding. The Study of Language. Applications of Natural Language Understanding. Evaluating Language Understanding Systems. The Different Levels of Language Analysis. Representations and Understanding. The Organization of Natural Language Understanding Systems. 2. Linguistic Background: An Outline of English Syntax. Words. The Elements of Simple Noun Phrases. Verb Phrases and Simple Sentences. Noun Phrases Revisited. Adjective Phrases. Adverbial Phrases. 3. Grammars and Parsing. Grammars and Sentence Structure. What Makes a Good Grammar. A Top-Down Parser. A Bottom-Up Chart Parser. Top-Down Chart Parsing. Finite State Models and Morphological Processing. Grammars and Logic Programming. 4. Features and Augmented Grammars. Feature Systems and Augmented Grammars. Some Basic Feature Systems for English. Morphological Analysis and the Lexicon. A Simple Grammar Using Features. Parsing with Features. Augmented Transition Networks. Definite Clause Grammars. Generalized Feature Systems and Unification Grammars. 5. Grammars for Natural Language. Auxiliary Verbs and Verb Phrases. Movement Phenomena in Language. Handling Questions in Context-Free Grammars. Noun Phrases and Relative Clauses. The Hold Mechanism in ATN. Gap Threading. 6. Toward Efficient Parsing. Human Preferences in Parsing. Encoding Uncertainty: Shift-Reduce Parsers. A Deterministic Parser. Techniques for Efficient Encoding of Ambiguity. Partial Parsing. 7. Ambiguity Resolution: Statistical Methods. Basic Probability Theory. Estimating Probabilities. Part-of-Speech Tagging. Obtaining Lexical Probabilities. Probabilistic Context-Free Grammars. Best-First Parsing. A Simple Context-Dependent Best-First Parser. 8. Semantics and Logical Form. Semantics and Logical Form. Word Senses and Ambiguity. The Basic Logical Form Language. Encoding Ambiguity in Logical Form. Verbs and States in Logical Form. Case Relations. Speech Acts and Embedded Sentences. Defining Semantic Structure: Model Theory. 9. Linking Syntax and Semantics. Semantic Interpretation and Compositionality. A Simple Grammar and Lexicon with Semantic Interpretation. Prepositional Phrases and Verb Phrases. Lexicalized Semantic Interpretation and Semantic Roles. Handling Simple Questions. Semantic Interpretation Using Feature Unification Generating Sentences from Logical Form. 10. Ambiguity Resolution. Selectional Restrictions. Semantic Filtering Using Selectional Restrictions. Semantic Networks. Statistical Word Sense Disambiguation. Statistical Semantic Preferences. Combining Approaches to Disambiguation. 11. Other Strategies for Semantic Interpretation. Grammatical Relations. Semantic Grammars. Template Matching. Semantically-Directed Parsing Techniques. 12. Scoping and the Interpretation of Noun Phrases. Scoping Phenomena. Definite Descriptions and Scoping. A Method for Scoping While Parsing. Co-Reference and Binding Constraints. Adjective Phrases. Relational Nouns and Nominalizations. Other Problems in Semantics. 13. Knowledge Representation and Reasoning. Knowledge Representation. A Representation Based on FOPC. Frames: Representing Stereotypical Information. Handling Natural Language Quantification. Time and Aspectual Classes of Verbs. Automating Deduction in Logic-Based Representations. Procedural Semantics and Question Answering. Hybrid Knowledge Representations. 14. Local Discourse Context and Reference. Defining Local Discourse Context and Discourse Entities. A Simple Model of Anaphora Based on History Lists. Pronouns and Centering. Definite Descriptions. Definite Reference and Sets. Ellipsis. Surface Anaphora. 15. Using World Knowledge. Using World Knowledge: Establishing Coherence. Matching Against Expectations. Reference and Matching Expectations. Using Knowledge About Action and Casualty. Scripts: Understanding Stereotypical Situations. Using Hierarchical Plans. Action-Effect-Based Reasoning. Using Knowledge About Rational Behavior. 16. Discourse Structure. The Need for Discourse Structure. Segmentation and Cue Phrases. Discourse Structure and Reference. Relating Discourse Structure and Inference. Discourse Structure, Tense, and Aspect. Managing the Attentional Stack. An Example. 17. Defining a Conversational Agent. What's Necessary to Build a Conversational Agent? Language as Multi-Agent Activity. Representing Cognitive State: Beliefs. Representing Cognitive State: Desires, Intentions, and Plans. Speech Acts and Communicative Acts. Planning. Communicative Acts. Communicative Acts and the Recognition of Intention. The Source of Intention in Dialogue. Recognizing Illocutionary Acts. Discourse and Level Planning. Appendices. An Introduction to Logic and Model Theoretic Semantics. Symbolic Computation. Spoken Language. 0805303340T04062001

ディープラーニング活用なくしてビジネスの飛躍的成長なし

データを利益に変える知恵とデザイン

人工知能 人工知能と世界の見方 人工知能と社会

個人の地域密着型アウトドアショップがデジタルマーケティングで業績を飛躍的にアップさせていくストーリーに乗せて、DMの基礎を… 個人の地域密着型アウトドアショップがデジタルマーケティングで業績を飛躍的にアップさせていくストーリーに乗せて、DMの基礎を学ぶ

強化学習編

本書は、言語研究者の関心に即したプログラミングの入門書です。近年、注目を集めているPythonを取り上げ、英語や日本語の言語分析のための具体的な処理例が多数収録されています。フィールドワークでも実験研究でもコーパス研究でも、電子テキストを使ったあらゆるタイプの研究者にすぐに実践できるテクニックを紹介しています。本書を通じてプログラミング処理が持つ威力を実感し、自分の研究に活用してください。

AIブームでニーズが急上昇!形態素解析の理論と実装を，技術者向けて網羅的に解説!! C++11による実装方法も掲載 あの工藤 拓が具体的に解説 !! 本書は，汎用形態素解析システムMeCabを開発した著者が、言語において意味を成す最小の要素である「形態素」の解析方法について，技術者向けにその理論や実装方法を網羅的，体系的に解説する．実装や高速化なども扱う点がユニークであるが、辞書やコーパスなどの言語資源の構築・利用といった形態素解析では外せないテーマもきちんと解説している． 本書を読めば，解析ツールを「ブラックボックス」として使っている人も中身を理解したうえで拡張・改良できる道筋ができ，ひいては独自の辞書の作成を目指せるようになる．C++11を使った具体的な実装方法も掲載．AI・自然言語処理関係の技術者，研究者には必携の書である． 汎用形態素解析システムMeCabを開発した著者が、言語において意味を成す最小の要素である形態素の解析方法について，技術者向けにその理論や実装方法を網羅的，体系的に解説．AI・自然言語処理関係の技術者，研究者必携 1．形態素解析の概要 2．言語資源 3．テキスト処理の基礎 4．辞書引きアルゴリズム 5．最小コスト法 6．点予測 7．未知語処理 8．評価 9．高度な解析