is one of the most recognizablecharacters in 20th century cinema. HAL is an artiﬁcial agent capable of such advancedlanguage behavior as speaking and understanding English, and at a crucial moment inthe plot, even reading lips. It is now clear that HAL’s creator, Arthur C. Clarke, wasa little optimistic in predicting when an artiﬁcial agent such as HAL would be avail-able. But just how far off was he? What would it take to create at least the language-relatedpartsofHAL?WecallprogramslikeHALthatconversewithhumansinnatural

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Speech and Language Processing

This article describes three statistical models for natural language parsing. The models extend methods from probabilistic context-free grammars to lexicalized grammars, leading to approaches in which a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree. Independence assumptions then lead to parameters that encode the X-bar schema, subcategorization, ordering of complements, placement of adjuncts, bigram lexical dependencies, wh-movement, and preferences for close attachment. All of these preferences are expressed by probabilities conditioned on lexical heads. The models are evaluated on the Penn Wall Street Journal Treebank, showing that their accuracy is competitive with other models in the literature. To gain a better understanding of the models, we also give results on different constituent types, as well as a breakdown of precision/recall results in recovering various types of dependencies. We analyze various characteristics of the models through experiments on parsing accuracy, by collecting frequencies of various structures in the treebank, and through linguistically motivated examples. Finally, we compare the models to others that have been applied to parsing the treebank, aiming to give some explanation of the difference in performance of the various models.

/pdf/head-driven-statistical-models-for-natural-language-parsing-l1eadq1q9u.pdf

Head-Driven Statistical Models for Natural Language Parsing

《Ｏｘｆｏｒｄ Ａｄｖａｎｃｅｄ Ｌｅａｒｎｅｒ’ｓ Ｄｉｃｔｉｏｎａｒｙ ｏｆ Ｃｕｒｒｅｎｔ Ｅｎｇｌｉｓｈ》（ 以下简称ＯＡＬＤ） 是英语学习词典的先驱。它是第一部既重视词语释义，又重视词法、句法，配有大量例证的词典。我国英语学习者对其第二、三、四版（ 英汉双解版） 都十分熟悉。１９９５ 年，ＯＡＬＤ 第五次修订再版，又有了新的特色，但仍有不足

评《Oxford Advanced Learner’s Dictionary of Current English》第五版

IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.

/pdf/building-watson-an-overview-of-the-deepqa-project-215b8hl6in.pdf

Building Watson: An Overview of the DeepQA Project

In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer.This paper presents a machine learning approach to question classification. We learn a hierarchical classifier that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. We show accurate results on a large collection of free-form questions used in TREC 10.

Learning question classifiers

We describe Abstract Meaning Representation (AMR), a semantic representation language in which we are writing down the meanings of thousands of English sentences. We hope that a sembank of simple, whole-sentence semantic structures will spur new work in statistical natural language understanding and generation, like the Penn Treebank encouraged work on statistical parsing. This paper gives an overview of AMR and tools associated with it.

/pdf/abstract-meaning-representation-for-sembanking-2tb177kukk.pdf

Abstract Meaning Representation for Sembanking

SHAPE ADJECTIVE COLOR DISEASE TEXT NARRATIVE* GENERAL-INFO DEFINITION USE EXPRESSION-ORIGIN HISTORY WHY-FAMOUS BIO ANTECEDENT INFLUENCE CONSEQUENT CAUSE-EFFECT METHOD-MEANS CIRCUMSTANCE-MEANS REASON EVALUATION PRO-CON CONTRAST RATING COUNSEL-ADVICE To create the QA Typology, we analyzed 17,384 questions and their answers (downloaded from answers.com); see (Gerber, 2001). The Typology contains 94 nodes, of which 47 are leaf nodes; a section of it appears in Figure 2. Each Typology node has been annotated with examples and typical patterns of expression of both Question and Answer, as indicated in Figure 3 for Proper-Person. Question examples Question templates Who was Johnny Mathis' high school track coach? who be 's Who was Lincoln's Secretary of State? Who was President of Turkmenistan in 1994? who be of Who is the composer of Eugene Onegin? Who is the CEO of General Electric? Actual answers Answer templates Lou Vasquez, track coach of...and Johnny Mathis , of Signed Saparmurad Turkmenbachy [Niyazov], of president of Turkmenistan ...Turkmenistan’s President Saparmurad Niyazov... ’s ...in Tchaikovsky's Eugene Onegin... 's Mr. Jack Welch, GE chairman... ... ...Chairman John Welch said ...GE's | of related role-verb Figure 3. Portion of QA Typology node annotations for Proper-Person.

/pdf/question-answering-in-webclopedia-4jdj4ybjng.pdf

Question Answering in Webclopedia.

We describe the treatment of questions (Question-Answer Typology, question parsing, and results) in the Weblcopedia question answering system.

/pdf/toward-semantics-based-answer-pinpointing-2a3s3d3nfd.pdf

Toward semantics-based answer pinpointing

This paper describes machine learning based parsing and question classification for question answering. We demonstrate that for this type of application, parse trees have to be semantically richer and structurally more oriented towards semantics than what most treebanks offer. We empirically show how question parsing dramatically improves when augmenting a semantically enriched Penn treebank training corpus with an additional question treebank.

https://www.aclweb.org/anthology/W01-1203.pdf

Parsing and question classification for question answering

This paper describes recent development in the Webclopedia QA (Question Answering) system, focusing on the use of knowledge resources such as WordNet and a QA typology to improve the basic operations of candidate answer retrieval, ranking, and answer matching.

/pdf/the-use-of-external-knowledge-in-factoid-qa-47juvnna80.pdf

Ulf Hermjakob

Papers

Abstract Meaning Representation for Sembanking

Question Answering in Webclopedia.

Toward semantics-based answer pinpointing

Parsing and question classification for question answering

The use of external knowledge in factoid QA