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.

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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.

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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 present a method for generating English sentences from Abstract Meaning Representation (AMR) graphs, exploiting a parallel corpus of AMRs and English sentences. We treat AMR-to-English generation as phrase-based machine translation (PBMT). We introduce a method that learns to linearize tokens of AMR graphs into an English-like order. Our linearization reduces the amount of distortion in PBMT and increases generation quality. We report a Bleu score of 26.8 on the standard AMR/English test set.

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Generating English from Abstract Meaning Representations.

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.

Learning Parse and Translation Decisions From Examples With Rich Context

We describe and evaluate how a generalized natural language based reformulation resource in our TextMap question answering system improves web exploitation and answer pinpointing The reformulation resource which can be viewed as a clausal extension of WordNet supports high precision syntactic and semantic reformulations of questions and other sentences as well as inferencing and answer generation The paper shows in some detail how these reformulations can be used to overcome challenges and bene t from the advantages of using the Web

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Natural Language Based Reformulation Resource and Wide Exploitation for Question Answering.

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.

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Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation

Current corpus-based machine translation techniques do not work very well when given scarce linguistic resources. To examine the gap between human and machine translators, we created an experiment in which human beings were asked to translate an unknown language into English on the sole basis of a very small bilingual text. Participants performed quite well, and debrieflngs revealed a number of valuable strategies. We discuss these strategies and apply some of them to a statistical translation system.

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Ulf Hermjakob

Papers

Generating English from Abstract Meaning Representations.

Learning Parse and Translation Decisions From Examples With Rich Context

Natural Language Based Reformulation Resource and Wide Exploitation for Question Answering.

Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation

Translating with Scarce Resources