人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

What is modal logic

Minimal recursion semantics (MRS) is a framework for computational semantics that is suitable for parsing and generation and that can be implemented in typed feature structure formalisms. We discuss why, in general, a semantic representation with minimal structure is desirable and illustrate how a descriptively adequate representation with a nonrecursive structure may be achieved. MRS enables a simple formulation of the grammatical constraints on lexical and phrasal semantics, including the principles of semantic composition. We have integrated MRS with a broad-coverage HPSG grammar.

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Minimal Recursion Semantics An Introduction

This article proposes a novel framework for representing and measuring local coherence. Central to this approach is the entity-grid representation of discourse, which captures patterns of entity distribution in a text. The algorithm introduced in the article automatically abstracts a text into a set of entity transition sequences and records distributional, syntactic, and referential information about discourse entities. We re-conceptualize coherence assessment as a learning task and show that our entity-based representation is well-suited for ranking-based generation and text classification tasks. Using the proposed representation, we achieve good performance on text ordering, summary coherence evaluation, and readability assessment.

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Modeling local coherence: An entity-based approach

A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.

Automated Planning: Theory and Practice.

The success of the large neural language models on many NLP tasks is exciting. However, we find that these successes sometimes lead to hype in which these models are being described as ``understanding'' language or capturing ``meaning''. In this position paper, we argue that a system trained only on form has a priori no way to learn meaning. In keeping with the ACL 2020 theme of ``Taking Stock of Where We've Been and Where We're Going'', we argue that a clear understanding of the distinction between form and meaning will help guide the field towards better science around natural language understanding.

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Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data.

We describe a novel approach to unsupervised learning of the events that make up a script, along with constraints on their temporal ordering. We collect natural-language descriptions of script-specific event sequences from volunteers over the Internet. Then we compute a graph representation of the script's temporal structure using a multiple sequence alignment algorithm. The evaluation of our system shows that we outperform two informed baselines.

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Learning Script Knowledge with Web Experiments

This paper presents the Constraint Language for Lambda Structures (CLLS), a first-order language for semantic underspecification that conservatively extends dominance constraints. It is interpreted over lambda structures, tree-like structures that encode λ-terms. Based on CLLS, we present an underspecified, uniform analysis of scope, ellipsis, anaphora, and their interactions. CLLS solves a variable capturing problem that is omnipresent in scope underspecification and can be processed efficiently.

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The Constraint Language for Lambda Structures

We describe the second installment of the Challenge on Generating Instructions in Virtual Environments (GIVE-2), a shared task for the NLG community which took place in 2009--10. We evaluated seven NLG systems by connecting them to 1825 users over the Internet, and report the results of this evaluation in terms of objective and subjective measures.

Report on the second NLG challenge on generating instructions in virtual environments (GIVE-2)

Natural-Language Generation from flat semantics is an NP-complete problem. This makes it necessary to develop algorithms that run with reasonable efficiency in practice despite the high worst-case complexity. We show how to convert TAG generation problems into dependency parsing problems, which is useful because optimizations in recent dependency parsers based on constraint programming tackle exactly the combinatorics that make generation hard. Indeed, initial experiments display promising runtimes.

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Alexander Koller

Papers

Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data.

Learning Script Knowledge with Web Experiments

The Constraint Language for Lambda Structures

Report on the second NLG challenge on generating instructions in virtual environments (GIVE-2)

Generation as Dependency Parsing