Shalom Lappin | King's College London (original) (raw)

Papers by Shalom Lappin

Transactions of the Association for Computational Linguistics

We study the influence of context on sentence acceptability. First we compare the acceptability r... more We study the influence of context on sentence acceptability. First we compare the acceptability ratings of sentences judged in isolation, with a relevant context, and with an irrelevant context. Our results show that context induces a cognitive load for humans, which compresses the distribution of ratings. Moreover, in relevant contexts we observe a discourse coherence effect that uniformly raises acceptability. Next, we test unidirectional and bidirectional language models in their ability to predict acceptability ratings. The bidirectional models show very promising results, with the best model achieving a new state-of-the-art for unsupervised acceptability prediction. The two sets of experiments provide insights into the cognitive aspects of sentence processing and central issues in the computational modeling of text and discourse.

Linguistic Nativism and the Poverty of the Stimulus

The capacity to recognise and interpret sluices|bare wh-phrases that ex- hibit a sentential meani... more The capacity to recognise and interpret sluices|bare wh-phrases that ex- hibit a sentential meaning|is essential to maintaining cohesive interaction between human users and a machine interlocutor in a dialogue system. In this paper we present a machine learning approach to sluice disambigu- ation in dialogue. Our experiments, based on solid theoretical consider- ations, show that applying machine learning techniques using

Philosophical Studies, 1976

Natural Language and Linguistic Theory, 2000

Computational Linguistics, Dec 1, 2001

Anaphora accounts for cohesion in texts and is a phenomenon under active study in formal and comp... more Anaphora accounts for cohesion in texts and is a phenomenon under active study in formal and computational linguistics alike. The correct interpretation of anaphora is vital for natural language processing (NLP). For example, anaphora resolution is a key task in natural language interfaces, machine translation, text summarization, information extraction, question answering, and a number of other NLP applications. After considerable initial research, followed by years of relative silence in the early 1980s, anaphora resolution has attracted the attention of many researchers in the last 10 years and a great deal of successful work on the topic has been carried out. Discourseoriented theories and formalisms such as Discourse Representation Theory and Centering Theory inspired new research on the computational treatment of anaphora. The drive toward corpus-based robust NLP solutions further stimulated interest in alternative and/or data-enriched approaches. Last, but not least, application-driven research in areas such as automatic abstracting and information extraction independently highlighted the importance of anaphora and coreference resolution, boosting research in this area. Much of the earlier work in anaphora resolution heavily exploited domain and linguistic knowledge (Sidner 1979; Carter 1987; Rich and LuperFoy 1988; Carbonell and Brown 1988), which was difficult both to represent and to process, and which required considerable human input. However, the pressing need for the development of robust and inexpensive solutions to meet the demands of practical NLP systems encouraged many researchers to move away from extensive domain and linguistic knowledge and to embark instead upon knowledge-poor anaphora resolution strategies. A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments (

Linguistics and Philosophy, 1997

In the past several years Chomsky has published a series of papers in which he proposes what he t... more In the past several years Chomsky has published a series of papers in which he proposes what he terms a minimalist view of syntax. 2 The model of grammar that emerges from this and related work represents a significant departure from the Government Binding (GB) theory of syntax, which dominated the Principles and Parameters (P&P) approach throughout the 1980's. 3 On the GB view, the syntactic well-formedness of a sentence depends upon the satisfaction of constraints that apply at one or more levels of representation in a derivation. The standard GB account recognizes a sequence of three syntactic levels of 1 Much of the research for this paper was done during the summer of 1995, when the alphabetically second author visited the first as a guest academic researcher in the

Foundations of Intensional Semantics, 2000

We present Property Theory with Curry Typing (PTCT), an intensional first-order logic for natural... more We present Property Theory with Curry Typing (PTCT), an intensional first-order logic for natural language semantics. PTCT permits fine-grained specifications of meaning. It also supports polymorphic types and separation types. 1 We develop an intensional number theory within PTCT in order to represent proportional generalized quantifiers like most. We use the type system and our treatment of generalized quantifiers in natural language to construct a typetheoretic approach to pronominal anaphora that avoids some of the difficulties that undermine previous type-theoretic analyses of this phenomenon. 7.1 PTCT: Syntax of the basic theory The core language of PTCT consists of the following sub-languages:

Natural Language and Linguistic Theory

The tension between expressive power and computational tractability poses an acute problem for th... more The tension between expressive power and computational tractability poses an acute problem for theories of underspecified semantic representation. In previous work we have presented an account of underspecified scope representations within Prop-erty Theory with Curry Typing (PTCT), an intensional first-order theory for natural language semantics. Here we show how filters applied to the underspecified-scope terms of PTCT permit both expressive completeness and the reduction of computa-tional complexity in a significant class of non-worst case scenarios.