Are embedding decisions independent?

Evidence from preposition(al) phrases

Abstract Full Paper (PDF)

One of the more difficult challenges in linguistics research concerns detecting how constraints might apply to the process of constructing phrases and clauses in natural language production. In previous work (Wallis 2019) we considered a number of operations modifying noun phrases, including sequential and embedded modification with postmodifying clauses. Notably, we found a pattern of a declining additive probability for each decision to embed postmodifying clauses, albeit a pattern that differed in speech and writing.

In this paper we use the same research paradigm to investigate the embedding of an altogether simpler structure: postmodifying nouns with prepositional phrases. These are approximately twice as frequent and structures exhibit as many as five levels of embedding in ICE-GB (two more than are found for clauses). Finally the embedding model is simplified because only one noun phrase can be found within each prepositional phrase. We discover different initial rates and patterns for common and proper nouns, and certain subsets of pronouns and numerals. Common nouns (80% of nouns in the corpus) do appear to generate a secular decline in the additive probability of embedded prepositional phrases, whereas the equivalent rate for proper nouns rises from a low initial probability, a fact that appears to be strongly affected by the presence of titles.

It may be generally assumed that like clauses, prepositional phrases are essentially independent units. However, we find evidence from a number of sources that indicate that some double-layered constructions may be being added as single units. In addition to titles, these constructions include schematic or idiomatic expressions whose head is an ‘indefinite’ pronoun or numeral.

Why Chomsky was Wrong About Corpus Linguistics

Introduction

When the entire premise of your methodology is publicly challenged by one of the most pre-eminent figures in an overarching discipline, it seems wise to have a defence. Noam Chomsky’s famous objection to corpus linguistics therefore needs a serious response.

“One of the big insights of the scientific revolution, of modern science, at least since the seventeenth century… is that arrangement of data isn’t going to get you anywhere. You have to ask probing questions of nature. That’s what is called experimentation, and then you may get some answers that mean something. Otherwise you just get junk.” (Noam Chomsky, quoted in Aarts 2001).

Chomsky has consistently argued that the systematic ex post facto analysis of natural language sentence data is incapable of taking theoretical linguistics forward. In other words, corpus linguistics is a waste of time, because it is capable of focusing only on external phenomena of language – what Chomsky has at various times described as ‘e-language’.

Instead we should concentrate our efforts on developing new theoretical explanations for the internal language within the mind (‘i-language’). Over the years the terminology varied, but the argument has remained the same: real linguistics is the study of i-language, not e-language. Corpus linguistics studies e-language. Ergo, it is a waste of time.

Argument 1: in science, data requires theory

Chomsky refers to what he calls ‘the Galilean Style’ to make his case. This is the argument that it is necessary to engage in theoretical abstractions in order to analyse complex data. “[P]hysicists ‘give a higher degree of reality’ to the mathematical models of the universe that they construct than to ‘the ordinary world of sensation’” (Chomsky, 2002: 98). We need a theory in order to make sense of data, as so-called ‘unfiltered’ data is open to an infinite number of possible interpretations.

In the Aristotelian model of the universe the sun orbited the earth. The same data, reframed by the Copernican model, was explained by the rotation of the earth. However, the Copernican model of the universe was not arrived at by theoretical generalisation alone, but by a combination of theory and observation.

Chomsky’s first argument contains a kernel of truth. The following statement is taken for granted across all scientific disciplines: you need theory to analyse data. To put it another way, there is no such thing as an ‘assumption free’ science. But the second part of this argument, that the necessity of theory permits scientists to dispense with engagement with data (or even allows them to dismiss data wholesale), is not a characterisation of the scientific method that modern scientists would recognise. Indeed, Beheme (2016) argues that this method is also a mischaracterisation of Galileo’s method. Galileo’s particular fame, and his persecution, came from one source: the observations he made through his telescope.

What might a corpus of parsed spoken data tell us about language?

Abstract Paper (PDF)

This paper summarises a methodological perspective towards corpus linguistics that is both unifying and critical. It emphasises that the processes involved in annotating corpora and carrying out research with corpora are fundamentally cyclic, i.e. involving both bottom-up and top-down processes. Knowledge is necessarily partial and refutable.

This perspective unifies ‘corpus-driven’ and ‘theory-driven’ research as two aspects of a research cycle. We identify three distinct but linked cyclical processes: annotation, abstraction and analysis. These cycles exist at different levels and perform distinct tasks, but are linked together such that the output of one feeds the input of the next.

This subdivision of research activity into integrated cycles is particularly important in the case of working with spoken data. The act of transcription is itself an annotation, and decisions to structurally identify distinct sentences are best understood as integral with parsing. Spoken data should be preferred in linguistic research, but current corpora are dominated by large amounts of written text. We point out that this is not a necessary aspect of corpus linguistics and introduce two parsed corpora containing spoken transcriptions.

We identify three types of evidence that can be obtained from a corpus: factual, frequency and interaction evidence, representing distinct logical statements about data. Each may exist at any level of the 3A hierarchy. Moreover, enriching the annotation of a corpus allows evidence to be drawn based on those richer annotations. We demonstrate this by discussing the parsing of a corpus of spoken language data and two recent pieces of research that illustrate this perspective.