Category Archives: LUG

What do we mean by “I mean”?

Click image to jump to Fernández-Polo, F. J. (2014) The role of I mean in conference presentations by ELF speakers. English for Specific Purposes 34, 58-67. (behind paywall)

Click image to jump to Fernández-Polo, F. J. (2014) The role of I mean in conference presentations by ELF speakers. English for Specific Purposes 34, 58-67. (behind paywall)

When analysing spoken English, it doesn’t take long to encounter discourse markers, the single words or phrases that speakers commonly use to mark their stance or organise their message. Common discourse markers include well, now, you know and i mean. In the April 2014 issue of English for Specific Purposes, Francisco Javier Fernández-Polo examines the discourse marker i mean in conference presentations included in the ELFA corpus. This subcorpus includes 34 conference presentations in English by speakers of 21 different first languages. Recorded at universities in Finland, the data consist of naturally occurring English used as a lingua franca (ELF) in academic settings.

Fernández-Polo’s study is qualitative, involving a close analysis of a small number of cases toward determining the functions of i mean in context. There are only 56 occurrences of i mean in this conference presentation subcorpus (94,314 words1), and Fernández-Polo takes 48 of them into his analysis. He classifies these into four different categories – correcting mistakes and dysfluencies; enhancing clarity and explicitness; organising text; and marking certainty and salience (see Table 1 below). Examples of each are discussed in turn.

A striking finding from the paper concerns the wide inter-speaker variation in the use of i mean. Fewer than half of the 34 presenters use i mean at least once, with a single speaker producing 20% of the occurrences, and five speakers contributing two thirds of all hits. To see if a different distribution might be found in similar English as a native language (ENL) data, Fernández-Polo consulted the monologic lectures in the American MICASE corpus. He found that i mean occurs in the MICASE lectures with the same standardised frequency (5 per 10,000 words) and with similar inter-speaker variation – one speaker in MICASE produced 27% of occurrences, with 14 speakers producing 60% of hits. It thus appears that the choice of discourse markers varies a lot based on a speaker’s preference or habit. Keep reading…


On the other side: variations in organising chunks in ELF

Variations in organising chunks aren't that common, but they do tend to stand out.Source: Livio Bourbon via The Telegraph

Variations in organising chunks aren’t that common, but they do tend to stand out.
Source: Livio Bourbon via The Telegraph

When working with ELF data – English used as a lingua franca between second/foreign-language speakers – one of the things that stands out are slight variations in conventional chunks of language. A formulaic chunk like as a matter of fact might be realised as as the matter of fact, or you could hear now that you mention it spoken as now that you say it. There’s no sense in calling them errors, since the variants won’t cause miscommunication, they resemble their conventional counterparts in both function and form, and the less-preferred variant is likely found elsewhere. It’s just not the English native-speaker preference.

These variations are interesting linguistically and they tend to stand out impressionistically for researchers, but I’ve wondered how often these variations actually occur in ELF – both in frequency and also in their distribution relative to conventional forms. It’s not an easy question to answer. Many of these formulaic chunks of language occur infrequently, so finding a couple variants doesn’t really tell you much. The example above of now that you say it occurs twice in the million-word ELFA corpus, with just one instance of the conventional form. Alternatively, as the matter of fact is found in ELFA 21 times compared to just eight occurrences of the expected chunk, but only two speakers account for those 21 instances.

We can see from these examples that a formulaic chunk that rarely shows up won’t reveal much about how often variation occurs among ELF users, across speech events, in different times and places. To find out more, I wanted to start with the highest frequency chunks I could find. These are described by Linear Unit Grammar as organising chunks, the recurring and relatively fixed chunks we use to structure our speech and writing, like on the other hand. Using the corpus freeware AntConc, I looked at the most frequent 3-, 4- and 5-word clusters (aka n-grams) in the ELFA corpus of spoken academic ELF. Keep reading…

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And so on, or something like that: vague expressions in academic ELF

Another lakeside view of our heavenly Finnish summer.© Nina Valtavirta

Another lakeside view of our heavenly Finnish summer.
© Nina Valtavirta

An important part of academic argumentation is not what you say, but how you say it. It’s one thing to make a bold claim, and another to “soften” it by adding expressions like or something like that, more or less, or in a way. These recurring chunks aren’t merely filler – they convey important interactive information. Vague expressions, or VEs, “express the speaker’s uncertainty or personal attitude towards the proposition and indicate for example solidarity” (Metsä-Ketelä 2012: 264).

Earlier research has expressed concern about non-native speakers’ learning and use of vague expressions, with the danger of sounding “blunt” or “pedantic” if these VEs are underused. In a recent paper by ELFA project member Maria Metsä-Ketelä, these concerns were investigated in the ELFA corpus of spoken academic ELF (English as a lingua franca). How are these vague chunks employed by second-language users in interaction with each other, and how do these findings compare to similar native-speaker data?

OI chunks: organising interaction

You’ll notice that vague expressions like and so on and in a sense function as units – they’re fixed chunks of language that typically don’t vary in form. From a Linear Unit Grammar (LUG) point of view, these are OI chunks (Organising Interaction) which can be used by a speaker to qualify her stance on the main content of an utterance. As Maria points out, the vague expressions in her study serve to intentionally add imprecision. They also have two other important traits:

  • VEs do not contribute to the propositional content of an utterance, or the message itself (the M chunks in LUG)
  • VEs “are supplementary, that is, they could be omitted from the utterance without compromising its syntactic structure” (Metsä-Ketelä 2012: 265).

Keep reading…

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Fluent chunks 2: How to label your chunks

Photo by Alan Chia via Wikimedia Commons

Photo by Alan Chia via Wikimedia Commons

Most people recognise that we don’t speak in “sentences”. Still, speech is analysed and described using the concepts of sentence grammars, even when these writing-based systems must be bent and stretched, or vice versa – isn’t it cheating to “clean up” naturally occurring speech so it fits into a sentence grammar?

In a previous post I introduced Linear Unit Grammar, or LUG, a chunk-based approach to analysing spoken and written text. In that post I introduced the linear, word-by-word process of chunking up a string of transcribed speech by placing intuitively directed chunk boundaries. The discussion focused on this short extract from an academic conference in the ELFA corpus. When asked about her experience with students in Brazil, the speaker responded:

er i c- i i so i i went to portugal er i live in portugal er for 13 years so i er my experience with brazilian students is is a long way @@ okay a long time ago (note: @@ = laughter and er is like uh in the US style)

How do you divide this into a well-formed constituency tree? The short answer is you don’t, and neither do speakers in actual interaction. LUG analysis attempts to mirror the real-time, linear processing of language as multi-word chunks, regardless of “grammaticality”. Keep reading…

Fluent chunks: an intro to Linear Unit Grammar

The spring equinox has arrived, the sun is shining, and the place is still frozen solid.© Nina Valtavirta

The spring equinox has passed, the sun is shining, and the place is still frozen solid.
© Nina Valtavirta

The question of how to evaluate English proficiency in lingua franca settings such as English-medium university programs has interested me for a while. One of the criticisms heard against ELF research is that it promotes an “anything goes” attitude toward English. But clearly anything does not go – at least not in high-stakes, professional contexts like academia. Yet, it doesn’t make a huge amount of sense to bring in the British Council to evaluate the non-British English used by non-British instructors to teach non-British students outside Britain. The need for contextually appropriate teaching and testing is one of the main motivations for ELF research.

It was my turn to talk at the ELF seminar this month, which was held on 14.3. I introduced my PhD project, which officially started this January and unofficially began over a year ago. I’m researching fluency in spoken academic ELF, but with a data-driven approach; instead of evaluating ELF users by an idealised “native-speaker model”, I’m starting by describing the features of fluency and dysfluency in a corpus of naturally occurring academic ELF. These texts are transcriptions of university level talk from pedagogical settings (lectures, seminar discussions from various fields) and professional events (conference presentations and discussions).

The idea is to first describe what is ordinary ELF in academic settings – what are the recurring patterns and routines of fluent interaction, and what are the (dys)fluency features which differentiate individual ELF users? This is the big question, but the even bigger problem is how to systematically identify and describe these patterns.

Keep reading…