Vyvyan Evans: "Semantic representation in LCCM Theory" (2009)

This is a relatively sketchy paper, published in New Directions in Cognitive Linguistics. Its main point is that we should see linguistic representation (discrete grammatical categories) as a crude, schematic system put on top of an ancient system for conceptual representation (qualitative sensibilities).

Various aspects of sentence and word meaning are put in one box or the other depending on whether the aspect is discrete or not. It is thus near-tautological that the "linguistic system" deals with crude, discrete categories, while everything else is relinquished to the dust bin category of fuzzy "conceptual content."

The psychological or "conceptual" part of the story is inspired by Lawrence Barsalou's ideas about mental representations.

Peter Harder: "Meaning as input" (2009)

This paper by Peter Harder argues---building on William Croft and D. Alan Cruse's Cognitive Linguistics (2004)---that sentences should be thought of as little programs that are run in the head of a hearer.

The main example supporting this view of semantics is the case of the definite article. In sentences like

• The UN condemned the test,

the noun phrase the test can be read as an instruction to find a suitable referent (p. 18).

This invites a view of grammar in which the syntactical structure of a sentence is seen as a chronological structure of a program. The sentence The test happened thus receives the following interpretation (p. 23):

• declarative (past tense (HAPPEN (definite (TEST))))

This should be read as a LISP-style program. Harder illustrates the syntax of this instructional language with the following "recipe" (p. 24):

• serve (sprinkle with lemon (grill (add salt (slice (salmon)))))

It should be quite obvious how this is intended to work. Note the parallel to Montague grammar.

The consequence of adopting such a view seems to be that language becomes a narrow communication channel that only allows for the transmission of code, not of meaning. If this is correct, understanding can be compared to a client-side execution of a server-provided script.

Meaning is thus constrained by the resources of the hearer, but the instruction is designed by the speaker. This may be what Harder means by the following quote, although I'm not entirely sure:

Knowing a language, I suggest, essentially consists of knowing the input properties of the forms you choose [the meaning of the keywords in the scripting language?] -- whereas actual outputs [interpretations, meanings?] can never be known for certain in advance [because you cannot control the settings and properties of the client-side computer?]. (p. 16)

The paper was published in New Directions in Cognitive Linguistics (2009), edited by Vyvyan Evans and Stéphanie Pourcel.

Ortony, Vondruska, Foss, and Jones: "Salience, Similies, and the Asymmetry of Similarity" (1985)

A paper from Journal of Memory and Language 24(5) most notable for its long list of good examples of similies (reproduced in the appendix).

Medin and Ortony: "Psychological essentialism" (1989)

This is a contribution by Douglas Medin and Andrew Ortony to the volume Similarity and Analogical Reasoning (1989) edited by Stella Vosniadou and Ortony. Medin and Ortony argue that entities have two distinct sets of features, a shallow and a deep set, that influence similarity judgments in different contexts.

Superficial Attributes and "Essence Slots"
The paper is called "Psychological essentialism" because Medin and Ortony thinks that the context-dependence of similarity judgment can be explained by supposing that ordinary lay folk are metaphysical essentialists. What this means is that the average test subject believes that entities have deep, perhaps unknown, properties in addition to their superficial attributes.

This is supposed to explain why we categorize dolphins with bats and not with sharks. In other cases, like an airplane, a subject's representation of this "essence" make take the form of a theory like "I don't know, but an experts could tell me."

There is something interesting and original about taking a bad philosophical theory and trying to explain it as a psychological phenomenon. However, think their theory hides as much as it shows, as it takes similarity judgment to be divorced from action. I think most of the paradoxical features of similarity judgments (as described by Lawrence Barsalou) would evaporate if we took "being similar" as explained by "treated similarly" rather than the other way around.

A Note on Gender and Style
When I was reading the paper, I was noticing that Medin and Ortony tend to refer to Linda E. Smith by both her first and last name, while they refer to male authors by their last name only. In order to check whether this was actually true, I counted how many times people were mentioned in the text, and whether their first names were mentioned:

Person	+ first name	– first name
Linda B. Smith	4	2
Lawrence W. Barsalou	1	9
Lance J. Rips	1	12
Ryszard Michalski	2	3
Edward E. Smith	2	8
Daniel N. Osherson	2	8
Ludwig Wittgenstein	0	2
John Locke	0	4

The numbers in the table show how often the respective authors occur with their first name spelled out completely. Last names mentioned in references such as "Smith and Medin (1981)" are not counted, since their form is dictated by more rigid style guides. I have not counted names that only occur once (which exlude Elanor Rosch, who is mentioned with first name).

Note that Linda B. Smith is the only woman referred to in the paper. The fact that her first name is mentioned more frequently than, say, Michalski's, may be attributed to the fact that there is another "Smith" frequently referenced in the paper, but note that he is more frequently referred to by his last name only. However, this may partly be due to the fact that his name is disambiguated by the fact that it occurs next to Osherson's as well as more often. A recent mention of a figure does to some extent make the first name obsolete.

Nevertheless, the numbers are quite striking. It would be interesting to do a more thorough investigation of this phenomenon. It would perhaps also be more interesting to investigate whether there is a significant difference in the distance from last mention that warrants reiterating a first name for men and women, respectively.

Srinivas Sankara Narayanan: "KARMA," an addition

I forgot to include this quote in the previous post, but it strikes me as illuminating:

[...] our focus on narrow set of expressions, our impoverished target domain and the inability of our system to extend existing metaphors are important shortcomings. (p. 217)

Srinivas Sankara Narayanan: "KARMA" (1997)

Srini Narayanan's PhD dissertation is often cited as an exemplary accomplishment of cognitive metaphor theory. However, when you read the small print, it seems less convincing. It has the distinct feel of the Georgetown-IBM system from 1953.

Since I have now made my way through the 300-page dissertation, I'll now do my best to give a clear and short presentation of its results. I'll do that in reverse order, starting with its evaluation methodology and moving towards a more detailed description of the program.

Evaluation
Narayanan's program was evaluated on a "corpus" consisting of 9 short snippets of text amounting to a total of 116 words. This evaluation set was, as far as I can understand, specifically constructed so as to look as much like the training set as possible in terms of vocabulary and theme.

For each sentence to be evaluated, the methodology consisted in the following steps:

1. For each sentence, a feature structure was constructed; thus, the sentence was parsed and partially interpreted by hand before entering the reasoning module.
2. Any "difficult" aspects of the sentences were smoothed out; e.g. the phrase perception of was removed from one of the examples to create a flatter semantics (p. 246).
3. Any knowledge that the system lacked in order to understand the sentence was fed into it; e.g., in one case it was given the information that NAFTA affects Mexico (p. 248).
4. The subject matter was, as far as I understand, always explicitly stated to be economic policy so that the system did not have to infer whether it should read motion verbs metaphorically or not.
5. A feature structure was produced by the system, and Narayanan read the contents of this structure and determined whether he thought it had fit his intuitions (as he mostly did).

It should be quite clear that this evaluation procedure is quite liberal. The line between system design and system evaluation is effectively invisible, as is the line between subject and experimenter.

Input knowledge
The system was equipped with a number of materials which were designed explicitly to deal with a training "corpus" of 20 sentences (roughly 750 words). These were:

• A background model of the domain of economic policies with some very strong and very simplified assumptions (pp. 173-75).
• A set of motion models or "execution schemas" that describe the temporal progression of actions like walking and recovering from illness in a highly idealized fashion.
• A set of highly specific maps that told the system how to link various objects, attributes, and events from the execution schemas to the economic objects, attributes, and events. These were often very specific and tailored to the corpus. For instance, there were mappings telling the system to an IncompetentDoctor object onto a Government object, and a step event onto an implement_policy event.

This background knowledge would obviously not be very robust if applied more widely.

The X-schema model
The target domain in Narayanan's thesis, economic policies, is represented fundamentally different from the source domains, movement and health. The target domain is represented as a Bayesian net in order to allow probabilistic reasoning. The source domain is represented as a kind of highly idiosyncratic simulation tool.

These simulation tools are what he calls "x-schemas" or "execution schemas." They are abstract representations of the causal structure of processes like walking or grasping something.

The x-schemas are connceted to a feature structure that keeps track of the state of whoever the agent performing the action is. The agent's state may influence the structure of the process and vice versa. The x-schemas are mostly structured like a little story with a beginning, a middle, and an end, and they are run as simulations when the system processes a sentence.

This simulation is a discrete-step process where the value of various parameters at one time (step size, energy level, distance to goal, etc.) combine to produce the parameter settings in the next time step. All parameter values are non-negative integers. The mechanism that produces the state at time t + 1 from the state at time t is designed by hand (and by intuition).

Examples
Narayanan gives a single completely explicit example of the kind of data structures that the system inputs and outputs. His example builds on the newspaper headline Indian Government Stumbling in Implementing Liberalization Plan.

He manually translates this sentences into the following feature structure representation (p. 208):

( (context Economic Policy)

  (type Liberalization)

  (actor IG)

  (event Stumble)
  (aspect Progressive)

  (ut-type Description) )

Fed this data structure, the programs runs a simulation of a walking event and maps the results to the domain of economic policies using maps like ACTOR IS MOVER. The output is the following (p. 215):

( (context Economic Policy)

  (type Liberalization)

  (actor IG)
  (event Stumble)

  (aspect Progressive)

  (status (ongoing 0 1)
  (suspend 1 1))

  (difficulty

    (t 0 .7)

    (t 1 1))

  (outcome (fail 1 .6))
  (goal

    (free-trade 0 1)

    (dereg 0 1)

    (free-trade 1 1)

    (dereg 1 1)))

It is not entirely clear to me what the first argument means in constructions like (dereg 0 1), and the text doesn't seem to help me out.

Narayanan has previously (pp. 55-58) gone through an example simulation of the WALK schema with a set of explicit values on various parameters such as Dist(walked) and Energy. I will not reproduce the tables here, but the schema can roughly be compared to an loop working according to the following rules:

• If walking is enabled, get ready
• If you're ready, check if you can see
• If you can see, take a step
• If you can't see, check your footing
• If your footing is OK, take a step
• If your footing is not OK, change position
• If you've just taken a step and you're not a your destination, get ready

I should be quite obvious that this is a highly idealized model. Whoever, its experiential implausibility does not constitute a problem in Narayanan's dissertation, probably because of the small size of the validation materials.

The Murphy/Gibbs debate

Spread out over three issues of Cognition in 1996 and 1997, Greg Murphy and Ray Gibbs had a very interesting exchange of opinions about cognitive metaphor theory (Murphy 1996, Gibbs 1996, Murphy 1997).

Murphy argued that a prior-similarity-based account of meaning extension is leaner and more precise than cognitive metaphor theory. Gibbs disagreed.

The Strong View and the Weak View
Murphy (1996) makes a distinction between what he calls the "strong" and the "weak" version of cognitive metaphor theory.

The strong view is that target domains have no inherent structure, and so get all their meaning through metaphors. The weak view is that they do have some "skeleton" of structure (1996: 187), perhaps consisting of something like thematic roles and unspecified relations between actors.

Murphy dismisses the strong view on two accounts.

First, all the false inferences predicted by a general metaphor need to be blocked. This can essentially only be done if the target domain has an autonomous structure, and if there is an authority higher than metaphor that can choose to apply or not apply the metaphors.

Second, suppose we have a chain of conceptualization such as

ARGUMENT <<< WAR <<< A WILD ANIMAL <<< A PERSON <<< A PLANT

There are then four links in which noise, wrong inferences, and memory problems may slip in. This seems computationally and psychologically unfortunate.

"Problems of circularity of evidence"
Murphy also notes that one cannot expect the same pool of data to be both explainer and explained. Non-linguistic evidence is needed (1996: 183).

He notes that some of Boaz Keysar and Bridget Bly's (1995) evidence also seems to suggest that metaphor understanding is a post-hoc, backward-looking construction.

That seems to be how our intuitions work. But Murphy warns us that we should not assume that the products of these intuitions reflect anything about the actual mental process (1996: 184).

"Problem of multiple metaphors"
Murphy goes on to note that some super-metaphorical arbiter must choose between metaphors when we have multiple inconsistent options.

As an example, he mentions LOVE, which is conceptualized as a JOURNEY, an OPPONENT, a UNITY, a HIDDEN OBJECT, a VALUABLE COMMODITY, and INSANITY. These various source domains seem to license different inferences.

In his response to Murphy's article, Gibbs rebuts that "concepts are temporary, independent constructions in working memory created on the spot" (Gibbs 1996: 313).

This is in direct conflict with the claim that the conceptual system exists "in long-term memory" which appears elsewhere in the literature (Lakoff 1998: 51; Lakoff and Johnson 1999: 69).

What counts as literal?
According to Murphy, when Lakoff and Johnson state that physical meanings are the only literal meanings, this is

their theory of concepts applied to language; namely, it says that only simple physical experiences can be directly encoded in linguistic meaning, and nonphysical or abstract relations must be expressed via metaphor. Thus, their claim that Inflation is rising is metaphoric is basically an assumption of their theory, rather than evidence for it. (1996; p. 189)

He concludes that "a number of the 'metaphors' that L&J and others identify may well not metaphors at all" (p. 190). He doesn't seem to have a clear account of how one could test "metaphoricity" himself, though.