Abstract
The main claim of this chapter and the next is that all psychologically plausible parsing models either represent or embody a grammar. I substantiate this claim by surveying top-down, bottom-up, and left-corner parsing algorithms, illustrating the ways in which they can draw on explicit representations of grammatical principles. I then discuss the Parsing as Deduction approach, wherein a proof procedure takes the rules of a grammar as axioms and derives MPMs as theorems, using a subpersonal analogue of natural deduction. This constitutes the most concrete implementation of the idea that the HSPM draws on syntactic principles as data. Finally, I turn to three strategies for dealing with the massive structural ambiguity that any parser will encounter in the input stream. Resource-based approaches emphasize parsing heuristics that minimize the use of computational resources, like short-term memory. Frequency-based approaches use statistical analyses of corpuses and treebanks to guide parsing decisions. Grammar-based approaches appeal directly to Minimalist syntactic principles in accounting for the HSPM’s behavior in the face of ambiguity. The latter possibility is particularly exciting, as it would show that a Minimalist grammar is not only suitable for describing abstract formal relations, but also the real-time operation of psychological mechanisms.
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Notes
- 1.
Devitt (2006a) characterizes the notion of a structure rule as follows: “The outputs of a linguistic competence … are governed by a system of rules, just like the outputs of the chess player, the logic machine, and the bee. Something counts as a sentence only if it has a place in the linguistic structure defined by these structure rules. Something counts as a particular sentence, has its particular syntactic structure, in virtue of the particular structure rules that govern it, in virtue of its particular place in the linguistic structure. Like the theory of the idealized outputs of the chess player, logic machine, and bee, our theory can be used to make distinctions among the nonideal. Strings that are not sentences can differ in their degree of failure. For they can differ in the sort and number of linguistic structure rules that they fail to satisfy” (p. 24).
- 2.
Steedman (2000) actually makes a stronger commitment: “It is important to note that the strong competence hypothesis as stated by Bresnan and Kaplan imposes no further constraint on the processor. In particular, it does not limit the structures built by the processor to fully instantiated constituents. However, the Strict Competence Hypothesis proposed in this book imposes this stronger condition” (228).
- 3.
Constraints of space preclude a fuller discussion of a number of formalisms that are currently popular in parsing theory. These include the lexicalist grammars that rely on feature unification—e.g., Lexical Functional Grammar (Bresnan 2001) and Head-driven Phrase Structure Grammar (Pollard and Sag 1994)—as well as Tree-Adjoining Grammar (Schabes et al. 1988) and Combinatory Categorial Grammars (Steedman 2000). Throughout the discussion, I will occasionally mention these, but I reserve a detailed treatment for future work. The philosophical conclusions pertaining to the psychological reality issue are not affected by this omission.
- 4.
This claim pertains to the implementations of such algorithms in conventional computers. How such algorithms are implemented in the human brain, if indeed they are, is a separate question. As noted above, it may well be that the brain embodies the rules, without explicitly representing them.
- 5.
We can trade in the awkward subjunctive locutions for ordinary material conditionals, so long as we keep firmly in mind that the latter are lawlike, in the sense of Goodman (1954/1983). See Chaps. 1 and 2 for a discussion of why this matters for the ontology of linguistic theory.
- 6.
See Kartunnen and Zwicky (1985: pp. 3–5) for a discussion of closely related issues.
- 7.
Strictly speaking, in order to secure this result, we would have to include an explicit rule to the effect that nothing else is a sentence of the language. Following convention, I have omitted this in the main text.
- 8.
There is a distinction between “recognizing” a sentence and assigning a syntactic structure to it. A system’s recognizing a string, or “accepting” it, amounts to no more than that system’s issuing a judgment to the effect that the string in question is grammatical, relative to the grammar with which the system is operating. Parsing, by contrast, involves constructing one or more representations of the string’s syntactic structure and, in the case of ambiguity, selecting one of these as the privileged representation—the one that constitutes the system’s “ultimate decision” about how the input should be interpreted. For a discussion of this distinction, see Berwick and Weinberg (1984), p. 252.
- 9.
Fodor et al. (1974) refer to top-down and bottom-up techniques as analysis-by-analysis and analysis-by-synthesis, respectively. Computer scientists sometimes use the terms recursive-descent and shift-reduce, for reasons that will become apparent below.
- 10.
In contemporary syntactic theories in the P&P tradition (Chap. 9), the phrasal category S has been replaced by other phrasal types—different ones in different theories. The list includes, inter alia, inflectional phrase (IP), complementizer phrase (CP), and tense phrase (TP). For ease of exposition, I ignore this and related complications.
- 11.
In principle, a top-down parser could make predictions even about which specific lexical items will appear in the input stream. But such predictions, even if made on the basis of frequency information and pragmatic/contextual clues, would still be rather risky. I assume, then, that lexical retrieval is in significant measure a data-driven process. This raises a question about whether the “matching” involved in lexical recognition is brute-causal, in the sense introduced by Devitt (2006a). The answer is that it’s not. The HSPM constructs phonological representations prior to, and in the service of, lexical retrieval. As with syntactic processing, the activation of a phonological representation is highly context-sensitive and dependent on factors that are not present in the immediate stimulus, but scattered over discontinuous chunks of time (Fernández and Cairns 2011: ch. 6). Moreover, higher-level syntactic decisions exert a downward influence on lexical retrieval and actively guide the correction of errors in the retrieval process.
- 12.
- 13.
- 14.
For details, see Jurafsky and Martin (2008: pp. 452–454). Kaplan (1973) contains an early but prescient discussion of various chart-parsing techniques.
- 15.
See also Schabes, Abeille, and Joshi (1988), who provide an instructive application of the Earley algorithm to lexicalized versions of context-free grammars, as well as to the mildly context-sensitive tree-adjoining grammar (TAG). The authors discuss the considerable gains in efficiency stemming from the lexicalization of these grammars.
- 16.
The terms “simple” and “complex” can be given a formal interpretation. Whereas programming languages tend to belong to the class of context-free languages, it has been known for some time that natural languages are slightly stronger than context-free. This was demonstrated by Shieber (1985), who discussed cases of cross-serial dependencies in Dutch and in Scandinavian languages. Syntacticians are in the process of constructing formalisms that fit this specification while avoiding overgeneration—i.e., without allowing the formulation of grammars that are not attested by any known natural language. The Minimalist grammars currently being explored in one branch of syntactic theory are committed to the existence of discontinuous constituents—syntactic chains in which antecedents do not c-command the traces or copies that they leave behind after movement. A grammar that generates discontinuous constituents is stronger than context-free. Minimalist grammars thus belong to the class of mildly context-sensitive grammars (Stabler 2001.) Shieber, Schabes, and Pereira (1993) discuss other mildly context-sensitive grammars, e.g., tree-adjoining grammars (TAGs), and provide a schema for constructing efficient parsers for these grammars.
- 17.
Though we’ll see in Sect. 8.4.2 that the differences between their concerns can also cause confusion.
- 18.
Another term for these parsers that may be familiar to computer scientists is ‘LR(k)’. The symbol ‘LR’ encodes the fact that the parser works from left to right. The symbol ‘(k)’ is a variable that defines the size of the parser’s “look-ahead window”—i.e., how much of the input it is allowed to take in before initiating some operation. An LR(2) parser, for instance, can look at two items of the input before deciding what to do next.
- 19.
There is a way in which this sort of remark can be misleading. Devitt (2006a: pp. 69–71) notes an important distinction between the expressions of a language, on the one hand, and structural descriptions of those expressions on the other. The latter can be derived from a theory of a language, but the former cannot. “[W]hat is derived from a grammar is not an expression of the language but a description of an expression, just as what is derived from an astronomical theory is not, say, a star, but a description of a star” (p. 69). If we are careful not to run afoul of this use/mention distinction, we must say that the parser uses its internally represented grammar—conceived now as (a subpersonal analogue of) a theory of a language—to generate structural descriptions of incoming linguistic stimuli. Hence, when we say that the parser produces “a structure that has an S node at its root,” we do not mean that it produces a sentence; rather the parser produces a description of the incoming stimulus, thus characterizing the stimulus as a structure that has an S node at its root. The question then arises: How do we get from such a descriptive characterization to the final product of language comprehension? If the final representation uses words, rather than mentioning them, then what accounts for the transition between a description (which merely mentions the words) to the final product (which uses them)? This puzzle disappears if say that the final representation merely “mentions” the words, though in a sense that’s closer to indirect discourse (e.g., “Kurt just said that it’s raining.”)
- 20.
Indeed, it had better not be the case, if psychological plausibility is our goal. As noted in Chaps. 5 and 6, the HSPM is an “eager” mechanism—the assignment of syntactic structure is never delayed; syntactic analysis begins at the very first hint of linguistic input and continues incrementally, morpheme by morpheme.
- 21.
- 22.
Note that the CFG must first be transformed into a binary-branching format known as Chomsky Normal Form (CNF). This transformation is well-defined and computationally trivial. See Jurafsky and Martin (2008: pp. 441–2).
- 23.
- 24.
See Abney and Johnson (1991), Stabler (1994), Crocker (1999), Harkema (2001). Abney and Johnson (1991) distinguish between what they call arc-eager and arc-standard left-corner parsers. The latter, they argue, are less efficient than the former. I omit the details here. Note also that left-corner parsing bears a close resemblance to what Fodor and Frazier (1980) term ‘information-paced parsing’. Frazier and Fodor argue that strictly top-down and bottom-up routines are too rigid to deal effectively with locally ambiguous inputs.
- 25.
- 26.
- 27.
This formulation is a paraphrase of the one found deVincenzi (1991). The second conjunct is equivalent to what Frazier and Clifton (1989) refer to as the “Active Filler Hypothesis.” I use the locution ‘gap/trace’ to avoid commitment to formalisms that have traces and movement operations in their theoretical toolkit.
- 28.
J. D. Fodor and L. Frazier (1978, 1980) do supply an additional argument for RT. Fodor and Frazier claim that their parsing model provides a principled explanation of why MA and LC are true of the parser, precisely in virtue of the model’s commitment to representing the grammar of a language in a separate data structure. They point out that the competing models, which implement the grammar “procedurally,” can build in such principles only in an ad hoc way, if at all. From this, they conclude that it “seems unavoidable that the well-formedness conditions on phrase markers are stored independently of the executive unit, and are accessed by it as needed.” (Frazier and Fodor 1978: 322n). We will return to this argument in Chap. 9.
- 29.
- 30.
- 31.
Manning and Schütze (2000) comment on this point, providing valuable methodological guidance to computational linguists: “It is not hard to induce some form of structure over a corpus of text. Any algorithm for making chunks—such as recognizing common subsequences—will produce some form of representation of sentences, which we might interpret as a phrase structure tree. However, most often the representations one finds bear little resemblance to the kind of phrase structure that is normally proposed in linguistics and NLP. Now, there is enough argument and disagreement within the field of syntax that one might find someone who has proposed syntactic structures similar to the ones that the grammar induction procedure which you have sweated over happens to produce. This can and has been taken as evidence for that model of syntactic structure. However, such an approach has more than a whiff of circularity to it. The structures found depend on the implicit inductive bias of the learning program. This suggests another tack. We need to get straight what structure we expect our model to find before we start building it. This suggests that we should begin by deciding what we want to do with parsed sentences. There are various possible goals: using syntactic structure as a first step towards semantic interpretation, detecting phrasal chunks for indexing in an IR system, or trying to build a probabilistic parser that outperforms n-gram models as a language model. For any of these tasks, the overall goal is to produce a system that can place a provably useful structure over arbitrary sentences, that is, to build a parser. For this goal, there is no need to insist that one begins with a tabula rasa. If one just wants to do a good job at producing useful syntactic structure, one should use all the prior information that one has” (407–408).
- 32.
Here, “in principle” means “identification in the limit,” where no bounds are placed on the amount of data the learning model is allowed to see.
- 33.
Gibson cites the results of a number of psycholinguistic experiments that establish the reality of this processing difficulty: “The object extraction is more complex by a number of measures including phoneme monitoring, on-line lexical decision, reading times, and response-accuracy to probe questions (Holmes 1973; Hakes et al. 1976; Wanner and Maratsos,1978; Holmes and O’Regan 1981; Ford 1983; Waters et al. 1987; King and Just 1991). In addition, the volume of blood flow in the brain is greater in language areas for object-extractions than for subject-extractions (Just et al. 1996a, b; Stromswold et al. 1996), and aphasic stroke patients cannot reliably answer comprehension questions about object-extracted RCs, although they perform well on subject-extracted RCs (Caramazza and Zurif 1976; Caplan and Futter 1986; Grodzinsky 1989; Hickok et al. 1993)” (p. 2).
- 34.
Resolving the dispute between advocates of serial and parallel models would clearly yield a deeper understanding of the HSPM. For discussion, see Crocker et al. (2000), and references therein.
- 35.
Of course, a serial parser can likewise make use of statistical information contained in PCFGs, e.g., to determine which rule to apply or which lexical item to select in cases of ambiguity. Jurafsky and Martin (2008: Chap. 14).
- 36.
Though, see Clark (2010) for a probabilistic parser that makes use of the sophisticated Combinatory Categorial Grammar (CCG). It seems likely that probabilistic extensions of such sophisticated grammars will emerge in the coming years.
- 37.
An interesting wrinkle: Manning (2003) argues that formal linguistics should itself take the statistical turn, so to speak, and cast its grammars in a probabilistic formalism. Thus, one way of securing a tight relation between the linguist’s descriptive grammar and the psycholinguist’s mental grammar is to adjust the former, not the latter.
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Pereplyotchik, D. (2017). Computational Models and Psychological Reality. In: Psychosyntax. Philosophical Studies Series, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-319-60066-6_8
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DOI: https://doi.org/10.1007/978-3-319-60066-6_8
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60064-2
Online ISBN: 978-3-319-60066-6
eBook Packages: Religion and PhilosophyPhilosophy and Religion (R0)