SYNTACTIC AMBIGUITY RESOLUTION 27 (6) Groucho Marx in ‘Animal Crackers’ (USA 1930): 2

One morning I shot an elephant in my pajamas.

How he got in my pajamas, I don’t know.

(6) shows the famous monologue of Groucho Marx as Captain Spaulding in ‘Ani-mal Crackers’. Initially, the syntactic ambiguity is not obvious. Knowledge about the world suggests that it was Spaulding who was still dressed in his pajamas, when he shot an elephant. However, when the second sentence unexpectedly re-veals that it was the elephant who was wearing the pajama, the recipient is puz-zled for a moment. The scene marks one of the best-known lines from a Marx Brother’s movie and shows that disambiguating a syntactic ambiguity towards the non-preferred (and totally unexpected) reading is a wonderful stylistic element in comedy. Thus,SYNTACTIC AMBIGUITY RESOLUTION delivers very valuable data for the exploration of the human sentences parser. A syntactic ambiguity occurs when more than one syntactic structure can be assigned to an input string.

The literature classifies instances of syntactic ambiguities in (i) GLOBAL AMBI

-GUITIES and (ii) and LOCAL AMBIGUITIES. A sentence is said to be globally ambiguous, if more than one structure can be assigned to the entire sentence. The unambiguous interpretation of the string remains unclear for the entire sentence.

A German example is given in (7) (cf. Bader, unpublished manuscript). The sen-tence consists of a transitive verb that asks for two arguments. Both noun phrases consist of proper names only. The missing specification for case of both NPs leads to global ambiguity. Both NPs, Maria and Max, can be either in the nominative or in the accusative case. This enables a subject-object (henceforth: SO) or an object-subject reading (henceforth: OS). In globally ambiguous sentences, dis-ambiguation does not take place within the sentence itself. The intended reading of (7) is only revealed by additional information supplied by a further context.

(7) Maria Maria

liebt loves

Max.

Max

‘Maria loves Max.’ or ‘Max loves Maria.’

Locally ambiguous sentences only bear an ambiguous reading up to a certain point within the sentence. At this point, integration of disambiguating information of a word w dismisses one possible syntactic structure in favor of another one. In (8), the globally ambiguous sentence in (7) has been modified with additional information. The second NP consists of a determiner marked for case and a proper name. At the beginning, (8) is ambiguous for case. Maria can be either the subject or the object of the phrase. Disambiguation takes place, when the second NP has to be integrated into the CPPM. Accusative case is on hand, when den (‘the_acc’)

2see: http://www.imdb.com/title/tt0020640/

occurs; nominative is on hand, when der (‘the_nom’) launches the NP. At this point, (8-a) disambiguates either towards a SO-reading, as den Max is in the accusative case, or towards a OS-reading in (8-b), as der Max is marked for nominative case.

(8) a. Maria liebt (_accden Max).

‘Maria loves Max.’

b. Maria liebt (_nomder Max).

‘Max loves Maria.’

Applying either of the two classes of ambiguities (cf. (7) and (8)) in experimental research adds valuable findings in investigating the HSPM. However, processing globally ambiguous sentences cannot satisfactorily be monitored in online experi-ments. Online data cannot unveil which of the two possible structures is applied to the incoming string. Reliable answers concerning ambiguity resolution of global ambiguous sentences have to be collected in offline tests. Locally ambiguous sen-tences are well-suited for online studies. The point of disambiguation takes place within the sentence and provides valuable data. The well-known sentence in (9) (taken from Bever, 1970) illustrates a phenomenon which has become known as theGARDEN-PATH EFFECT.

(9) The horse raced past the barn fell.

Experimental findings show that (9) is processed without problems until the dis-ambiguating word fell occurs. It seems that in the preferred initial analysis raced is assigned the role of the main verb. The three words following raced are still compatible with this analysis. However, when the HSPM is confronted with the task of integrating fell into the initial CPPM, the process of sentence parsing fails.

With the appearance of fell, it turns out that the initial analysis of the sentence structure was wrong. The word raced cannot be the verb of a main clause. In-stead, raced has to be analyzed as a past-participle of a reduced relative clause.

At the point of fell, the parser has to detect the previous mis analysis and instantly assign a new fitting structure to the current sentence string. The initial misparsing proverbially lead the parser “down the garden path”. Therefore, sentences as (9) are known asGARDEN-PATH SENTENCES. They are defined as locally ambiguous sentences that are disambiguated towards the non-preferred reading. At the point of disambiguation, the HSPM has to deal with detection and recovery from the initial mis analysis. These processes go along with increased processing difficul-ties. As Bader and Bayer (2006:24ff.) explain, the literature reports a wide range of garden-path effects of various degrees. Experimental findings show that the example above yields strong garden-path effects, while other garden-paths hardly affect sentence processing at all. For extensive work on varying degrees of

garden-3.3. SYNTACTIC AMBIGUITY RESOLUTION 29 path strength, see Meng (1998). The garden-path effect has brought up some in-teresting questions: why is one of the possible syntactic structures of a locally ambiguous sentence preferred over other interpretations? How does the HSPM decide which analysis will be computed when an ambiguous word appears?

Three Kinds of Processing Models. When the HSPM encounters that a new incoming word w allows more than one structural analysis, there are different processing strategies to adhere to. First, the parser immediately chooses one of the possible structures and builds the CPPM following this analysis. Second, the parser considers and computes all possible syntactical structures until disam-biguating information appears and resolves the ambiguity. Third, the parser ap-plies a wait-and-see strategy and delays the analysis of the incoming string until the ambiguity is resolved by new information. In the following, all three strategies will be explained in more detail.

A parser following the first strategy is known as a SERIAL PARSER. A se-rial parser works in an incremental way and immediately integrates every new word w into the CPPM. It treats all incoming sentence strings, unambiguous and ambiguous ones, alike. Thus, when encountering an ambiguity, a serial parser instantly chooses one of the possible analyses and computes it till disambiguat-ing information appears. At the point of disambiguation two scenarios can occur.

First, the initial analysis turns out to be correct. The resolving information dis-ambiguates the sentence towards the structure that has already been preselected by the HSPM. In this case sentence processing continues smoothly. This initial analysis of an incoming string has become known as FIRST-PASS PARSING. As the serial parser proceeds to parse local ambiguities immediately and with only a single CPPM, one might state that there is no difference in first-pass parsing of ambiguous and unambiguous sentences. The only difference between both events is the choice that has to be made when an ambiguity is encountered, compared to the unambiguous sentence string. When an ambiguity is resolved towards an alternative, unpreferred reading, additional processes have to be accomplish by the serial parser. The events of recovery and reanalysis have become known as

SECOND-PASS PARSING. The terms first-pass parsing and second-pass parsing will explicated after the other two major parsing models have been discussed.

APARALLEL PARSER is supposed to be incremental in nature, just as a serial parsing mechanism. It integrates every incoming new word immediately into the CPPM. However, when encountering a syntactic ambiguity, the parallel parsers differs from the procedural method of a serial parsing mechanism. A parallel parser immediately computes all possible alternative structures that fit the input up to this point. The parser keeps up all parallel parse trees until the appear-ance of a disambiguating word resolves ambiguity towards one interpretation.

All other structures are immediately discarded at the point of disambiguation.

Mitchell (1994) differentiates between ‘unbiased parallel models’ and ‘weighted parallel models’. Unbiased models treat all alternative structures alike. Parallel analyses are not qualified in some hierarchical ranking, but are coequal to each other. Assumptions of coequal analyses conflict with findings of increased pro-cessing complexities in garden-path sentences. If all analyses were computed and treated alike in parallel models, no disambiguation towards an unpreferred reading was possible. Weighted models might provide an explanation to effects of increased processing complexities. Weighted models also compute parallel analyses for ambiguous sentence strings. In these models, parallelism is ranked with regard to some given information. In influential parallel parser models those ranking mechanisms are often incorporated as a result of assumptions of resource limitations (cf. the ‘Dependency Locality Theory’; Gibson, 2000; for additional details to Gibson’s theory see chapter 4). Contrary to resource-free parser models, resource driven models succumb to certain economical constraints. The models are assumed to rank analyses according to some predicted likelihood of appear-ance. Implausible structures often have to be discarded before disambiguation takes place. Thus, incorporating processes of ranking and pruning enables paral-lel processing models to explain findings of garden-path effects.

A MINIMAL COMMITMENT PARSER strongly differs from the other parser models. When encountering an ambiguous word, the parser neither immediately commits to a single analysis, nor does it follow all possible readings. Just as a parallel parser, a minimal commitment parser differs between ambiguous and unambiguous sentence strings. Unambiguous sentences are parsed incrementally and fully specified. For ambiguous sentences, the parser does not fully commit to a specific reading. Sturt and Crocker (1998) state that the human parser objects to dissolve an already built structure if possible. It seems to be less burdening to add information to a CPPM than to breakup an already computed analysis. As a result of this, the ‘monotonicity hypothesis’ assumes that, starting with the point of am-biguity, CPPMs are computed unspecified for some aspects until disambiguating information appears. Committing to general and unspecific readings can, in the most extreme situation, result in a complete delay of processing. At the point of disambiguation, missing grammatical information will be added to the previously built structure. Adding these information is supposed to be easy for the parser and should not increase processing complexity. In order to be able to explain complexity phenomena, those models suggests that sentence parsing consists of various levels of commitment. The HSPM is supposed to make “[. . . ] an immedi-ate, but relatively weak, commitment, which can be relatively easily broken at the point of disambiguation [. . . ] as well as a firmer, but more delayed, commitment, which causes the processing trouble [. . . ]” (Sturt and Crocker, 1998:303).