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Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it 2016

Anna Corazza
Simonetta Montemagni
Giovanni Semeraro

Written word production and lexical self-organisation: evidence from English (pseudo)compounds

Marcello Ferro, Franco Alberto Cardillo, Vito Pirrelli, Christina L. Gagné et Thomas L. Spalding


Elevation in typing latency for the initial letter of the second constituent of an English compound, relative to the latency for the final letter of the first constituent of the same compound, provides evidence that implementation of a motor plan for written compound production involves smaller constituents, in both semantically transparent and semantically opaque compounds. We investigate here the implications of this evidence for algorithmic models of lexical organisation, to show that effects of differential perception of the internal structure of compounds and pseudo-compounds can also be simulated as peripheral stages of lexical access by a self-organising connectionist architecture, even in the absence of morphosemantic information. This complementary evidence supports a maximization-of-opportunity approach to lexical modelling, accounting for the integration of effects of pre-lexical and lexical access.

Texte intégral

1 The evidence

1A key question concerning the representation and processing of compound words has focused on whether (and, if so, how) morphological structure plays a role. The bulk of the research on this issue has come from recognition or comprehension tasks such as lexical decision or reading. However, written production provides a useful counterpart and allows researchers to examine whether morphemes are used even after a word has been accessed. One advantage of a typing task (in which the time to type each letter of a word is recorded) is that researchers can examine differences in processing difficulty at various points in the word. Previous research found an elevation in typing latency for the initial letter of the second constituent relative to the latency for the final letter of the first constituent for English (Gagné & Spalding 2014; Libben et al. 2012; Libben & Weber 2014) and German compounds (Sahel et al. 2008; Will et al. 2006). This elevation in typing latency at the morpheme boundary suggests that the system plans the output morpheme by morpheme, rather than as a whole unit, and that morphological programming is not complete when the motor system begins the output of the word (Kandel et al. 2008).

2Gagné and Spalding (2016) examined the role of morphemic structure and semantic transparency on typing latency. The stimuli consisted in 200 compounds, 50 pseudo-compounds, and 250 monomorphemic words matched pairwise with the compounds and pseudo-compounds in the number of syllables and letters. The pseudocompounds contain two words that do not function as morphemes (e.g., carpet contains car and pet). The compounds varied in whether the first and second constituent were semantically transparent. The items were displayed individually using a progressive demasking procedure and participants typed the word as the computer recorded the time required to type each letter.

3The time to initiate the first letter was equivalent for monomorphemic and compound words. Typing times got faster across the word for both word types, but the rate of change was faster for monomorphemic words than for compound words. This difference was not observed when comparing monomorphemic words and pseudocompounds.

4For compounds, the rate of speed-up was slower when the first constituent was transparent than when it was opaque, but was unaffected by the transparency of the second constituent. The elevation in typing latency at the morpheme boundary was larger when the first constituent was transparent than when it was opaque, but was unaffected by the transparency of the second constituent. This difference is due to the final letter of the first constituent when the first constituent requiring less time to type when it was transparent than when it was opaque.

5The data for the pseudo-compounds indicated that embedded morphemes influence production, even when they do not function as morphemes. Typing latency increased one letter prior to the end of the first constituent of a pseudocompound and remained elevated through the boundary (e.g., both r and c in scarcity were elevated relative to the a).

1.1 Implications for lexical architectures

6The reported evidence clearly indicates that morphemic structure is involved in written word production. The production of compounds differs from that of monomorphemic words and the semantic transparency of the two constituents leads to different effects. Furthermore, embedded pseudo-morphemes appear to influence the production of pseudo-compounds, but not in the same way that the embedded morphemes affect the production of compounds.

7This appears to lend only partial support to models of lexical architecture where both compounds and their constituents are represented and processed as independent access units (Figure 1). In panel A, following Taft & Forster (1975), access and output of compounds are mediated by their constituents (Cs), but extra procedures would be needed to account for the role of semantic transparency in modulating the size of elevation in typing latency at the morpheme boundary. A supralexical account (panel B: Giraudo & Grainger 2000, Grainger et al. 1991), where constituents are activated upon compositional interpretation of compounds, cannot capture the persistence of typing effects in semantically opaque compounds (and, to an extent, in pseudo-compounds). Race models (panel C: Schreuder & Baayen 1995) posit parallel pathways for compound processing (both holistic and compositional), depending on variables such as whole word vs. constituent frequency, but it is not clear how they can account for effects of interaction between the two paths. Connectionist models (panel D: Rumelhart & McClelland 1986, Plaut & Gonnerman 2000), on the other hand, tend to dispense with specialized representational levels and access procedures, and make room for distributed effects of sublexical coactivation through overlaying patterns of processing units. A defining feature of these models is that they blur the traditional distinction between representations and processing units. We suggest that blurring this distinction can go a long way in addressing some of the issues that appear to elude models A, B and C.

Figure 1 – Four architectures of form-meaning mapping in the mental lexicon: C1+C2 designates two-word compounds and Cs mono-morphemic constituents (adapted from Diependaele et al. 2012)

Image 1000000000000313000002697E906023.jpg

8Temporal Self-Organising Maps (TSOMs: Ferro et al. 2011; Marzi et al. 2014; Pirrelli et al. 2015), are a time-sensitive variant of Kohonen’s SOMs (Kohonen, 2002), where words are stored through routinized, time-bound patterns of repeatedly successful processing units. Since all input words are stored concurrently on the same layer of fully connected nodes, TSOMs account for effects of co-activation of competing representations in terms of a continuous function of distributional regularities in the input data. In what follows, starting from Gagné & Spalding’s evidence, we will focus on peripheral stages of lexical access/output, to verify if mechanisms of parallel, distributed pattern activation can account for differential processing effects between compounds and pseudo-compounds even in the absence of morpho-semantic information. Although computational testing is carried out on TSOMs only, our discussion and concluding remarks address issues that go beyond a specific computational framework.


9A TSOM consists of a grid of memory nodes with two layers of connectivity. The first layer (or I-layer) fully connects each node to the input vector, where symbols are sampled at discrete time ticks as patterns of activation ranging in the [0, 1] interval. Weights on the I-layer are adjusted in training for individual nodes to develop specialised sensitivity to particular input symbols. Each node is also connected to all other nodes through a layer of re-entrant connections (or T-layer), whose weight strength determines the amount of influence that activation of one node has on other nodes at a one-tick delay.

10When an input symbol is presented at time t, the level of activation yi(t) of node i is a function of: (a) the node’s sensitivity to the current input symbol (yI_layer,1(t)), and (b) the re-entrant support the node receives from the map activation state at t-1 (yT_layer,i(t)=f(yj(t-1)) , where f is a linear function and j ranges over all map nodes). More formally:


11The node responding most strongly to the input symbol S at time tick t is called Best Matching Unit (hereafter BMU(S, t) or BMU(t) for short).

12The map’s response to a sequence of input symbols like carpet is a chain of consecutively firing BMUs, each responding to a letter in carpet. During training, connection weights between consecutive BMUs are adjusted to the frequency distribution of input symbols in the training set, according to Hebbian principles of correlative learning. Given the bigram ab, the connection strength between BMU(a, t-1) and BMU(b, t) increases if a often precedes b (entrenchment) and decreases if b is often preceded by a symbol other than a (competition) (Figure 2, left). Combination of entrenchment and competition yields selective specialisation of chains of BMUs (Figure 2, right). If the same input symbol follows different contexts, it will tend to be responded to by more BMUs, one for each context. The stronger the probabilistic support that the input symbol receives from its preceding context, the more likely the recruitment of a dedicated BMU, and the stronger its re-entrant connection. As a result of this dynamic, high-frequency words recruit specialised node chains, low-frequency words are responded to by weaker, “blended” node chains.

Figure 2 Left: operation of Hebbian rules on potentiated (‘+’) and inhibited (‘-‘) connections. Right: forward onetick-delay connections leaving ‘A’ at time t-1. Larger nodes represent BMUs. Shades of grey indicate levels of node activation.

Image 1000000000000313000001A566174488.jpg

2.1 The Experiment

13The 200 compounds and 50 pseudo-compounds used by Gagné & Spalding were used to train a 40x40 node TSOM for 100 learning epochs. Besides compounds and pseudo-compounds, the training set included 500 (pseudo)constituents as individual words (e.g. car and wash in carwash, car and pet in carpet), for a total amount of 750 items. At each training epoch, monomorphemic words were shown 10 times as often as compounds. We ran 5 repetitions of the experiment, and results were analysed using linear mixed effects models (LME), with experiment repetitions and training items as random variables.

14To analyse differential processing effects for pseudo-compounds and compounds, we focused on two types of evidence: (i) per-letter performance of a trained TSOMs in incrementally anticipating compounds and pseudo-compounds;

Image 10000000000001130000001EB9FAF089.jpg

15(ii) structural connectivity of BMUs responding to letter bigrams at the C1-C2 boundary.

16To anticipate a progressively presented input word, a TSOM propagates the activation of the current BMU(t) through its forward temporal connections, and outputs, at each time tick, the symbol SMBU(t+1) encoded on the I_layer of the most strongly (pre)activated node:

17where mi,h is the weight value on the forward temporal connection from node h to node i. Each correctly predicted symbol in the input word is assigned the prediction score of the preceding symbol incremented by 1. Otherwise, the symbol receives a 0-point score.

Figure 3 – Marginal plots of interaction effects between compounds vs. pseudo-compounds and letter distance to morpheme boundary in an LME model fitting anticipation of up-coming BMUs by a TSOM. Negative and positive x values indicate letter positions located, respectively, in the first and second constituent. Anticipation is plotted across whole (pseudo)compounds (top panel), and by individual constituents (bottom panel).

Image 1000000000000313000004BEF98519C7.jpg

18Figure 3 (top panel) illustrates the rate of letter anticipation across the word for both compounds and pseudo-compounds, plotted by distance to the morpheme boundary. The steeper rate for pseudo-compounds than for compounds shows that pseudo-compounds are easier to predict/anticipate than compounds. We take this evidence to be in line with evidence of a faster speedup rate in the typing of monomorphemic vs. compound words. A closer look at anticipation rates for individual constituents (Figure 3 bottom panel) shows a drop of anticipation at the C1-C2 boundary (more prominent for compounds than pseudo-compounds) with a steeper increase in C1 and C2 for pseudo-compounds, which happen to be, on average, shorter than C1 and C2 in real compounds.

19To look for structural correlates of anticipation rates in the map, we conducted, for each item, a letter-by-letter analysis of values of pointwise entropy (PWH) for the connections between consecutive BMUs, namely h=BMU(t-1) and i=BMU(t):

Image 10000000000000980000002320EF557A.jpg

20The value of PWH for the connection between end-C1 and start-C2 (x = 0) has a local peak in compounds only (Figure 4). Since PWH provides a measure of how unexpected the activation of BMU(t) is, this structural evidence can account for a delay in processing and a drop in anticipation at the morpheme boundary of compounds, but not of pseudo-compounds.

Figure 4 – Marginal plots of interaction effects between compound vs. pseudo-compound constituents and letter distance to morpheme boundary in an LME model fitting pointwise entropy of forward BMU connections. Negative and positive x values indicate letter positions located, respectively, in the first and second constituent.

Image 10000000000003130000027C00D00E1E.jpg

3 Discussion and conclusions

21Trained on both compounds and pseudocompounds, TSOMs develop a growing sensitivity to surface distributional properties of input data, turning chains of randomly connected, general-purpose nodes into specialised sub-chains of BMUs that respond to specific letter strings at specific positions. Compounds not only tend to occur, on average, less frequently than their C1/C2 constituents do as independent words (Ji et al. 2011), but they tend to present lowerfrequency bigrams at the C1-C2 boundary than do pseudo-compounds. Principles of Hebbian learning allow TSOMs to capitalise on both effects. Entrenchment makes expectations for highfrequency bigrams stronger and expectations for low-frequency bigrams weaker. At the same time, the competition between C1 as an independent word and C1 as the first constituent in a C1-C2 compound biases the map’s expectation towards the most frequent event (C1 in isolation). Compound families, i.e. sets of compounds sharing C1 (windmill, windshield etc.) or C2 (snowball, basketball etc.), magnify these effects, making the map more sensitive to formal discontinuity at morpheme boundaries. When more C2s can follow the same C1 in complementary distribution, the left-to-right expectation for a particular C2 to occur, given C1, decreases. Likewise, when more C1s competitively select the same C2, the individual contribution of each C1 to the prediction of C2 decreases. We conjecture that more global effects of lexical organisation like these may eventually blur local memory effects based on position-independent bigram frequencies.

22Our simulations with TSOMs can model the correlation between continuously varying distributional regularities in the input data and peripheral levels of routinized recognition and production patterns. These patterns are in line with Gagné & Spalding’s evidence of (a) the influence of embedded pseudo-morphemes on cascaded models of written word production, and (b) faster anticipation rates for monomorphemic vs. compound words.

23Further experimental results (not reported here), obtained by including compound families in the training data, confirm slower anticipation rates for true compound constituents, due to the combined effect of word frequency distributions and word compositionality in compound families. The size of a compound family can arguably be a function of the degree of productivity and semantic transparency of its members (Baroni et al. 2007). The influence of the compound family size on anticipation rates can shed light on the influence of levels of semantic transparency on compound processing. Simulation evidence suggests that the bigger the family, the stronger its influence will be. Finally, we also monitored the influence of increasing token frequencies of monomorphemic words in the training data on the map perception of constituent boundaries within compounds. As expected, for constant frequency values of compounds in the training set, the higher the token frequency of monomorphemic words, the higher the pointwise entropy of connections at the C1-C2 boundary.

24A full account of Gagné & Spalding’s evidence of a graded influence of semantic transparency on compound processing is beyond the reach of the computational architecture presented here. Surface effects of discontinuity in the internal structure of compounds (as opposed to pseudo-compounds) appear to provide a purely formal, pre-lexical scaffolding for truly morphosemantic effects to emerge at later processing stages. To model these effects, we appear to be in need of a parallel processing architecture able to effectively integrate several representational levels (orthographic, phonological, morphological, and conceptual) and different processing steps within a single distributed system (Smolka et al. 2009). Nonetheless, our simulations show that by letting compounds, pseudo-compounds and (pseudo)constituents compete for the same level of memory resources on a topological map, it is possible to account for apparently contradictory effects of a) graded perception of constituent boundary in both compounds and pseudocompounds, apparently requiring prelexical decomposition, and b) higher anticipation rates for pseudo-compounds than compounds, supporting full form representations for lexical access.


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