Full Word Forms Research Articles

Coarticulation across morpheme boundaries: An ultrasound study of past-tense inflection in Scottish English

It has been hypothesized that morphologically-complex words are mentally stored in a decomposed form, often requiring online composition during processing. Morphologically-simple words can only be stored as a whole. The way a word is stored and retrieved is thought to influence its realization during speech production, so that when retrieval requires less time, the articulatory plan is executed faster. Faster articulatory execution could result in more coarticulation. Accordingly, we hypothesized that morphologically-simple words might be produced with more coarticulation than apparently homophonous morphologically-complex words, because the retrieval of monomorphemic forms is direct, in contrast to morphologically-complex ones, which might need to be composed online into full word forms. Using Ultrasound Tongue Imaging, we tested this hypothesis with nine speakers of Scottish English. Over two days of training, participants learned phonemically identical monomorphemic and morphologically-complex nonce words, while on the third consecutive testing day, they produced them in two prosodic contexts. Two types of articulatory analyses revealed no systematic differences in coarticulation between monomorphemic and morphologically-complex items, yet a few speakers did idiosyncratically produce some morphological effects on articulation. Our work contributes to our understanding of how morphologically complex words are stored and processed during speech production.

The impact of indexing approaches on Arabic text classification

This paper investigates the impact of using different indexing approaches (full-word, stem, and root) when classifying Arabic text. In this study, the naïve Bayes classifier is used to construct the multinomial classification models and is evaluated using stratified k-fold cross-validation ( k ranges from 2 to 10). It is also uses a corpus that consists of 1000 normalized Arabic documents. The results of one experiment in this study show that significant accuracy improvements have occurred when the full-word form is used in most k-folds. Further experiments show that the classifier has achieved the highest accuracy in the eight-fold by using 7/8–1/8 train–test ratio, despite the indexing approach being used. The overall results of this study show that the classifier has achieved the maximum micro-average accuracy 99.36%, either by using the full-word form or the stem form. This proves that the stem is a better choice to use when classifying Arabic text, because it makes the corpus dataset smaller and this will enhance both the processing time and storage utilization, and achieve the highest level of accuracy.

Limits on lexical prediction during reading

Efficient language processing may involve generating expectations about upcoming input. To investigate the extent to which prediction might facilitate reading, a large-scale survey provided cloze scores for all 2689 words in 55 different text passages. Highly predictable words were quite rare (5% of content words), and most words had a more-expected competitor. An eye-tracking study showed sensitivity to cloze probability but no mis-prediction cost. Instead, the presence of a more-expected competitor was found to be facilitative in several measures. Further, semantic and morphosyntactic information was highly predictable even when word identity was not, and this information facilitated reading above and beyond the predictability of the full word form. The results are consistent with graded prediction but inconsistent with full lexical prediction. Implications for theories of prediction in language comprehension are discussed.

The Variations of Words in Their Meanings

<p>The article investigates the variation of full word-forms and empty word-forms in the English language. Having investigated a lot of linguistic sources the author gives the explanation to them. Firstly, the author investigates the meanings of words. She considers words to be ambiguous notions both in everyday speech and in linguistics. As linguists claim words may be considered as forms whether spoken or written, or as composite expressions which have form and meaning. Then basing on linguistic sources the author gives the classes of word-forms in English: full forms and empty forms. The author investigates the differences between the two classes. She states that empty word-forms differ grammatically and semantically from full-words.</p>

A Statistical Method for English to Arabic Machine Translation

Translating from English into a morphologically richer language like Arabic is a challenge in statistical machine translation. Segmentation of Arabic text was introduced to bridge the inflection morphology gap. In this work, we investigate the impact of supporting Arabic morphologically segmented training corpus in a phrase-based statistical machine translation system with one to one dictionary and examine the effects on system performance. The results show that the dictionary improves the quality of the translation output especially when the corpus used is normalized and fully segmented excluding the determiner. The dictionary also decreases the out of vocabulary rate. The effect of the dictionary support with different baseline and factored models using data ranging from full word form to fully segmented forms are also demonstrated.

The impact of Arabic morphological segmentation on broad-coverage English-to-Arabic statistical machine translation

Morphologically rich languages pose a challenge for statistical machine translation (SMT). This challenge is magnified when translating into a morphologically rich language. In this work we address this challenge in the framework of a broad-coverage English-to-Arabic phrase based statistical machine translation (PBSMT). We explore the largest-to-date set of Arabic segmentation schemes ranging from full word form to fully segmented forms and examine the effects on system performance. Our results show a difference of 2.31 BLEU points averaged over all test sets between the best and worst segmentation schemes indicating that the choice of the segmentation scheme has a significant effect on the performance of an English-to-Arabic PBSMT system in a large data scenario. We show that a simple segmentation scheme can perform as well as the best and more complicated segmentation scheme. An in-depth analysis on the effect of segmentation choices on the components of a PBSMT system reveals that text fragmentation has a negative effect on the perplexity of the language models and that aggressive segmentation can significantly increase the size of the phrase table and the uncertainty in choosing the candidate translation phrases during decoding. An investigation conducted on the output of the different systems, reveals the complementary nature of the output and the great potential in combining them.

Idiolekto požymiai elektroninių laiškų leksikoje

Šiame straipsnyje tyrinėjama asmeninių elektroninių laiškų leksika. Tyrimo tikslas yra nustatyti, ar elektroninių laiškų leksikoje atsispindi laiškų autoriaus idiolektas, ir kokios žodžių grupės gali padėti jį atpažinti. Šiuo tikslu tirta, kokios dažnai vartojamos žodžių formos skiriasi šešių asmenų rašytuose elektroniniuose laiškuose. Nustatyta, kad žodžių formos, kurias vieni autoriai pasirenka dvigubai arba kelis kartus dažniau nei kiti, sudaro nuo 7 proc. iki 21 proc. vieno autoriaus elektroniniuose laiškuose pasikartojančios neteminės leksikos. Vartosenos dažniu išsiskiriančios žodžių formos priklauso įvairioms kalbos dalims, tad idiolekto atpažinimui reikalinga plati visų kalbos dalių vartosenos analizė. Nustatyta, kad elektroniniuose laiškuose idiolektus labiausiai skiria autoriaus požiūrį perteikiantys žodžiai, žodžių formos, pasirenkamos iš galimų sinonimų ir variantų, nenorminės vartosenos žodžių formos ir laiko dimensiją nusakantys žodžiai. Šis tyrimas parodo, kad elektroninių laiškų leksikoje esama idiolekto požymių, kurie, šalia kitų kalbos lygmenų analizės, gali padėti atpažinti autoriaus stilių ir atskirti jo parašytus tekstus nuo kitų autorių tekstų.

Authorship Attribution with Support Vector Machines

In this paper we explore the use of text-mining methods for the identification of the author of a text. We apply the support vector machine (SVM) to this problem, as it is able to cope with half a million of inputs it requires no feature selection and can process the frequency vector of all words of a text. We performed a number of experiments with texts from a German newspaper. With nearly perfect reliability the SVM was able to reject other authors and detected the target author in 60–80% of the cases. In a second experiment, we ignored nouns, verbs and adjectives and replaced them by grammatical tags and bigrams. This resulted in slightly reduced performance. Author detection with SVMs on full word forms was remarkably robust even if the author wrote about different topics.

THE PROCESSING OF GRAMMATICAL GENDER AND NUMBER IN SPANISH

The aim of the present study is to explore the representation and processing of inflectional morphology in Spanish. Experiment 1 compared the access time for words from the same base morpheme contrasted by the surface frequency of the masculine and feminine form, i.e., masculine-dominant items and feminine-dominant items. The results showed a surface frequency effect in both types of items. Experiment 2 compared the access time for masculine words having the same surface frequency but differing in their summed frequency (masculine plus feminine forms), the results showing no significant effect of this parameter. Finally, experiment 3 compared the access time for words from the same stem and contrasting by the surface frequencies for the singular and plural forms, i.e., singular-dominant and plural-dominant words. A clear frequency effect was observed for the singular-dominant words but not for plural-dominant ones. These results suggest that gender information is stored in the corresponding lexical entry and accessed from the full word form whereas the information about number is accessed from the stem corresponding to the singular form.

The identification of morphologically complex spoken words: Continuous processing or decomposition?

Models of word recognition differ in the claims they make about the processing and representation of morphologically complex words. The research reported here on the processing of spoken prefixed words contrasts predictions made by continuous left-to-right processing models and by models adhering to the notion of decomposition and discontinuous access to the mental lexicon. In left-to-right models, the speech input is mapped onto full-form representations in the lexicon in a continuous manner. In discontinuous models, the particular stretch of input corresponding to the stem of a complex word is used as the access unit, irrespective of where this information is located in the speech signal. Three sets of experiments, using gating and phoneme monitoring tasks, investigate whether the processing of prefixed (and pseudo-prefixed) words is determined by the complete, full-word form, or by the stem of the word. The results clearly disconfirm the decomposition model of word-recognition. None of the experiments provide evidence for the stem as the crucial unit in lexical access. Moreover, the results are not compatible with continuous processing models which claim that the lexicon contains only full-word forms. The implications of these findings for the processing and representation of morphologically complex spoken words are discussed.