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No description available.

This is a mixed-methods study investigating the effects of oral repetition tasks on Japanese students' use of lexcial phrases in communicative EFL instruction. The quantitative study showed that oral repetition facilitated the students' short-term memory of target lexical phrases but it did not translate into their long-term memory or use of the phrases. The qualitative study indicated that the participants perceived interpersonal conversations and small-group discussions in English as enjoyable and useful activities. / CITE/Language Arts

The purpose of this study was to examine changes in muscle ultrastructure and strength performance following a single bout of elbow flexor resistance exercise. Eight untrained males performed 8 sets of 8 repetitions at 80% concentric 1 RM. One arm performed only the concentric (CON) phase of the movement while the other performed only the eccentric (ECC) phase. Maximum isometric (MVC), low (LV} and high velocity (HV} concentric peak torque, and evoked contractile property measurements of the elbow flexors were made before and after the bout, and at 24, 48, 72 and 96 h. Needle biopsies were obtained from the biceps brachii prior to the exercise, immediately post-exercise from each arm (POSTCON, POST-ECC}, and 48 h post-exercise from each arm (48H-CON, 48H-ECC). Electron microscopy was used to quantify the extent of fiber disruption in each sample. The severity of disruption was classified as focal (FOC}, moderate (MOD}, or extreme (EXT}. All strength measurements decreased (P s; o. 05} below pre-exercise values immediately post-ex in both arms, but dramatic differences were observed between arms during the subsequent recovery period. MVC, LV, HV and peak twitch torque (PTT) recovered to pre-ex values by 24 h in the CON arm. In the ECC arm, HV did not recover for at least 72 h, and MVC, LV and PTT remained depressed at 96 h. ANOVA revealed a greater (P s; 0.05) number fibers were disrupted in the POST-CON, POST-ECC, 48H-CON and 48H-ECC samples compared to BASE. Significantly more fibers appeared disrupted in the POST-ECC (82%) and 48H-ECC (80%) samples compared to the POSTCON (33%) and 48H-CON (37%) samples, respectively. In addition, the POST-ECC (41%) and 48H-ECC (50%) samples contained a greater number of fibers with EXT disruption compared to the POST-CON (13%) and 48H-CON (17%) samples. Decreases in MVC at 48 h correlated (P ~ 0.05) with the extent of EXT disruption in the 48H-CON and 48H-ECC samples. These data indicate that both the CON and ECC phase of weightlifting produce myofibrillar disruption, with the greatest disruption occurring during the ECC phase. This study was supported by the Natural Sciences and Engineering Research Council of Canada. / Thesis / Master of Science (MSc)

Specifications of digital hardware systems are typically written in a natural language. The objective of this research is automatic information extraction from specifications to aid model generation for system level design automation. This is done by automatic extraction of the noun phrases and the verbs from the natural language specification statements. First, the natural language sentences are parsed using a chart parser. Then, a noun phrase and verb extractor scans these charts to obtain the noun phrases with their frequencies of occurrence. The noun phrases are then classified by semantic types. Also the verbs are automatically assigned their respective roots and classified. Finally, each sentence is summarized as a sequence of "chunks": noun phrases, verbs and prepositions. Vectors are generated from these chunks and imported into MS Excel for plotting occurrence graphs of noun phrases and verbs with respect to the sentences in which they occur. Finally, inter-term dependencies between noun phrases, and between noun phrases and verbs were studied. The frequencies of occurrence, the classification of chunks, the occurrence graphs and the inter-term dependencies together give useful information about the subject, the hardware components and the behavior of a system described by a natural language specification document. / Master of Science

The objective of this research was to extract simple noun phrases from natural language texts using two different grammars: stochastic context-free grammar (SCFG) and non-statistical context free grammar (CFG). Precision and recall were calculated to determine how many precise and correct noun phrases were extracted using these two grammars. Several text files containing sentences from English natural language specifications were analyzed manually to obtain the test-set of simple noun-phrases. To obtain precision and recall, this test-set of manually extracted noun phrases was compared with the extracted-sets of noun phrases obtained using the both grammars SCFG and CFG. A probabilistic chart parser was developed by modifying a deterministic parallel chart parser. Extraction of simple noun-phrases with the SCFG was accomplished using this probabilistic chart parser, a dictionary containing word probabilities along with the meaning, context-free grammar rules associated with rule probabilities and finally an algorithm to extract most likely parses of a sentence. The probabilistic parsing algorithm and the algorithm to determine figures of merit were implemented using C++ programming language. / Master of Science

published_or_final_version / English Studies / Master / Master of Arts

Thesis (M. A. (African Languages) --University of Limpopo,2005 / The study aimsat finding out thecategorical status of copulatives in Northern Sotho. This will be achieved by examining the morphosemantic features of various copulatives. From a morphological perspective, thestudy focuses on the following types ofcopulative verbs: ke, se, ba, le, na, and COP. The study argues that the foresaid copulatives are not particles but are fully-fledged verbs. As far as the semantic nature of the copulatives is concerned, the study discovered a variety of meanings associated with copulatives, such as the following: the identifying, descriptive,locational and associative. Lastly, the study contends that any research on the copulative in Northern Sotho should be a morphosemantic one, as previous studies focused on one and neglected the other.

In this project, we address the task of nested noun phrase identification in English sentences, where a phrase is defined as a group of words functioning as one unit in a sentence. Prior research has extensively explored the identification of various phrases for language understanding and text generation tasks. Our aim is to tackle the novel challenge of identifying nested noun phrases within sentences. To accomplish this, we first review existing work on related topics such as partial parsing and noun phrase identification. Subsequently, we propose a novel approach based on transformer models to recursively identify nested noun phrases in sentences. We fine-tune a pre-trained uncased BERT model to detect phrase structures in a sentence and determine whether they represent noun phrases. Our recursive approach involves merging relevant segments of a sentence and assigning labels to the noun phrases at each step, facilitating the identification of nested structures. The evaluation of our model demonstrates promising results, achieving a high accuracy of up to 93.6% when considering all noun phrases in isolation and 90.9% when accounting for the predicted phrase structure of the sentence. Additionally, our model exhibits a recall rate of 83.5% and 81.2% at both levels, respectively. Overall, our model proves to be effective in identifying nested noun phrases, showcasing the potential of transformer-based models in phrase structure identification. Future research should explore further applications and enhancements of such models in this domain. / I detta projekt tar vi upp uppgiften att identifiera nästlade substantivfraser i engelska meningar, där en fras definieras som en grupp ord som fungerar som en enhet i en mening. Tidigare forskning har utförligt utforskat identifieringen av olika fraser för språkförståelse och textgenereringsuppgifter. Vårt mål är att ta itu med den nya utmaningen att identifiera nästlade substantivfraser i meningar. För att åstadkomma detta granskar vi först befintligt arbete med relaterade ämnen som partiell analys och identifiering av substantivfraser. Därefter föreslår vi en ny metod baserad på transformers-modeller för att rekursivt identifiera nästlade substantivfraser i meningar. Vi finjusterar en förtränad BERT-modell utan kapsling för att upptäcka frasstrukturer i en mening och avgöra om de representerar substantivfraser. Vårt rekursiva tillvägagångssätt innebär att sammanfoga relevanta segment av en mening och att tilldela etiketter till substantivfraserna vid varje steg, vilket underlättar identifieringen av nästlade strukturer. Utvärderingen av vår modell visar lovande resultat och uppnår en hög precision på upp till 93,6% när man tar hänsyn till alla substantivfraser isolerat och 90,9% när man tar hänsyn till meningens förutsagda frasstruktur. Dessutom uppvisar vår modell en täckning (recall) på 83,5% respektive 81,2% på båda nivåerna. Sammantaget visar vår modell sig vara effektiv för att identifiera nästlade substantivfraser, vilket visar potentialen hos transformers-modeller för identifiering av frasstruktur. Framtida forskning bör utforska ytterligare tillämpningar och förbättringar av sådana modeller på detta område.