Treatment Approaches for Word Retrieval Deficits in Persons with Aphasia: Recent Advances

Word retrieval deficit is found to be one of the most persistent symptoms reported among the constellation of symptoms exhibited by persons with aphasia (PWAs). This deficit restraints the persons with aphasia to perform with ease across day-to-day conversations. As a consequence, PWAs fail to communicate their desired ideas or thoughts. Word retrieval is an intricate process as it entails various levels of processing. In addition, word retrieval breakdown can occur at multiple levels (semantic level or lexical-semantic level, or phonological level). Thus, there is a need for speech-language pathologists (SLPs) to treat this deficit through effective treatment approaches. In recent decades, semantic feature analysis, verb network strengthening treatment, and phonological component analysis have received greater focus and importance in treating word retrieval deficits. Many studies confirmed that the use of these treatment approaches on PWAs possesses a pivotal role in remediating word retrieval deficits.

Comparative Study on Feature-Based Scoring Using Vector Space Modelling System

This paper shows the importance of automated scoring (AS) and that it is better than human graders in terms of degree of reproducibility. Considering the potential of the automated scoring system, there is further a need to refine and develop the existing system. The paper goes through the state of the art. It presents the results concerning the problems of existing systems. The paper also presents the semantic features that are indispensable in the scoring system as they have complete content. Moreover, in the present research, a huge deviation has been exhibited by the system which has been shown later in performance analysis of the study, and this clearly indicates the novelty and improved results of the system. It explains the algorithms included in the methodology of this proposed system. The novelty of our work consists in the use of its own similarity function and its notation mechanism. It does not use the cosine similarity function between two vectors. This paper describes and develops a more accurate system which employs a statistical method for scoring. This system adopts and integrates rule-based semantic feature analysis.