Semantic Feature Analysis

Semantic Feature Analysis (SFA) is an educational technique designed to enhance vocabulary development and reading comprehension by visually mapping relationships between words and their meanings. This method involves creating charts that allow students to explore connections among vocabulary terms, enabling them to understand language as an interconnected system rather than mere lists to memorize. SFA is particularly beneficial for language learners, as it improves vocabulary recall and literacy skills, and fosters positive attitudes towards learning.

In addition to its applications in language education, SFA is also utilized in therapeutic settings for individuals with aphasia, a condition affecting communication due to brain injury. Through tailored chart exercises, patients learn to recall specific words by making personal associations, thereby strengthening their neural pathways related to language. Research indicates that SFA can significantly aid in improving word recall and communication effectiveness for patients with mild to moderate aphasia.

The versatility of Semantic Feature Analysis allows it to be employed across various age groups and educational contexts, from classrooms to therapy sessions. As technology evolves, digital tools and apps are being developed to facilitate SFA, making it accessible for independent practice at home and extending its reach to those in remote areas. Overall, SFA represents a valuable approach in both education and rehabilitation, promoting deeper understanding and effective communication.

Published in: 2021

By: Hahn, Allison, PhD

Subject Terms

Semantic Feature Analysis

Abstract

Semantic feature analysis is an educational chart-based method used to encourage students to see connections and make predictions about word choice. This is a useful tool in helping students to find the word that they are looking for and to build reading comprehension. Semantic feature analysis is sometimes taught as an individual skill, and at other times it is used in small or large groups. Semantic feature analysis can be used by many different age groups and ability levels. Beyond its use in standard classrooms, semantic feature analysis can be used as a therapy technique for people suffering from aphasia.

Overview

Semantic feature analysis is a popular tool used especially by students learning a foreign language. It involves constructing a simple chart that identifies words and phrases and then asking students to manipulate the information in such a way as to discover connections among the terms. Foreign language learners have shown improvements in both vocabulary recall and literacy after being trained in the use of semantic feature analysis. For example, Amer (2018) conducted a study to find out why semantic feature analysis was so commonly used by teachers of English as a second language. The resulting study showed that students are more successful using this method because they come to understand vocabulary not as a list of words to be memorized for a quiz, but as an interconnected set of language. Similarly, Yakışık and Dişli (2017) found that language learners have generally positive feelings toward this kind of formulaic language learning technique.

Semantic feature analysis is often used by patients with aphasia, a condition caused by brain injury that inhibits an individual’s use of words. The goal of semantic feature analysis treatment is to help users find the right word and to use the word correctly. When assigned as a type of therapy, semantic feature analysis is used in a continual, practice-based way that trains patients to both recall a specific word and to practice the process of recalling a word in future situations. After participating in semantic feature analysis, patients are able to more quickly recall words as well as show improvements in their ability to retell a story and make connections between different stories. These improvements result in an overall improvement in commutation effectiveness. They are especially helpful for bilingual and multilingual patients who may have difficulty recalling a word in one language, or across all languages. For example, patients with aphasia who were fluent in both English and German responded well after ten weeks of therapy (Knoph, Lind & Simonsen, 2015).

Many studies of semantic feature analysis rely on small sample groups, though they do show remarkable success rates and encourage larger research projects. Kladouchou, Papathanasiou, Efstratiadou, Christaki, and Hilari (2017) examined the ways that semantic feature analysis can be used in both individual and group settings. In this study, researchers assessed the video recordings of fifteen sessions of individual or group therapy for patients with aphasia. They found that while each therapy session showed improvement in the patient’s word recall and learning, the greatest improvements were shown when a patient was working individually with the therapist.

Applications

Semantic feature analysis begins when a student selects a category or specific topic which they will analyze. The student should be provided with an assortment of vocabulary terms, concepts, or images related to that topic. Then, they are given a chart that lists vocabulary terms on the left-hand column and features or images across the top row. The students are tasked with finding connections between the vocabulary terms and the features, placing an x in each box where there is a connection. This technique is used in many different fields and across an assortment of age groups. For example, young children in a reading class might be given a table which asks them to find the characteristics of key characters in a story. In the left column would be the names of characters from a story, and across the top row would be actions or characteristics which were displayed in the story. From this comparative chart, students might find that both the protagonist and victim in a story demonstrate the characteristic of caring but differ in the ways they solve problems.

Sematic feature analysis might also be used in a science class to help students classify different types of plants, animals, or other organisms. In this example, along the left-hand column would be different types of similar organisms, such as different types of primates. Then, along the top column would be differing characteristics, such as types of fur, movement, and diets. Students would be tasked with coordinating the primate types with their characteristics. Through this activity they would be better able to see connections among various species and perhaps determine how species that are similar in some respects are also different in their behaviors or other characteristics.

Semantic feature analysis is also used after an individual has suffered a stroke or other injury to the left side of the brain resulting in aphasia. A person suffering from mild aphasia will have difficulty finding a precise word but may be able to name similar objects or otherwise describe the word they are trying to come up with. In more severe forms of aphasia, patients find it nearly impossible to communicate, having difficulty both producing their own speech and understanding information given verbally or in written form. Semantic feature analysis works best for those patients who have mild or moderate aphasia. When used as a therapy, this tool helps patients to build new neural networks that provide overall improvement to vocabulary. This means that while a patient might work with a therapist on one specific word or topic, they could see improvements across a range of topics, even those that are not directly practiced (Pound, Parr Lindsay & Woolf, 2018).

The chart commonly used for aphasia patients is different than that used for young students. This is because the aphasia patients are typically much older than the student group. They can comprehend much more detailed charts, as well as can draw from more detailed examples and experiences. This chart places a picture in the center. The picture might be a complex painting or an image of a single object, such as a chair. Oftentimes this is an object the aphasia patient has had difficulty finding the word for. Surrounding the central object are six boxes, each prompting the patient to think of an association with the object in the central picture. These six boxes ask the patient to identify the noun group, use, action, properties, location, and association of the object in the central picture.

In the example of a chair, the patient might indicate that the chair is grouped as furniture, used for sitting, provides a place to sit, has a soft cushion, is found in a living room, and reminds the patient of a family dinner. These answers would be personal for each patient, for example the same chair could remind one patient of a family dinner and another of doing homework after school. These personal connections are part of the neural network which is being reestablished through the use of semantic feature analysis. By the time they have worked through the chart, the patient might have come up with the word “chair,” which would be an exciting result. However, even if they have not, the effort has not been lost. The therapist can provide the word “chair” and have the patient repeat the word back. By doing so, the patient is beginning to build the systems that will eventually lead to success and the ability to recall more words. This sometimes slow process of semantic feature analysis can be frustrating for patients. However, when used over a long period of study it can lead to remarkable improvements in language recall and use.

Computer programs and apps have also been developed to work through this chart, automatically generating the image for the center and walking the patient through four to six questions designed to help them to find the word for the object. These apps allow people with aphasia to continue practicing at home and independently, and as a result are able to encourage quicker recovery. Kurlund, Liu & Stokes (2018) found that aphasia patients made improvements when using unsupervised home practice and weekly teleconferencing meetings with their therapist. Concerns that older patients with aphasia would not be able to participate in this type of therapy because they would be too unfamiliar with the technology (such as a tablet computer) on which the therapy relies proved unfounded. Kurlund et al. reports that there were no difficulties with the technology, even among the oldest participants. Additionally, they report satisfaction among the patients who used the computerized version of semantic feature analysis. Development of this kind of program may help patients to receive therapy at home. This is especially important for elderly living at home or patients in rural areas who might otherwise lose out on treatment because of the time, effort, and cost of traveling long distances.

Viewpoints

Scholars and practitioners have proposed many variations to semantic feature analysis. These include focusing on sentence construction or working to find word errors in a complex sentence. Other adaptations encourage the patient to re-tell a story or sentence and focus on the ability to do so accurately (de Riesthal & Diehl, 2018; Pak-Hin Kong & Wan-Ying Wong, 2018). When used in a classroom to build vocabulary, teachers also use this technique to quickly drill concepts and vocabulary with students by asking yes/no or multiple choice questions about a specific subject (Kuder, 2017). For example, a teacher might work through flashcards of animal pictures asking students which animals run, which hop, and which fly. This type of recall asks students to mentally classify animals in the same way as if they were working with the standard charts used in semantic feature analysis. Even if children cannot think of a specific animal, they will still learn about animals and classification through this activity, which enables them to see and hear the examples proposed by their classmates. This is one example where working as a group can support semantic feature analysis. The group will be especially helpful if the students disagree about an answer, opening space for the teacher and students to have an in-depth conversation about animal classification and terms.

Other researchers are investigating the ways that semantic feature analysis can be used for machine learning. For example, Yang, Gu & Liu (2018) assessed the ways that collaborative filtering could be developed to help machines learn how to assess Big Data sets. The problem with analyzing Big Data is that there are often too many data points in one set of information. It is possible to break a Big Data set into smaller points, but in doing so, researchers will lose the richness of the data set. So, in an attempt to understand the full data set, computer programmers are working to develop programs that can handle large sets of data. While a computer can eventually analyze each piece of data, doing so takes a long time.

Additionally, data analysis at any speed is pointless if the computer does not know what it is looking for. Computer programs need to learn how to spot the most important words so they can search for meaningful word clusters, such as “political opponent” rather than “after the.” Finally, computer programs need to be trained to spot synonyms, similar phrases, and the emotions expressed in the texts they are analyzing. The program needs to learn, for example, that it can pair together information about both pop and soda. Much like the patient with aphasia, computers must be trained to find and produce necessary terms from a set of clues. Researchers hope that the interdisciplinary application of semantic feature analysis will assist them in producing faster, more efficient programs to read and analyze text.

Terms & Concepts

Aphasia: A language impairment that causes problems in the production and comprehension of speech, reading, and/or writing. This condition is caused by a brain injury, such as by stroke, and is most common among older individuals.

Big Data: Exceptionally large data sets, such as all of the Tweets ever sent, or decades of medical records. Computer archiving systems have made it possible to collect this data. However, scholars are still working on ways to assess and make meaning from Big Data sets.

Bilingual Aphasia Test: Patients who are fluent in several languages require specific assessments if they are affected by aphasia. The Bilingual Aphasia Test is not one specific test but rather a set of cultural and linguistic adaptations of a standard aphasia test. The bilingual test is given to provide therapists with information about which languages have been most affected by aphasia.

Circumlocution: A technique in which speakers talk around a word—instead of demanding that a precise word be used, circumlocution encourages giving a long description or relationship with the word, which can help the speaker to eventually find the right word. Even when the precise word is not found, using circumlocution allows the speaker to get his or her meaning across.

Lexical Retrieval: An educational process of helping students think from the abstract or big picture down to the very specific, singular word description.

Semantics: A linguistic term for the logic and meaning of language. This term encapsulates the definition of a term, as the meaning and interpretations of larger strings of language such as sentences and paragraphs.

Syntax: Arranging words to form meaning, usually in a sentence or longer composition.

Bibliography

Akil, M. A., & Rosida, A. (2018). The application of semantic feature analysis as a strategy to enrich student’s vocabulary. Journal of Advanced English Studies, 1(2), 12–20.

Amer, A. (2018). Teaching/Developing Vocabulary Using Semantic Feature Analysis. The TESOL Encyclopedia of English Language Teaching, 1–7.

de Riesthal, M., & Diehl, S. K. (2018). Conceptual, methodological, and clinical considerations for a core outcome set for discourse. Aphasiology, 32(4), 469–471.

Kladouchou, V., Papathanasiou, I., Efstratiadou, E. A., Christaki, V., & Hilari, K. (2017). Treatment integrity of elaborated semantic feature analysis aphasia therapy delivered in individual and group settings. International journal of language & communication disorders, 52(6), 733–749. Retrieved December 1, 2018 from EBSCO Online Database Education Source. http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=126052933&site=ehost-live

Knoph, M. I., Lind, M., & Simonsen, H. G. (2015). Semantic feature analysis targeting verbs in a quadrilingual speaker with aphasia. Aphasiology, 29(12), 1473–1496.

Kuder, S. J. (2017). Vocabulary instruction for secondary students with reading disabilities: An updated research review. Learning Disability Quarterly, 40(3), 155–164. Retrieved December 1, 2018 from EBSCO Online Database Education Source. http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=124139491&site=ehost-live

Kurland, J., Liu, A., & Stokes, P. (2018). Effects of a Tablet-Based Home Practice Program With Telepractice on Treatment Outcomes in Chronic Aphasia. Journal of Speech, Language, and Hearing Research, 61(5), 1140–1156.

Pak-Hin Kong, A., & Wan-Yin Wong, C. (2018). An Integrative Analysis of Spontaneous Storytelling Discourse in Aphasia: Relationship With Listeners’ Rating and Prediction of Severity and Fluency Status of Aphasia. American Journal of Speech-Language Pathology, 27, 1491–1505. Retrieved December 1, 2018 from EBSCO Online Database Education Source. http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=133216849&site=ehost-live

Pound, C., Parr, S., Lindsay, J., & Woolf, C. (2018). Beyond aphasia: Therapies for living with communication disability. Routledge.

Yakışık, B. Y., & Dişli, Ö. (2017). Perceptions of EFL learners towards a ‘word hunting’ experience in oral communication skills course. ELT Research Journal, 6(4), 322–336.

Yang, P., Gu, L., & Liu, X. (2018). Collaborative filtering driven by fast semantic feature analysis on Spark. Wireless Networks, 1–14.

Semantic Feature Analysis

On this Page

Subject Terms

Semantic Feature Analysis

Abstract

Overview

Applications

Viewpoints

Terms & Concepts

Bibliography

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