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November 19, 2013

Semantic Feature Analysis


The meaning of each word in a language is formed of a set of  abstract characteristics known as semantic features (also known as Semantic Properties, Sense Components, Semantic Markers, Semantic Components), which acts as the determinant for distinguishing one word from another.The method by which the meaning of a word is analyzed into a set of semantic features is called Semantic Feature Analysis (also called Contrast Analysis, Componential Analysis).

Theoretical Assumptions

The theoretical assumptions underlying this approach are as follows:
  • The total meaning of a word can be analyzed in terms of a number of distinct elements or semantic features.
  • One kind of word can be distinguished from another by extracting the main features.


In Semantic Feature Analysis a word is analyzed in terms of a number of components of meaning. That is, during such analysis, the word is broken down into meaningful components which form the total sum of the meaning in a word. These components are not part of the vocabulary itself; the theoreticians postulated them in order to facilitate the description of the semantic relationship  between the words of a given language. They could be considered as semantic universals as they may possess the same characteristics in all languages.
Semantic Feature Analysis is capable of determining the presence or absence of semantic features. For example, by finding out the right semantic property of a word the learner is able to choose the appropriate noun for using it as the subject of a verb:

Semantic Features

The above sentence is syntactically sound unless we judge it in terms of meaning. From semantic point of view the sentence is quite nonsensical, because, here the noun “television” has been inappropriately used as the subject of the verb “killed”. That means the noun “television” does have the right property to enable it to kill a person. Therefore, although the sentence is structurally correct, it is odd due to its meaninglessness.


The main objective of Semantic Feature Analysis is to guide the students to analyze the meanings of selected vocabulary items from a topic which they are familiar with. It also aims to show the learners how words are both similar and different, thereby emphasizing the uniqueness of each word in the language.


Semantic Feature Analysis employs a chart to identify the basic features shared by key vocabulary words in a sentence or topic of discussion. By analyzing such a chart the learner is able to detect connections, make predictions and master important concepts. He will be also able to realize things that he doesn’t know yet, so he will know what additional research he need to do. The Semantic Analysis is prepared by observing the following steps:

  1. The teacher first chooses a topic to be studied.
  2. He then draws a chart.
  3. In the left column of the chart the teacher puts some key vocabulary items related to the topic. During selection of key vocabulary words the teacher tries not to list any words which the students already might know.
  4. Then across the top row of the chart the teacher lists a set of meaningful features that some of the vocabulary items might have.
  5. After that the teacher asks the students to put a “+” (plus) sign in cells in which a given vocabulary word possesses appropriate feature, and a “-” (minus) sign where it doesn't. The following is an example of the chart for Semantic Feature Analysis:
The television killed the man
Features/Property/Components of Words ↓
Key Vocabulary ↓
From the above chart we can guess that the word “television” in English involves the features (-animate, -human, -male, -adult). Therefore, it is obvious that the word “television” cannot be related with a living entity.


  1. The Semantic Feature Analysis helps to develop the learner’s ability of comprehension and vocabulary skills.
  2. Such an analysis creates ample scope for the learners to examine the related features to distinguish one word from another.
  3. Semantic Feature Analysis helps the learner to understand the conceptual meaning (also known as denotative meaning) of words, which is the meaning given in the dictionary and forms the core of word-meaning.
  4. Semantic Feature Analysis increases the learner’s ability to choose the right word in right place.
  5. The Semantic Feature Analysis provides opportunity for the teacher to know the learners’ knowledge about the topic of discussion; therefore, it allows the teacher to mould his instruction accordingly.


  1. Semantic Feature Analysis is incapable of explaining the connotative or figurative meaning of words.
  2. Although Semantic Feature Analysis is capable of describing of words that share certain fairly obvious semantic properties, it fails to analyze all vocabulary items of the language.
  3. Semantic Feature Analysis is Limited in focus and mechanical in style.


“Componential analysis.” Wikipedia. 2013. Wikimedia Foundation, Inc. 10 November 2013
< http://en.wikipedia.org/wiki/Componential_analysis>.

“Semantic Feature Analysis.” AdLit.org. 2013. WETA Washington, D.C. 10 November 2013
< http://www.adlit.org/strategies/22731/>.

“Semantic Feature Analysis.” Reading Rockets. 2013. WETA Washington, D.C.  10 November 2013
< http://www.readingrockets.org/strategies/semantic_feature_analysis/>.

“Semantic Feature Analysis.” Edweb. 2013. San Diego State University. 10 November 2013
< http://edweb.sdsu.edu/triton/guides/SFA.html>.

“The Theory of Componential Analysis in Semantics.” Neo English System. 2013.
Neo English System. 10 November 2013
< http://neoenglishsystem.blogspot.com/2010/12/theory-of-componential-analysis-in.html>.

Yule, George. The Study of Language. 2nd ed. Cambridge: CUP, 1996. 115-116.


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