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The semantic structure of word and its analysis Lecture
§ 1. Componential Analysis the meaning of every lexeme can be analyzed in terms of a set of more general semantic components or semantic features ( semes ), some or all of which will be common to several different lexemes in the vocabulary Make logical pairs of words so that each pair has smth in common with the rest: man, woman, boy, girl, bull, cow man : : woman = boy : : girl = bull : : cow (sex — male : : female) man : : boy = woman : : girl (age — adult : : non-adult) man : : bull = woman : : cow (human : : animal) The sense of man on the basis of these oppositions might be held to combine the concepts male, adult, and human
Thus, meaning may be regarded as semantic oppositions because the word’s meaning is reduced to its contrastive elements. The segmentation is continued as far as we can have markers needed for a group of words , and stops when a unique feature is reached (like classification of species in biology). A spinster — noun, count noun, human, adult, female, who has never married
§ 2. Polysemy and Semantic structure of the word The word is a structured set of interrelated lexical variants realized in different contexts and thought of as a group because manifested with one form and have a common semantic component. Polysemy is the ability of a word to possess several meanings (or lexico-semantic variants — LSV ). All lexico-semantic variants of a word taken together form its semantic structure or semantic paradigm. Some LSV are lexico-grammatical variants of a word, i. e. they belong to different lexico-grammatical groups of the same part of speech. Ex. run is intransitive in / ran home, but transitive in / run this office. Some of the variants demand an object naming some vehicle as in to run a car off the road ( go or take off course ), and so on.
The semantic structure of the word ““ youth” Includes three lexico-grammatical variants: 1) an abstract uncountable noun, as in the friends of one’s youth It includes two LSV recognized due to the lexical peculiarities of distribution : a) the state of being young b) the time of being young In “ to feel that one’s youth has gone” they are blended together because both the time and the state can be meant 2) a countable personal noun ‘a young man’ (plural youths) 3) a collective noun ‘young men and women’ having only singular form
Types of LSV as elements of a word’s semantic structure 1. Direct (nominates the referent in isolation, i. e. in one word sentences ex. fire-screen ) vs. figurative (the object named is characterized through its similarity with another object ex. smoke screen ) 2. Concrete vs. abstract ( screen actor, screen version ) 3. main/primary (the highest frequency) vs. secondary 4. Central vs. peripheral ( derived; ex. screen as ‘a silver-colored sheet on which pictures are shown ‘) 5. Narrow vs. extended (due to the scope of corresponding concept) 6. General vs. special/particular (terminological) 7. present-day vs. obsolete 8. stylistically neutral vs. stylistically coloured
Contexual meaning 9. language meaning vs. speech meaning (contextual/implicational) Contextual/implicational is the communicative value of a word based on latent possibilities which are not realized in a particular LSV but able to create new derived meanings; the information implied by virtue of what the speakers know about the referent Ex. A wolf is known to be greedy and cruel but the denotative meaning of this word does not necessarily include these features ( He is a wolf = a cruel greedy person ) Ex. Some men have acted courage who had it not; but no man can act wit (=pretend + irony because of unusual LG meaning of transitivity and the lexical meaning of objects to this verb) Ex. bomb implies great power the new colloquial meanings ‘great success ‘ and ‘great failure ‘
§ 3. Semantic change: ways of semantic structure development The semantic structure of a word is a flexible category, it changes with the development of the language. radiation and chain-development # 1 # 2 # 3 # 4 # 1 # 2 # 4 # 3 # 5 the split of polysemy; homonyms
3. 1. Classification of semantic changes A) based on associations of similarity or of contiguity : linguistic metaphor & metonymy Metaphor is a transfer of the name of one object onto another on the basis of comparison. It can be based on different types of similarity: similarity of shape: head of a cabbage, the teeth of a saw similarity of function: head of the school, the key to a mystery similarity of position: foot of a page/of a mountain, the leg of a chair or table similarity of behaviour: bookworm
Metonymy is a transfer of the meaning on the basis of contiguity ( closeness or contact ). Wall Street is in a panic The White House isn’t saying anything the crown for ‘monarchy‘ hand for ‘handwriting‘ diesel engine — a type of compression ignition engine invented by a German mechanical engineer Rudolf Diesel I want to have a word with you (part for the whole)
B) identified on the basis of comparing logical notions: specialization & generalisation Specialization — the content of the notion is being enriched, as it includes a greater number of relevant features by which the notion is characterized but the word can name fewer objects (fewer referents). Ex. Originally meat meant any kind of food. In the course of time the word meat was specialized in its meaning and nowadays it means « the flesh of an animal » . Fowl originally meant any kind of a bird; now it means home birds. OE deor ‘ — wild beast’ Mod. E ‘deer‘ — wild ruminant of a particular species’ Sometimes a word passes from a general sphere to some special area of communication. case is specialized in its meaning when used in law or in medicine.
Generalization (reverse to specialization) is the way of transforming the meaning when a word acquires a broader meaning. Due to the change of use: to serve was a clerical term, now it is used in everyday speech To arrive was a nautical term, which meant to reach the land by water. Now it means to reach the place of destination in any way: by land, by water, by air. The transfer from a concrete meaning to an abstract one: Journey was borrowed from French with the meaning one day trip , now it means a trip of any duration. Fly originally meant ‘to move through the air with wings ‘; now it denotes any kind of movement in the air or outer space
C) Changes depending on the social attitude to the object named and change of emotional tone: amelioration & pejoration Words change semantic structure because their referents come up or down the social scale Pejoration (degradation) is the way of transforming the meaning when the meaning becomes worse: villain originally meant working on a villa. Then it acquired the meaning of scoundrel. Churl means ‘an ill-mannered and surly fellow, a boor ‘ ceorl of the 13 th century denoted the lowest rank of a freeman, later — a serf clown — the original meaning was also ‘peasant’ or ‘farmer’.
Amelioration ( elevation) is the way of transforming the meaning when it becomes better in the cause of time: knight meant a boy , then became a servant , at last it acquired a meaning о f a noble man. OE cwen ‘a woman’ Mod. E queen noble as ‘ possessing high ideals or excellent moral character ’ from ‘belonging to the nobility’
3. 2. Causes of semantic changes A) linguistic 1) Differentiation of synonyms is a gradual change observed in the course of language history, sometimes involving the semantic assimilation of loan words Ex. time and tide used to be synonyms. Then tide took on its more limited application to the shifting waters , and time alone is used in the general sense. The word beast was borrowed from French into Middle English. Before it appeared the general word for animal was deer which after the word beast was introduced became narrowed to its present meaning ‘a hoofed animal of which the males have antlers’
2) changes taking place in connection with ellipsis and with fixed contexts The qualifying words of a frequent phrase get omitted, what’s left acquiring the meaning of the whole: sale comes to be used for cut-price sale propose for propose marriage be expecting for be expecting a baby summit for summit meeting 3) changes resulting from ambiguity in certain contexts One can be doubtful about a doubtful question, in a healthy climate children are healthy
B) The extralinguistic causes are determined by the social nature of the language 1) the development of the notion expressed and the thing named The word space meant ‘extent of time or distance’. Alongside this meaning a new meaning developed ‘the limitless and indefinitely great expanse in which all material objects are located’ 2) the appearance of new notions and things The word bikini as ‘a very scanty two-piece bathing suit worn by women ’, is named after Bikini atoll in the Western Pacific because it appeared at the time when the atomic bomb tests by the US in the Bikini atoll were fresh in everybody’s memory. The associative field is emotional referring to the «atomic» shock the first bikinis produced.
Practical task # 4 1. Guess whether words below are homonyms (different words accidentally having one form) or LSV of one word a) 26 letters of the ABC vs. to receive letters regularly b) To be a foot long vs. at the foot of the mountain c) A hand of the clock vs. to hold a pen in one’s hand
2. Match types of LSV on the right to the meanings of the word on the left as compared to the 1 st one (more than one characteristic is possible) Clock 1. a freestanding device that measures and records time 2. a ny measuring instrument with a dial or a digital display, especially the odometer ( records the distance traveled ) 3. an electronic circuit that generates pulses at a constant rate in order to synchronize the internal operations in a computer 4. the fluffy white seed head of a dandelion (одуванчик) a) Special b) Figurative c) Peripheral d) Extended e) Secondary
3. Define the type of semantic change in the semantic structure of the word a) “ case” from ‘circumstances in which a person or a thing is’ to ‘a patient’, ‘an illness’ (in medicine) b) “ cat” from ‘ a small domesticated mammal that has soft fur, sharp claws, pointed ears, and, usually, a long furry tail ’ to ‘ a spiteful or malicious woman ’ c) “ iron” from ‘ a heavy, magnetic, silvery white metallic element ( Symbol Fe ) to ‘ a small electrical appliance with a flat metal base that is heated and used to press clothes ’
Chair 1. 1. a seat with a back support, usually for one person 2. 2. somebody presiding over something such as a committee, board, or meeting 3. 3. somebody who holds an endowed professorship at a university 4. 4. the ranked position of a musician in an orchestra 5. 5. a device to keep reinforcing rods in place during the pouring of concrete
There
exist several methods of the analysis of meaning:
a)
Definitional
method
is the study of dictionary definitions of words. In this analysis the
data from various dictionaries are analysed comparatively, e.g.
b)
Transformational
analysis implies that the dictionary definitions are subjected to
transformational operations, e.g. the word dull (adj) is defined as
-‘uninteresting’
→ we transform it into ‘deficient in interest or excitement’
—
‘stupid’→ ‘deficient in intellect’
—
‘not active’ → ‘deficient in activity’, etc.
In
this way the semantic components of the analysed words are singled
out. In the analysed examples we have singled out just denotative
components, there are no connotative components in them.
c)
Componential
analysis
– is the distinguishing of semantic components of the analysed word
– denotation and connotation (if any). In the process of this
analysis the meaning of a word is defined as a set of elements of
meaning which are not part of the vocabulary of the language itself,
but rather theoretical elements. These theoretical elements are
necessary to describe the semantic relations between the lexical
elements of a given language.
d)
The semantic structure of a word is also studied through the word’s
linear relationships with other words, i.e. through its
combinability, collocability or in other words – distribution.
Using this method words are replaced by conventional word-class
symbols: N – noun, V – verb, A – adjective, prepositions and
conjunctions are not coded.
Thus,
this distributional
analysis or the analysis of combinability, collocability studies
semantics of a word through its occurrence with other words.
c)
Close to the previous method is contextual
analysis. It states that difference in meaning of linguistic units is
always indicated by a difference in environment. It studies
interaction of a polysemantic word with syntactic and lexical
environment. Context is divided into lexical, syntactical and mixed.
As
a rule indication on the meaning comes from syntactic, lexical and
morphological factors combined. The contextual method makes the study
of details and specific features more exact.
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From Wikipedia, the free encyclopedia
In linguistics, semantic analysis is the process of relating syntactic structures, from the levels of phrases, clauses, sentences and paragraphs to the level of the writing as a whole, to their language-independent meanings. It also involves removing features specific to particular linguistic and cultural contexts, to the extent that such a project is possible. The elements of idiom and figurative speech, being cultural, are often also converted into relatively invariant meanings in semantic analysis. Semantics, although related to pragmatics, is distinct in that the former deals with word or sentence choice in any given context, while pragmatics considers the unique or particular meaning derived from context or tone. To reiterate in different terms, semantics is about universally coded meaning, and pragmatics, the meaning encoded in words that is then interpreted by an audience.[1]
Semantic analysis can begin with the relationship between individual words. This requires an understanding of lexical hierarchy, including hyponymy and hypernymy, meronomy, polysemy, synonyms, antonyms, and homonyms.[2] It also relates to concepts like connotation (semiotics) and collocation, which is the particular combination of words that can be or frequently are surrounding a single word. This can include idioms, metaphor, and simile, like, «white as a ghost.»
With the availability of enough material to analyze, semantic analysis can be used to catalog and trace the style of writing of specific authors.[3]
See also[edit]
- Lexical analysis
- Discourse analysis
- Semantic analysis (machine learning)
- Literal and figurative language
- Translation
- Semantic structure analysis
- Sememe
References[edit]
- ^ Goddard, Cliff (2013). Semantic Analysis: An Introduction (2nd ed.). New York: Oxford University Press. p. 17.
- ^ Manning, Christopher; Scheutze, Hinrich (1999). Foundations of Statistical Natural Language Processing. Cambridge: MIT Press. p. 110. ISBN 9780262133609.
- ^ Miranda-Garcıa, Antonio; Calle-Martın, Javier (May 2012). «The Authorship of the Disputed Federalist Papers with an Annotated Corpus». English Studies. 93 (3): 371–390. doi:10.1080/0013838x.2012.668795. S2CID 162248379.
The purpose of semantic analysis is to draw exact meaning, or you can say dictionary meaning from the text. The work of semantic analyzer is to check the text for meaningfulness.
We already know that lexical analysis also deals with the meaning of the words, then how is semantic analysis different from lexical analysis? Lexical analysis is based on smaller token but on the other side semantic analysis focuses on larger chunks. That is why semantic analysis can be divided into the following two parts −
Studying meaning of individual word
It is the first part of the semantic analysis in which the study of the meaning of individual words is performed. This part is called lexical semantics.
Studying the combination of individual words
In the second part, the individual words will be combined to provide meaning in sentences.
The most important task of semantic analysis is to get the proper meaning of the sentence. For example, analyze the sentence “Ram is great.” In this sentence, the speaker is talking either about Lord Ram or about a person whose name is Ram. That is why the job, to get the proper meaning of the sentence, of semantic analyzer is important.
Elements of Semantic Analysis
Followings are some important elements of semantic analysis −
Hyponymy
It may be defined as the relationship between a generic term and instances of that generic term. Here the generic term is called hypernym and its instances are called hyponyms. For example, the word color is hypernym and the color blue, yellow etc. are hyponyms.
Homonymy
It may be defined as the words having same spelling or same form but having different and unrelated meaning. For example, the word “Bat” is a homonymy word because bat can be an implement to hit a ball or bat is a nocturnal flying mammal also.
Polysemy
Polysemy is a Greek word, which means “many signs”. It is a word or phrase with different but related sense. In other words, we can say that polysemy has the same spelling but different and related meaning. For example, the word “bank” is a polysemy word having the following meanings −
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A financial institution.
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The building in which such an institution is located.
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A synonym for “to rely on”.
Difference between Polysemy and Homonymy
Both polysemy and homonymy words have the same syntax or spelling. The main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. For example, if we talk about the same word “Bank”, we can write the meaning ‘a financial institution’ or ‘a river bank’. In that case it would be the example of homonym because the meanings are unrelated to each other.
Synonymy
It is the relation between two lexical items having different forms but expressing the same or a close meaning. Examples are ‘author/writer’, ‘fate/destiny’.
Antonymy
It is the relation between two lexical items having symmetry between their semantic components relative to an axis. The scope of antonymy is as follows −
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Application of property or not − Example is ‘life/death’, ‘certitude/incertitude’
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Application of scalable property − Example is ‘rich/poor’, ‘hot/cold’
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Application of a usage − Example is ‘father/son’, ‘moon/sun’.
Meaning Representation
Semantic analysis creates a representation of the meaning of a sentence. But before getting into the concept and approaches related to meaning representation, we need to understand the building blocks of semantic system.
Building Blocks of Semantic System
In word representation or representation of the meaning of the words, the following building blocks play an important role −
-
Entities − It represents the individual such as a particular person, location etc. For example, Haryana. India, Ram all are entities.
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Concepts − It represents the general category of the individuals such as a person, city, etc.
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Relations − It represents the relationship between entities and concept. For example, Ram is a person.
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Predicates − It represents the verb structures. For example, semantic roles and case grammar are the examples of predicates.
Now, we can understand that meaning representation shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relation and predicates to describe a situation. It also enables the reasoning about the semantic world.
Approaches to Meaning Representations
Semantic analysis uses the following approaches for the representation of meaning −
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First order predicate logic (FOPL)
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Semantic Nets
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Frames
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Conceptual dependency (CD)
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Rule-based architecture
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Case Grammar
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Conceptual Graphs
Need of Meaning Representations
A question that arises here is why do we need meaning representation? Followings are the reasons for the same −
Linking of linguistic elements to non-linguistic elements
The very first reason is that with the help of meaning representation the linking of linguistic elements to the non-linguistic elements can be done.
Representing variety at lexical level
With the help of meaning representation, unambiguous, canonical forms can be represented at the lexical level.
Can be used for reasoning
Meaning representation can be used to reason for verifying what is true in the world as well as to infer the knowledge from the semantic representation.
Lexical Semantics
The first part of semantic analysis, studying the meaning of individual words is called lexical semantics. It includes words, sub-words, affixes (sub-units), compound words and phrases also. All the words, sub-words, etc. are collectively called lexical items. In other words, we can say that lexical semantics is the relationship between lexical items, meaning of sentences and syntax of sentence.
Following are the steps involved in lexical semantics −
-
Classification of lexical items like words, sub-words, affixes, etc. is performed in lexical semantics.
-
Decomposition of lexical items like words, sub-words, affixes, etc. is performed in lexical semantics.
-
Differences as well as similarities between various lexical semantic structures is also analyzed.
Semantic Analysis
Humans interact with each other through speech and text, and this is called Natural language. Computers understand the natural language of humans through Natural Language Processing (NLP).
NLP is a process of manipulating the speech of text by humans through Artificial Intelligence so that computers can understand them. It has made interaction between humans and computers very easy.
(Recommended read : Top 10 Applications of NLP)
Human language has many meanings beyond the literal meaning of the words. There are many words that have different meanings, or any sentence can have different tones like emotional or sarcastic. It is very hard for computers to interpret the meaning of those sentences.
Semantic analysis is a subfield of NLP and Machine learning that helps in understanding the context of any text and understanding the emotions that might be depicted in the sentence. This helps in extracting important information from achieving human level accuracy from the computers. Semantic analysis is used in tools like machine translations, chatbots, search engines and text analytics.
In this blog, you will learn about the working and techniques of Semantic Analysis.
How does Semantic Analysis work?
According to this source, Lexical analysis is an important part of semantic analysis. Lexical semantics is the study of the meaning of any word. In semantic analysis, the relation between lexical items are identified. Some of the relations are hyponyms, synonyms, Antonyms, Homonyms etc.
Let us learn in details about the relations:
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Hyponymy: It illustrates the connection between a generic word and its occurrences. The generic term is known as hypernym, while the occurrences are known as hyponyms.
-
Homonymy: It may be described as words with the same spelling or form but diverse and unconnected meanings.
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Polysemy: Polysemy is a term or phrase that has a different but comparable meaning. To put it another way, polysemy has the same spelling but various and related meanings.
-
Synonymy: It denotes the relationship between two lexical elements that have different forms but express the same or a similar meaning.
-
Antonymy: It is the relationship between two lexical items that include semantic components that are symmetric with respect to an axis.
-
Meronomy: It is described as a logical arrangement of letters and words indicating a component portion of or member of anything.
Through identifying these relations and taking into account different symbols and punctuations, the machine is able to identify the context of any sentence or paragraph.
(Read also: What is text mining?)
Meaning Representation:
Semantic analysis represents the meaning of any sentence. These are done by different processes and methods. Let us discuss some building blocks of the semantic system:
-
Entities: Any sentence is made of different entities that are related to each other. It represents any individual category such as name, place, position, etc. We will discuss in detail about entities and their correlation later in this blog.
-
Concepts: It represents the general category of individual, such as person, city etc.
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Relations: It represents the relation between different entities and concepts in a sentence.
-
Predicates: It represents the verb structure of any sentence.
There are different approaches to Meaning Representations according, some of them are mentioned below:
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First-order predicate logic (FOPL)
-
Frames
-
Semantic Nets
-
Case Grammar
-
Rule-based architecture
-
Conceptual graphs
-
Conceptual dependency (CD)
(Related blog: Sentiment Analysis of YouTube Comments)
Meaning Representation is very important in Semantic Analysis because:
-
It helps in linking the linguistic elements of a sentence to the non-linguistic elements.
-
It helps in representing unambiguous data at lexical level.
-
It helps in reasoning and verifying correct data.
Processes of Semantic Analysis:
The following are some of the processes of Semantic Analysis:
-
Word Sense disambiguation:
It is an automatic process of identifying the context of any word, in which it is used in the sentence. In natural language, one word can have many meanings. For eg- The word ‘light’ could be meant as not very dark or not very heavy. The computer has to understand the entire sentence and pick up the meaning that fits the best. This is done by word sense disambiguation.
-
Relationship Extraction:
In a sentence, there are a few entities that are co-related to each other. Relationship extraction is the process of extracting the semantic relationship between these entities. In a sentence, “I am learning mathematics”, there are two entities, ‘I’ and ‘mathematics’ and the relation between them is understood by the word ‘learn’.
(Also read: NLP library with Python)
Techniques of Semantic Analysis:
There are two types of techniques in Semantic Analysis depending upon the type of information that you might want to extract from the given data. These are semantic classifiers and semantic extractors. Let us briefly discuss them.
-
Semantic Classification models:
These are the text classification models that assign any predefined categories to the given text.
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Topic classification:
It is a method for processing any text and sorting them according to different known predefined categories on the basis of its content.
For eg: In any delivery company, the automated process can separate the customer service problems like ‘payment issues’ or ‘delivery problems’, with the help of machine learning. This will help the team notice the issues faster and solve them.
(Related read: Text cleaning and processing in NLP)
-
Sentiment analysis:
It is a method for detecting the hidden sentiment inside a text, may it be positive, negative or neural. This method helps in understanding the urgency of any statement. In social media, often customers reveal their opinion about any concerned company.
For example, someone might comment saying, “The customer service of this company is a joke!”. If the sentiment here is not properly analysed, the machine might consider the word “joke” as a positive word.
Latent Semantic Analysis: It is a method for extracting and expressing the contextual-usage meaning of words using statistical calculations on a huge corpus of text. LSA is an information retrieval approach that examines and finds patterns in unstructured text collections as well as their relationships.
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Intent classification:
It is a method of differentiating any text on the basis of the intent of your customers. The customers might be interested or disinterested in your company or services. Knowing prior whether someone is interested or not helps in proactively reaching out to your real customer base.
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Semantic Extraction Models:
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Keyword Extraction:
It is a method of extracting the relevant words and expressions in any text to find out the granular insights. It is mostly used along with the different classification models. It is used to analyze different keywords in a corpus of text and detect which words are ‘negative’ and which words are ‘positive’. The topics or words mentioned the most could give insights of the intent of the text.
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Entity extraction:
As mentioned earlier in this blog, any sentence or phrase is made up of different entities like names of people, places, companies, positions, etc. This method is used to identify those entities and extract them.
It can be very useful for customer service teams of businesses like delivery companies as the machine can automatically extract the names of their customers, their location, shipping numbers, contact information or any other relevant or important data.
(Recommended read: Word embedding in NLP using python code)
Conclusion
In any customer centric business, it is very important for the companies to learn about their customers and gather insights of the customer feedback, for improvement and providing better user experience.
With the help of machine learning models and semantic analysis, machines can easily extract meaning from unstructured data gathered from their customer base in real time. It helps the company get accurate feedback that drives better decision-making and as a result improves the customer base.