Natural Language Processing Semantic Analysis

It is a study of the patterns of formation of words by the combination of sounds into minimal distinctive units of meaning called morphemes. Phonetics is the study of language at the level of sounds while phonology is the study of the combination of sounds into organized units of speech. In the below picture we can see the structure of any synset where we are having synonyms of benefit in the array of synsets with the definition and the example of usage of benefit word. This synset is related to another synset word, where the words benefit and profit have exactly the same meaning. This process is experimental and the keywords may be updated as the learning algorithm improves.

In a language, the Lexicon of a language describes the collection of words and phrases. This analysis involves identifying and analyzing the structure of words. It includes the general knowledge about the structure of the world and what each language user must know about the other user’s beliefs and goals. Discourse language concerns inter-sentential links that is how the immediately preceding sentences affect the interpretation of the next sentence. The above image is an example of the relationship between hyponyms and hypernym.

Related terms:

Lexical analysis is the process of identifying and categorizing lexical items in a text or speech. It is a fundamental step for NLP and AI, as it helps machines recognize and interpret the words and phrases that humans use. Lexical analysis involves tasks such as tokenization, lemmatization, stemming, part-of-speech tagging, named entity recognition, and sentiment analysis. The basic units of lexical semantics are words and phrases, also known as lexical items.

This article is part of an ongoing blog series on Natural Language Processing (NLP). I hope after reading that article you can understand the power of NLP in Artificial Intelligence. So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis. Prestructuralist semantics – apart from coining the term onomasiology itself (Zauner, 1902) – has introduced some of the basic terminology for describing lexicogenetic mechanisms.

Representing variety at the lexical level

In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text. Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text. Use of computer applications to translate text or speech from one natural language to another.

Without going into detail (for a full treatment, see Geeraerts, 1993), let us illustrate the first type of problem. In the case of autohyponymous words, for instance, the definitional approach does not reveal an ambiguity, whereas the truth-theoretical criterion does. Dog is autohyponymous between the readings ‘Canis familiaris,’ contrasting with cat or wolf, and ‘male Canis familiaris,’ contrasting with bitch.

Classic NLP is dead — Next Generation of Language Processing is Here

Semantics Analysis is a crucial part of Natural Language Processing (NLP). In the ever-expanding era of textual information, it is important for organizations to draw insights from such data to fuel businesses. Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts. Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text. Pragmatic analysis helps users to discover this intended effect by applying a set of rules that characterize cooperative dialogues. This phase scans the source code as a stream of characters and converts it into meaningful lexemes.

It is used in applications, such as mobile, home automation, video recovery, dictating to Microsoft Word, voice biometrics, voice user interface, and so on. In Case Grammar, case roles can be defined to link certain kinds of verbs and objects. Collectively, the three concepts of Lexical Normalization, Lexical Disambiguation, and Bilingual Lexicons are often referred to as Lexical Processing or Lexical Semantics.

Second, linguistic tests involve syntactic rather than semantic intuitions. Specifically, they are based on acceptability judgments about sentences that contain two related occurrences of the item under consideration (one of which may be implicit). If the grammatical relationship between both occurrences requires their semantic identity, the resulting sentence may be an indication for the polysemy of the item. For instance, the so-called identity test involves ‘identity-of-sense anaphora.’ Thus, at midnight the ship passed the port, and so did the bartender is awkward if the two lexical meanings of port are at stake.

In (17b), the event is in the door being opened and Sally may or may not have opened it previously. To render these two different meanings, “again” attaches to VPs in two different places, and thus describes two events with a purely structural change. Beck and Johnson show that the object in (15a) has a different relation to the motion verb as it is not able to carry the meaning of HAVING which the possessor (9a) and (15a) can. In (15a), Satoshi is an animate possessor and so is caused to HAVE kisimen.

Lexical Semantics

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• Lexical Ambiguity exists in the presence of two or more possible meanings of the sentence within a single word.
• Warrington and Cipolotti (1996) define semantic memory as “a system which processes, stores and retrieves information about the meaning of words, objects, facts and concepts” (p. 611).
• The accuracy of the summary depends on a machine’s ability to understand language data.
• The degree of morphology’s influence on overall grammar remains controversial.[12] Currently, the linguists that perceive one engine driving both morphological items and syntactic items are in the majority.
• Prototypical categories cannot be defined by means of a single set of criterial (necessary and sufficient) attributes.