Semantics and Semantic Interpretation Principles of Natural Language Processing

In other words, we can say that polysemy has the same spelling but different and related meanings. In this component, we combined the individual words to provide meaning in sentences. Understanding these terms is crucial to NLP programs that seek to draw insight from textual information, extract information and provide data. It is also essential for automated processing and question-answer systems like chatbots. You can find out what a group of clustered words mean by doing principal component analysis (PCA) or dimensionality reduction with T-SNE, but this can sometimes be misleading because they oversimplify and leave a lot of information on the side. It’s a good way to get started (like logistic or linear regression in data science), but it isn’t cutting edge and it is possible to do it way better.

We are exploring how to add slots for other new features in a class’s representations. Some already have roles or constants that could accommodate feature values, such as the admire class did with its Emotion constant. We are also working in the opposite direction, using our representations as inspiration for additional features for some classes. The compel-59.1 class, for example, now has a manner predicate, with a V_Manner role that could be replaced with a verb-specific value. The verbs of the class split primarily between verbs with a compel connotation of compelling (e.g., oblige, impel) and verbs with connotation of persuasion (e.g., sway, convince) These verbs could be assigned a +compel or +persuade value, respectively.

Statistical NLP (1990s–2010s)

By default, every DL ontology contains the concept “Thing” as the globally superordinate concept, meaning that all concepts in the ontology are subclasses of “Thing”. [ALL x y] where x is a role and y is a concept, refers to the subset of all individuals x such that if the pair is in the role relation, then y is in the subset corresponding to the description. [EXISTS n x] where n is an integer is a role refers to the subset of individuals x where at least n pairs are in the role relation. [FILLS x y] where x is a role and y is a constant, refers to the subset of individuals x, where the pair x and the interpretation of the concept is in the role relation. [AND x1 x2 ..xn] where x1 to xn are concepts, refers to the conjunction of subsets corresponding to each of the component concepts. Figure 5.15 includes examples of DL expressions for some complex concept definitions.

This analysis gives the power to computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying the relationships between individual words of the sentence in a particular context. This degree of language understanding can help companies automate even the most complex language-intensive processes and, in doing so, transform the way they do business. So the question is, why settle for an educated guess when you can rely on actual knowledge? This is a key concern for NLP practitioners responsible for the ROI and accuracy of their NLP programs.

3.2 Compositionality in Logic-Based Representations

Machine learning side-stepped the rules and made great progress on foundational NLP tasks such as syntactic parsing. When they hit a plateau, more linguistically oriented features were brought in to boost performance. Additional processing such as entity type recognition and semantic role labeling, based on linguistic theories, help considerably, but semantics nlp they require extensive and expensive annotation efforts. Deep learning left those linguistic features behind and has improved language processing and generation to a great extent. However, it falls short for phenomena involving lower frequency vocabulary or less common language constructions, as well as in domains without vast amounts of data.

Sometimes a thematic role in a class refers to an argument of the verb that is an eventuality. Because it is sometimes important to describe relationships between eventualities that are given as subevents and those that are given as thematic roles, we introduce as our third type subevent modifier predicates, for example, in_reaction_to(e1, Stimulus). Here, as well as in subevent-subevent relation predicates, the subevent variable in the first argument slot is not a time stamp; rather, it is one of the related parties. In_reaction_to(e1, Stimulus) should be understood to mean that subevent e1 occurs as a response to a Stimulus.