Understanding Frame Semantic Parsing in NLP by Arie Pratama Sutiono

Whether it is Siri, Alexa, or Google, they can all understand human language (mostly). Today we will be exploring how some of the latest developments in NLP (Natural Language Processing) can make it easier for us to process and analyze text. The Basics of Syntactic Analysis Before understanding syntactic analysis in NLP, we must first understand Syntax. Natural language processing (NLP) for Arabic text involves tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition, among others….

The next stage involved developing representations for classes that primarily dealt with states and processes. Because our representations for change events necessarily included state subevents and often included process subevents, we had already developed principles for how to represent states and processes. Once our fundamental structure was established, we adapted these basic representations to events that included more event participants, such as Instruments and Beneficiaries. We applied them to all frames in the Change of Location, Change of State, Change of Possession, and Transfer of Information classes, a process that required iterative refinements to our representations as we encountered more complex events and unexpected variations. Other classes, such as Other Change of State-45.4, contain widely diverse member verbs (e.g., dry, gentrify, renew, whiten). It is interesting to note that popular Deep Learning (DL) approach to NLP/NLU almost never works sufficiently well for specific data domains.

Significance of Semantics Analysis

Some search engine technologies have explored implementing question answering for more limited search indices, but outside of help desks or long, action-oriented content, the usage is limited. Tasks like sentiment analysis can be useful in some contexts, but search isn’t one of them. When there are multiple content types, federated search can perform admirably by showing multiple search results in a single UI at the same time. Most search engines only have a single content type on which to search at a time.

I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet. Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to help computers understand language as well as we do. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more. In this post, we’ll cover the basics of natural language processing, dive into some of its techniques and also learn has benefited from recent advances in deep learning. As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals.

How Decision Intelligence Solutions Mitigate Poor Data Quality

Stanford CoreNLP is a suite of NLP tools that can perform tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Customized semantic analysis for specific domains, such as legal, healthcare, or finance, will become increasingly prevalent. Tailoring NLP models to understand the intricacies of specialized terminology and context is a growing trend. Real-time semantic analysis will become essential in applications like live chat, voice assistants, and interactive systems.

Regardless of the specific syntax of configuration the grammar is typically defined as a collection of semantic entities where each entity at minimum has a name and a list of synonyms by which this entity can be recognized. That ability to group individual words into high-level semantic entities was introduced to aid in solving a key problem plaguing the early NLP systems — namely a linguistic ambiguity. Although they did not explicitly mention semantic search in their original GPT-3 paper, OpenAI did release a GPT-3 semantic search REST API . While the specific details of the implementation are unknown, we assume it is something akin to the ideas mentioned so far, likely with the Bi-Encoder or Cross-Encoder paradigm.

By far the most common event types were the first four, all of which involved some sort of change to one or more participants in the event. We developed a basic first-order-logic representation that was consistent with the GL theory of subevent structure and that could be adapted for the various types of change events. We preserved existing semantic predicates where possible, but more fully defined them and their arguments and applied them consistently across classes. In this first stage, we decided on our system of subevent sequencing and developed new predicates to relate them. We also defined our event variable e and the variations that expressed aspect and temporal sequencing. At this point, we only worked with the most prototypical examples of changes of location, state and possession and that involved a minimum of participants, usually Agents, Patients, and Themes.

Natural language processing can help customers book tickets, track orders and even recommend similar products on e-commerce websites. Teams can also use data on customer purchases to inform what types of products to stock up on and when to replenish inventories. Keeping the advantages of natural language processing in mind, let’s explore how different industries are applying this technology.

Universal vs. Domain Specific

Finally, the Dynamic Event Model’s emphasis on the opposition inherent in events of change inspired our choice to include pre- and post-conditions of a change in all of the representations of events involving change. Previously in VerbNet, an event like “eat” would often begin the representation at the during(E) phase. This type of structure made it impossible to be explicit about the opposition between an entity’s initial state and its final state. It also made the job of tracking participants across subevents much more difficult for NLP applications.

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