2106 08117 Semantic Representation and Inference for NLP

When there are multiple content types, federated search can perform admirably by showing multiple search results in a single UI at the same time. Another way that named entity recognition can help with search quality is by moving the task from query time semantic nlp to ingestion time (when the document is added to the search index). While NLP is all about processing text and natural language, NLU is about understanding that text. Whether it is Siri, Alexa, or Google, they can all understand human language (mostly).

And if we want to know the relationship of or between sentences, we train a neural network to make those decisions for us. Semantics, the study of meaning, is central to research in Natural Language Processing (NLP) and many other fields connected to Artificial Intelligence. Nevertheless, how semantics is understood in NLP ranges from traditional, formal linguistic definitions based on logic and the principle of compositionality to more applied notions based on grounding meaning in real-world objects and real-time interaction. We review the state of computational semantics in NLP and investigate how different lines of inquiry reflect distinct understandings of semantics and prioritize different layers of linguistic meaning. In conclusion, we identify several important goals of the field and describe how current research addresses them.

Sentiment analysis

We attempted to replace these with combinations of predicates we had developed for other classes or to reuse these predicates in related classes we found. 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. 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.

Thus, semantic analysis is the study of the relationship between various linguistic utterances and their meanings, but pragmatic analysis is the study of context which influences our understanding of linguistic expressions. Pragmatic analysis helps users to uncover the intended meaning of the text by applying contextual background knowledge. Semantic analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context. It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software.

Basic Units of Semantic System:

Figure 5.15 includes examples of DL expressions for some complex concept definitions. As an example, for the sentence “The water forms a stream,”2, SemParse automatically generated the semantic representation in (27). In this case, SemParse has incorrectly identified the water as the Agent rather than the Material, but, crucially for our purposes, the Result is correctly identified as the stream. The fact that a Result argument changes from not being (¬be) to being (be) enables us to infer that at the end of this event, the result argument, i.e., “a stream,” has been created. The classes using the organizational role cluster of semantic predicates, showing the Classic VN vs. VN-GL representations. In thirty classes, we replaced single predicate frames (especially those with predicates found in only one class) with multiple predicate frames that clarified the semantics or traced the event more clearly.

Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text. Seunghak et al. [158] designed a Memory-Augmented-Machine-Comprehension-Network (MAMCN) to handle dependencies faced in reading comprehension. The model achieved state-of-the-art performance on document-level using TriviaQA and QUASAR-T datasets, and paragraph-level using SQuAD datasets. There is a system called MITA (Metlife’s Intelligent Text Analyzer) (Glasgow et al. (1998) [48]) that extracts information from life insurance applications.

Our representations of accomplishments and achievements use these components to follow changes to the attributes of participants across discrete phases of the event. Expert.ai’s rule-based technology starts by reading all of the words within a piece of content to capture its real meaning. It then identifies the textual elements and assigns them to their logical and grammatical roles. Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context. Luong et al. [70] used neural machine translation on the WMT14 dataset and performed translation of English text to French text. The model demonstrated a significant improvement of up to 2.8 bi-lingual evaluation understudy (BLEU) scores compared to various neural machine translation systems.

• To represent this distinction properly, the researchers chose to “reify” the “has-parts” relation (which means defining it as a metaclass) and then create different instances of the “has-parts” relation for tendons (unshared) versus blood vessels (shared).
• In revising these semantic representations, we made changes that touched on every part of VerbNet.
• This article aims to give a broad understanding of the Frame Semantic Parsing task in layman terms.
• Creation predicates and accomplishments generally also encode predicate oppositions.
• As in any area where theory meets practice, we were forced to stretch our initial formulations to accommodate many variations we had not first anticipated.

Changes to the semantic representations also cascaded upwards, leading to adjustments in the subclass structuring and the selection of primary thematic roles within a class. To give an idea of the scope, as compared to VerbNet version 3.3.2, only seven out of 329—just 2%—of the classes have been left unchanged. Within existing classes, we have added 25 new subclasses and removed or reorganized 20 others. 88 classes have had their primary class roles adjusted, and 303 classes have undergone changes to their subevent structure or predicates. Our predicate inventory now includes 162 predicates, having removed 38, added 47 more, and made minor name adjustments to 21.

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It was believed that machines can be made to function like the human brain by giving some fundamental knowledge and reasoning mechanism linguistics knowledge is directly encoded in rule or other forms of representation. Statistical and machine learning entail evolution of algorithms that allow a program to infer patterns. An iterative process is used to characterize a given algorithm’s underlying algorithm that is optimized by a numerical measure that characterizes numerical parameters and learning phase. Machine-learning models can be predominantly categorized as either generative or discriminative. Generative methods can generate synthetic data because of which they create rich models of probability distributions.

Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. In finance, NLP can be paired with machine learning to generate financial reports based on invoices, statements and other documents. Financial analysts can also employ natural language processing to predict stock market trends by analyzing news articles, social media posts and other online sources for market sentiments. Understanding human language is considered a difficult task due to its complexity.