Its the Meaning That Counts: The State of the Art in NLP and Semantics KI Künstliche Intelligenz

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 semantics nlp 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.

But question-answering systems still get poor results for questions that require drawing inferences from documents or interpreting figurative language. Just identifying the successive locations of an entity throughout an event described in a document is a difficult computational task. Semantic processing can be a precursor to later processes, such as question answering or knowledge acquisition (i.e., mapping unstructured content into structured content), which may involve additional processing to recover additional indirect (implied) aspects of meaning. Semantics gives a deeper understanding of the text in sources such as a blog post, comments in a forum, documents, group chat applications, chatbots, etc. With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text. Syntactic analysis (syntax) and semantic analysis (semantic) are the two primary techniques that lead to the understanding of natural language.

Semantic Analysis Is Part of a Semantic System

Every type of communication — be it a tweet, LinkedIn post, or review in the comments section of a website — may contain potentially relevant and even valuable information that companies must capture and understand to stay ahead of their competition. Capturing the information is the easy part but understanding what is being said (and doing this at scale) is a whole different story. With its ability to quickly process large data sets and extract insights, NLP is ideal for reviewing candidate resumes, generating financial reports and identifying patients for clinical trials, among many other use cases across various industries. These two sentences mean the exact same thing and the use of the word is identical. Below is a parse tree for the sentence “The thief robbed the apartment.” Included is a description of the three different information types conveyed by the sentence.

• Second, we followed GL’s principle of using states, processes and transitions, in various combinations, to represent different Aktionsarten.
• 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.
• They often occurred in the During(E) phase of the representation, but that phase was not restricted to processes.
• Usually, relationships involve two or more entities such as names of people, places, company names, etc.
• Together is most general, used for co-located items; attached represents adhesion; and mingled indicates that the constituent parts of the items are intermixed to the point that they may not become unmixed.

The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach. “Automatic entity state annotation using the verbnet semantic parser,” in Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop (Lausanne), 123–132. This representation follows the GL model by breaking down the transition into a process and several states that trace the phases of the event.

Introduction to Natural Language Processing

In this chapter, we will first explain some sorts of basic binary composition functions in both the semantic vector space and matrix-vector space. After, we will climb up to more complex composition scenarios and introduce several approaches to model sentence-level composition. Introducing consistency in the predicate structure was a major goal in this aspect of the revisions. In Classic VerbNet, the basic predicate structure consisted of a time stamp (Start, During, or End of E) and an often inconsistent number of semantic roles. The time stamp pointed to the phase of the overall representation during which the predicate held, and the semantic roles were taken from a list that included thematic roles used across VerbNet as well as constants, which refined the meaning conveyed by the predicate.

For example, there are an infinite number of different ways to arrange words in a sentence. Also, words can have several meanings and contextual information is necessary to correctly interpret sentences. Just take a look at the following newspaper headline “The Pope’s baby steps on gays.” This sentence clearly has two very different interpretations, which is a pretty good example of the challenges in natural language processing. Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language.

Tasks involved in Semantic Analysis

Authority_relationship shows a stative relationship dynamic between animate participants, while has_organization_role shows a stative relationship between an animate participant and an organization. Lastly, work allows a task-type role to be incorporated into a representation (he worked on the Kepler project). A second, non-hierarchical organization (Appendix C) groups together predicates that relate to the same semantic domain and defines, where applicable, the predicates’ relationships to one another.

Lexis relies first and foremost on the GL-VerbNet semantic representations instantiated with the extracted events and arguments from a given sentence, which are part of the SemParse output (Gung, 2020)—the state-of-the-art VerbNet neural semantic parser. In addition, it relies on the semantic role labels, which are also part of the SemParse output. The state change types Lexis was designed to predict include change of existence (created or destroyed), and change of location. The utility of the subevent structure representations was in the information they provided to facilitate entity state prediction. This information includes the predicate types, the temporal order of the subevents, the polarity of them, as well as the types of thematic roles involved in each.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. • Predicates consistently used across classes and hierarchically related for flexible granularity. This article does not contain any studies with human or animal subjects performed by any of the authors.

Based on this function, one could apply it on a word sequence recursively and derive sentence-level composition. Here a word sequence could be any level of the semantic units, such as a phrase, a sentence, a paragraph, a knowledge entity, or even a document. 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. We strove to be as explicit in the semantic designations as possible while still ensuring that any entailments asserted by the representations applied to all verbs in a class. Occasionally this meant omitting nuances from the representation that would have reflected the meaning of most verbs in a class.

For example, the stem for the word “touched” is “touch.” “Touch” is also the stem of “touching,” and so on. Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) have not been needed anymore. Although rule-based systems for manipulating symbols were still in use in 2020, they have become mostly obsolete with the advance of LLMs in 2023. “Class-based construction of a verb lexicon,” in AAAI/IAAI (Austin, TX), 691–696. ” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics), 7436–7453.

• As we worked toward a better and more consistent distribution of predicates across classes, we found that new predicate additions increased the potential for expressiveness and connectivity between classes.
• To improve its representation ability, the RNN could be enhanced as bi-directional RNN by considering sequential and reverse-sequential information.
• Here a word sequence could be any level of the semantic units, such as a phrase, a sentence, a paragraph, a knowledge entity, or even a document.
• VerbNet is also somewhat similar to PropBank and Abstract Meaning Representations (AMRs).
• For the purposes of illustration, we will consider the mappings from phrase types to frame expressions provided by Graeme Hirst[30] who was the first to specify a correspondence between natural language constituents and the syntax of a frame language, FRAIL[31].