2106 08117 Semantic Representation and Inference for NLP

Thus, the ability of a machine to overcome the ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense Disambiguation. Natural Language Processing (NLP) research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more. Named entity recognition (NER) concentrates on determining which items in a text (i.e. the “named entities”) can be located and classified into predefined categories. These categories can range from the names of persons, organizations and locations to monetary values and percentages.

The table presented above reveals marked differences in the translation of these terms among the five translators. These disparities can be attributed to a variety of factors, including the translators’ intended audience, the cultural context at the time of translation, and the unique strategies each translator employed to convey the essence of the original text. The term “君子 Jun Zi,” often translated as “gentleman” or “superior man,” serves as a typical example to further illustrate this point regarding the translation of core conceptual terms. 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. Question answering is an NLU task that is increasingly implemented into search, especially search engines that expect natural language searches.

The Total corpus of five translations

As we will describe briefly, GL’s event structure and its temporal sequencing of subevents solves this problem transparently, while maintaining consistency with the idea that the sentence describes a single matrix event, E. There is a growing realization among NLP experts that observations of form alone, without grounding in the referents it represents, can never lead to true extraction of meaning-by humans or computers (Bender and Koller, 2020). Another proposed solution-and one we hope to contribute to with our work-is to integrate logic or even explicit logical representations into distributional semantics and deep learning methods. We are encouraged by the efficacy of the semantic representations in tracking entity changes in state and location. We would like to see if the use of specific predicates or the whole representations can be integrated with deep-learning techniques to improve tasks that require rich semantic interpretations. In multi-subevent representations, ë conveys that the subevent it heads is unambiguously a process for all verbs in the class.

For some classes, such as the Put-9.1 class, the verbs are semantically quite coherent (e.g., put, place, situate) and the semantic representation is correspondingly precise 7. The goal of this subevent-based VerbNet representation was to facilitate inference and textual entailment tasks. Similarly, Table 1 shows the ESL of the verb arrive, compared with the semantic frame of the verb in classic VerbNet.

How Does Semantic Analysis Work?

A final pair of examples of change events illustrates the more subtle entailments we can specify using the new subevent numbering and the variations on the event variable. Changes of possession and transfers of information have very similar representations, with important differences in which entities have possession of the object or information, respectively, at the end of the event. In 15, the opposition between the Agent’s possession in e1 and non-possession in e3 of the Theme makes clear that once the Agent transfers the nlp semantic Theme, the Agent no longer possesses it. However, in 16, the E variable in the initial has_information predicate shows that the Agent retains knowledge of the Topic even after it is transferred to the Recipient in e2. 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.

When there are multiple content types, federated search can perform admirably by showing multiple search results in a single UI at the same time. It takes messy data (and natural language can be very messy) and processes it into something that computers can work with. NLP and NLU make semantic search more intelligent through tasks like normalization, typo tolerance, and entity recognition. “Integrating generative lexicon event structures into verbnet,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (Miyazaki), 56–61. ” At the moment, the most common approach to this problem is for certain people to read thousands of articles and keep  this information in their heads, or in workbooks like Excel, or, more likely, nowhere at all. Clearly, then, the primary pattern is to use NLP to extract structured data from text-based documents.

Combining these two technologies enables structured and unstructured data to merge seamlessly. VerbNet’s semantic representations, however, have suffered from several deficiencies that have made them difficult to use in NLP applications. To unlock the potential in these representations, we have made them more expressive and more consistent across classes of verbs. We have grounded them in the linguistic theory of the Generative Lexicon (GL) (Pustejovsky, 1995, 2013; Pustejovsky and Moszkowicz, 2011), which provides a coherent structure for expressing the temporal and causal sequencing of subevents. Explicit pre- and post-conditions, aspectual information, and well-defined predicates all enable the tracking of an entity’s state across a complex event. As discussed above, as a broad coverage verb lexicon with detailed syntactic and semantic information, VerbNet has already been used in various NLP tasks, primarily as an aid to semantic role labeling or ensuring broad syntactic coverage for a parser.

With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text. Semantics, the study of meaning, is central to research in Natural Language Processing (NLP) and many other fields connected to Artificial Intelligence. 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.

In this field, professionals need to keep abreast of what’s happening across their entire industry. Most information about the industry is published in press releases, news stories, and the like, and very little of this information is encoded in a highly structured way. However, most information about one’s own business will be represented in structured databases internal to each specific organization. Finally, NLP technologies typically map the parsed language onto a domain model.

For example, representations pertaining to changes of location usually have motion(ë, Agent, Trajectory) as a subevent. Second, we followed GL’s principle of using states, processes and transitions, in various combinations, to represent different Aktionsarten. We use E to represent states that hold throughout an event and ën to represent processes. Transitions are en, as are states that hold for only part of a complex event. These can usually be distinguished by the type of predicate-either a predicate that brings about change, such as transfer, or a state predicate like has_location. Our representations of accomplishments and achievements use these components to follow changes to the attributes of participants across discrete phases of the event.

One of the downstream NLP tasks in which VerbNet semantic representations have been used is tracking entity states at the sentence level (Clark et al., 2018; Kazeminejad et al., 2021). Entity state tracking is a subset of the greater machine reading comprehension task. The goal is to track the changes in states of entities within a paragraph (or larger unit of discourse). This change could be in location, internal state, or physical state of the mentioned entities. For instance, a Question Answering system could benefit from predicting that entity E has been DESTROYED or has MOVED to a new location at a certain point in the text, so it can update its state tracking model and would make correct inferences.

• Grammatical rules are applied to categories and groups of words, not individual words.
• Likewise, the word ‘rock’ may mean ‘a stone‘ or ‘a genre of music‘ – hence, the accurate meaning of the word is highly dependent upon its context and usage in the text.
• Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language.
• This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes.
• Expert.ai’s rule-based technology starts by reading all of the words within a piece of content to capture its real meaning.

By integrating insights from previous translators and leveraging paratextual information, future translators can provide more precise and comprehensive explanations of core concepts and personal names, thus enriching readers’ understanding of these terms. For translators, in the process of translating The Analects, it is crucial to accurately convey core conceptual terms and personal names, utilizing relevant vocabulary and providing pertinent supplementary information in the para-text. The author advocates for a compensatory approach in translating core conceptual words and personal names.