A semantics-aware approach for multilingual natural language inference Language Resources and Evaluation

It also allows the reader or listener to connect what the language says with what they already know or believe. The first major change to this representation was that path_rel was replaced by a series of more specific predicates depending on what kind of change was underway. Here, it was replaced by has_possession, which is now defined as “A participant has possession of or control over a Theme or Asset.” It has three fixed argument slots of which the first is a time stamp, the second is the possessing entity, and the third is the possessed entity. These slots are invariable semantics nlp across classes and the two participant arguments are now able to take any thematic role that appears in the syntactic representation or is implicitly understood, which makes the equals predicate redundant. It is now much easier to track the progress of a single entity across subevents and to understand who is initiating change in a change predicate, especially in cases where the entity called Agent is not listed first. 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.

This article is part of an ongoing blog series on Natural Language Processing (NLP). I hope after reading that article you can understand the power of NLP in Artificial Intelligence. So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis. However, many organizations struggle to capitalize on it because of their inability to analyze unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes.

A semantics-aware approach for multilingual natural language inference

Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks. 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. Although its coverage of English vocabulary is not complete, it does include over 6,600 verb senses. We were not allowed to cherry-pick examples for our semantic patterns; they had to apply to every verb and every syntactic variation in all VerbNet classes. The classes using the organizational role cluster of semantic predicates, showing the Classic VN vs. VN-GL representations. We have organized the predicate inventory into a series of taxonomies and clusters according to shared aspectual behavior and semantics.

• 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.
• We are encouraged by the efficacy of the semantic representations in tracking entity changes in state and location.
• And if NLP is unable to resolve an issue, it can connect a customer with the appropriate personnel.
• As discussed in previous articles, NLP cannot decipher ambiguous words, which are words that can have more than one meaning in different contexts.
• This study also highlights the weakness and the limitations of the study in the discussion (Sect. 4) and results (Sect. 5).

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 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 SNePS framework has been used to address representations of a variety of complex quantifiers, connectives, and actions, which are described in The SNePS Case Frame Dictionary and related papers. SNePS also included a mechanism for embedding procedural semantics, such as using an iteration mechanism to express a concept like, “While the knob is turned, open the door”. Description logics separate the knowledge one wants to represent from the implementation of underlying inference. Inference services include asserting or classifying objects and performing queries.

Natural language processing

Syntax is the grammatical structure of the text, whereas semantics is the meaning being conveyed. A sentence that is syntactically correct, however, is not always semantically correct. For example, “cows flow supremely” is grammatically valid (subject — verb — adverb) but it doesn’t make any sense. Another remarkable thing about human language is that it is all about symbols. According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system. This means we can convey the same meaning in different ways (i.e., speech, gesture, signs, etc.) The encoding by the human brain is a continuous pattern of activation by which the symbols are transmitted via continuous signals of sound and vision.

It also meant that classes with a clear semantic characteristic, such as the type of emotion of the Experiencer in the admire-31.2 class, could only generically refer to this characteristic, leaving unexpressed the specific value of that characteristic for each verb. Fire-10.10 and Resign-10.11 formerly included nothing but two path_rel(CH_OF_LOC) predicates plus cause, in keeping with the basic change of location format utilized throughout the other -10 classes. This representation was somewhat misleading, since translocation is really only an occasional side effect of the change that actually takes place, which is the ending of an employment relationship. 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.

Syntactic and Semantic Analysis

One can distinguish the name of a concept or instance from the words that were used in an utterance. 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. The richer and more coherent representations described in this article offer opportunities for additional types of downstream applications that focus more on the semantic consequences of an event. However, the clearest demonstration of the coverage and accuracy of the revised semantic representations can be found in the Lexis system (Kazeminejad et al., 2021) described in more detail below. The Escape-51.1 class is a typical change of location class, with member verbs like depart, arrive and flee. The most basic change of location semantic representation (12) begins with a state predicate has_location, with a subevent argument e1, a Theme argument for the object in motion, and an Initial_location argument.

• Insurance companies can assess claims with natural language processing since this technology can handle both structured and unstructured data.
• In this chapter, we will first explain some sorts of basic binary composition functions in both the semantic vector space and matrix-vector space.
• 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.
• In_reaction_to(e1, Stimulus) should be understood to mean that subevent e1 occurs as a response to a Stimulus.
• “Annotating lexically entailed subevents for textual inference tasks,” in Twenty-Third International Flairs Conference (Daytona Beach, FL), 204–209.

Other classes, such as Other Change of State-45.4, contain widely diverse member verbs (e.g., dry, gentrify, renew, whiten). A class’s semantic representations capture generalizations about the semantic behavior of the member verbs as a group. 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.

The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Graphs can also be more expressive, while preserving the sound inference of logic.

The entities involved in this text, along with their relationships, are shown below. Ontology editing tools are freely available; the most widely used is Protégé, which claims to have over 300,000 registered users. In Sentiment analysis, our aim is to detect the emotions as positive, negative, or neutral in a text to denote urgency.

In this chapter, we first introduce the semantic space for compositional semantics. Afterwards, we take phrase representation as an example to introduce various models for binary semantic composition, including additive models and multiplicative models. Finally, we introduce typical models for N-ary semantic composition including recurrent neural network, recursive neural network, and convolutional neural network. We will give a detailed introduction to these scenarios in the following chapters. Despite impressive advances in NLU using deep learning techniques, human-like semantic abilities in AI remain out of reach.

There is no notion of implication and there are no explicit variables, allowing inference to be highly optimized and efficient. Instead, inferences are implemented using structure matching and subsumption among complex concepts. One concept will subsume all other concepts that include the same, or more specific versions of, its constraints. These processes are made more efficient by first normalizing all the concept definitions so that constraints appear in a  canonical order and any information about a particular role is merged together. These aspects are handled by the ontology software systems themselves, rather than coded by the user.

Fourth, word sense discrimination determines what words senses are intended for tokens of a sentence. Discriminating among the possible senses of a word involves selecting a label from a given set (that is, a classification task). Alternatively, one can use a distributed representation of words, which are created using vectors of numerical values that are learned to accurately predict similarity and differences among words. This information is determined by the noun phrases, the verb phrases, the overall sentence, and the general context. The background for mapping these linguistic structures to what needs to be represented comes from linguistics and the philosophy of language.

It involves words, sub-words, affixes (sub-units), compound words, and phrases also. In real-world NLP tasks, the input is usually a sequence of multiple words rather than just a pair of words. Therefore, besides designing a suitable binary compositional operator, the order to apply binary operations is also important. In this section, we will introduce three mainstream strategies in N-ary composition by taking language modeling as an example. 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.

On average of the three strong models, our semantic-aware approach improves natural language inference in different languages. Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding. Your phone basically understands what you have said, but often can’t do anything with it because it doesn’t understand the meaning behind it. Also, some of the technologies out there only make you think they understand the meaning of a text.