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

The y-axis represents the semantic similarity results, ranging from 0 to 100%. A higher value on the y-axis indicates a higher degree of semantic similarity between sentence pairs. We are exploring how to add slots for other new features in a class’s representations. Some already have roles or constants that could accommodate feature values, such as the admire class did with its Emotion constant. We are also working in the opposite direction, using our representations as inspiration for additional features for some classes.

Compiling this data can help marketing teams understand what consumers care about and how they perceive a business’ brand. While NLP-powered chatbots and callbots are most common in customer service contexts, companies have also relied on natural language processing to power virtual assistants. These assistants are a form of conversational AI that can carry on more sophisticated discussions.

Natural language processing

The observations regarding translation differences extend to other core conceptual words in The Analects, a subset of which is displayed in Table 9 due to space constraints. Translators often face challenges in rendering core concepts into alternative words or phrases while striving to maintain fidelity to the original text. Yet, even with the translators’ understanding of these core concepts, significant variations emerge in their specific word choices.

Whether translations adopt a simplified or literal approach, readers stand to benefit from understanding the structure and significance of ancient Chinese names prior to engaging with the text. Most proficient translators typically include detailed explanations of these core concepts and personal names either in the introductory or supplementary sections of their translations. If feasible, readers should consult multiple translations for cross-reference, especially when interpreting key conceptual terms and names. However, given the abundance of online resources, sourcing accurate and relevant information is convenient. Readers can refer to online resources like Wikipedia or academic databases such as the Web of Science. While this process may be time-consuming, it is an essential step towards improving comprehension of The Analects.

Elements of Semantic Analysis

We show examples of the resulting representations and explain the expressiveness of their components. Finally, we describe some recent studies that made use of the new representations to accomplish tasks in the area of computational semantics. 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).

Through the analysis of our semantic similarity calculation data, this study finds that there are some differences in the absolute values of the results obtained by the three algorithms. Several factors, such as the differing dimensions of semantic word vectors used by each algorithm, could contribute to these dissimilarities. Figure 1 primarily illustrates the performance of three distinct NLP algorithms in quantifying semantic similarity.

Statistical NLP (1990s–2010s)

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. A “stem” is the part of a word that remains after the removal of all affixes. For example, the stem for the word “touched” is “touch.” “Touch” is also the stem of “touching,” and so on. It is a complex system, although little children can learn it pretty quickly.

• Keeping the advantages of natural language processing in mind, let’s explore how different industries are applying this technology.
• The classes using the organizational role cluster of semantic predicates, showing the Classic VN vs. VN-GL representations.
• The analysis encompassed a total of 136,171 English words and 890 lines across all five translations.
• A class’s semantic representations capture generalizations about the semantic behavior of the member verbs as a group.

For this, we use a single subevent e1 with a subevent-modifying duration predicate to differentiate the representation from ones like (20) in which a single subevent process is unbounded. The long-awaited time when we can communicate with computers naturally-that is, with subtle, creative human language-has not yet arrived. We’ve come far from the days when computers could only deal with human language in simple, highly constrained situations, such as leading a speaker through a phone tree or finding documents based on key words.

Auto NLP

Often compared to the lexical resources FrameNet and PropBank, which also provide semantic roles, VerbNet actually differs from these in several key ways, not least of which is its semantic representations. Both FrameNet and VerbNet group verbs semantically, although VerbNet takes into consideration the syntactic regularities of the verbs as well. Both resources define semantic roles for these verb groupings, with VerbNet roles being fewer, more coarse-grained, and restricted to central participants in the events. What we are most concerned with here is the representation of a class’s (or frame’s) semantics. In FrameNet, this is done with a prose description naming the semantic roles and their contribution to the frame.

The lexical unit, in this context, is a pair of basic forms of a word (lemma) and a Frame. At frame index, a lexical unit will also be paired with its part of speech tag (such as Noun/n or Verb/v). I believe the purpose is to clearly state which meaning is this lemma refers to (One lemma/word that has multiple meanings is called polysemy). Frame element is a component of a semantic frame, specific for certain Frames. It means if you have seen the frame index you will notice there are highlighted words.

These are the frame elements, and each frame may have different types of frame elements. At first glance, it is hard to understand most terms in the reading materials. Healthcare professionals can develop more efficient workflows with the help of natural language processing.

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.

This study conduct triangulation method among three algorithms to ensure the robustness and reliability of the results. “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. nlp semantics 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.

Named Entity Recognition

Conversely, the outcomes of semantic similarity calculations falling below 80% constitute 1,973 sentence pairs, approximating 22% of the aggregate number of sentence pairs. Although this subset of sentence pairs represents a relatively minor proportion, it holds pivotal significance in impacting semantic representation amongst the varied translations, unveiling considerable semantic variances therein. To delve deeper into these disparities and their foundational causes, a more comprehensive and meticulous analysis is slated for the subsequent sections.

In revising these semantic representations, we made changes that touched on every part of VerbNet. Within the representations, we adjusted the subevent structures, number of predicates within a frame, and structuring and identity of predicates. 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.

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. The proposed test includes a task that involves the automated interpretation and generation of natural language. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.

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. Among the five translations, only a select number of sentences from Slingerland and Watson consistently retain identical sentence structure and word choices, as in Table 4. The three embedding models used to evaluate semantic similarity resulted in a 100% match for sentences NO. 461, 590, and 616. In other high-similarity sentence pairs, the choice of words is almost identical, with only minor discrepancies. However, as the semantic similarity between sentence pairs decreases, discrepancies in word selection and phraseology become more pronounced.

Grasping the unique characteristics of each translation is pivotal for guiding future translators and assisting readers in making informed selections. This research builds a corpus from translated texts of The Analects and quantifies semantic similarity at the sentence level, employing natural language processing algorithms such as Word2Vec, GloVe, and BERT. The findings highlight semantic variations among the five translations, subsequently categorizing them into “Abnormal,” “High-similarity,” and “Low-similarity” sentence pairs. This facilitates a quantitative discourse on the similarities and disparities present among the translations. Through detailed analysis, this study determined that factors such as core conceptual words, and personal names in the translated text significantly impact semantic representation.