Understanding Semantic Analysis Using Python - NLP Towards AI

Whether that movement toward one end of the recall-precision spectrum is valuable depends on the use case and the search technology. It isn’t a question of applying all normalization techniques but deciding which ones provide the best balance of precision and recall. Conversely, a search engine could have 100% recall by only returning documents that it knows to be a perfect fit, but sit will likely miss some good results. For example, to require a user to type a query in exactly the same format as the matching words in a record is unfair and unproductive. With these two technologies, searchers can find what they want without having to type their query exactly as it’s found on a page or in a product. Some are centered directly on the models and their outputs, others on second-order concerns, such as who has access to these systems, and how training them impacts the natural world.

Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines. 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. The compel-59.1 class, for example, now has a manner predicate, with a V_Manner role that could be replaced with a verb-specific value.

ORIGINAL RESEARCH article

When appropriate, however, more specific predicates can be used to specify other relationships, such as meets(e2, e3) to show that the end of e2 meets the beginning of e3, or co-temporal(e2, e3) to show that e2 and e3 occur simultaneously. The latter can be seen in Section 3.1.4 with the example of accompanied motion. This paper introduces a semantics-aware approach to natural language inference which allows neural network models to perform better on natural language inference benchmarks. We propose to incorporate explicit lexical and concept-level semantics from knowledge bases to improve inference accuracy. We conduct an extensive evaluation of four models using different sentence encoders, including continuous bag-of-words, convolutional neural network, recurrent neural network, and the transformer model.

NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis. Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud, determining customer sentiment, and optimizing aircraft maintenance — and nlp semantic customer-facing, like Google Translate. 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.

Tasks involved in Semantic Analysis

This research aims to enrich readers’ holistic understanding of The Analects by providing valuable insights. Additionally, this research offers pragmatic recommendations and strategies to future translators embarking on this seminal work. 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.

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. We have added 3 new classes and subsumed two others into existing classes. 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.

Applications

A clear example of that utility of VerbNet semantic representations in uncovering implicit information is in a sentence with a verb such as “carry” (or any verb in the VerbNet carry-11.4 class for that matter). If we have ◂ X carried Y to Z▸, we know that by the end of this event, both Y and X have changed their location state to Z. This is not recoverable even if we know that “carry” is a motion event (and therefore has a theme, source, and destination). This is in contrast to a “throw” event where only the theme moves to the destination and the agent remains in the original location. Such semantic nuances have been captured in the new GL-VerbNet semantic representations, and Lexis, the system introduced by Kazeminejad et al., 2021, has harnessed the power of these predicates in its knowledge-based approach to entity state tracking.

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. This is an optional last step where bert_model is unfreezed and retrained

with a very low learning rate. This can deliver meaningful improvement by

incrementally adapting the pretrained features to the new data. The model should take at least, the tokens, lemmas, part of speech tags, and the target position, a result of an earlier task.

Entity Extraction

It can be in the form of tasks, such as word sense disambiguation, co-reference resolution, or lemmatization. There are terms for the attributes of each task, for example, lemma, part of speech tag (POS tag), semantic role, and phoneme. Now, we have a brief idea of meaning representation that shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation.

Natural language processing brings together linguistics and algorithmic models to analyze written and spoken human language. Based on the content, speaker sentiment and possible intentions, NLP generates an appropriate response. In machine translation done by deep learning algorithms, language is translated by starting with a sentence and generating vector representations that represent it. Then it starts to generate words in another language that entail the same information. With sentiment analysis we want to determine the attitude (i.e. the sentiment) of a speaker or writer with respect to a document, interaction or event.

Approaches to Meaning Representations

A higher value on the y-axis indicates a higher degree of semantic similarity between sentence pairs. All these models aim to provide numerical representations of words that capture their meanings. This study obtains high-resolution PDF versions of the five English translations of The Analects through purchase and download.

This article does not contain any studies with human participants performed by any of the authors. Since each translation contains 890 sentences, pairing the five translations produces 10 sets of comparison results, totaling 8900 average results. You could imagine using translation to search multi-language corpuses, but it rarely happens in practice, and is just as rarely needed. One thing that we skipped over before is that words may not only have typos when a user types it into a search bar. Nearly all search engines tokenize text, but there are further steps an engine can take to normalize the tokens. The meanings of words don’t change simply because they are in a title and have their first letter capitalized.

In this post, we’ll cover the basics of natural language processing, dive into some of its techniques and also learn how NLP has benefited from recent advances in deep learning. This study ingeniously integrates natural language processing technology into translation research. The semantic similarity calculation model utilized in this study can also be applied to other types of translated texts. Translators can employ this model to compare their translations degree of similarity with previous translations, an approach that does not necessarily mandate a higher similarity to predecessors. This allows them to better realize the purpose and function of translation while assessing translation quality. Understanding human language is considered a difficult task due to its complexity.

The translation of The Analects contains several common words, often referred to as “stop words” in the field of Natural Language Processing (NLP). These words, such as “the,” “to,” “of,” “is,” “and,” and “be,” are typically filtered out during data pre-processing due to their high frequency and low semantic weight. Similarly, words like “said,” “master,” “never,” and “words” appear consistently across all five translations. However, despite their recurrent appearance, these words are considered to have minimal practical significance within the scope of our analysis. This is primarily due to their ubiquity and the negligible unique semantic contribution they make. For these reasons, this study excludes these two types of words-stop words and high-frequency yet semantically non-contributing words from our word frequency statistics.

All of the rest have been streamlined for definition and argument structure. This study employs sentence alignment to construct a parallel corpus based on five English translations of The Analects. Subsequently, this study applied Word2Vec, GloVe, and BERT to quantify the semantic similarities among these translations. The similarities and dissimilarities among these five translations were evaluated based on the resulting similarity scores. The Jennings’ translation considered the readability of the text and restructured the original text, which was a very reader-friendly innovation at the time.