Natural Language Understanding in a Semantic Web Context by Caroline Barrière
By Caroline Barrière
This publication serves as a place to begin for Semantic internet (SW) scholars and researchers drawn to getting to know what normal Language Processing (NLP) has to provide. NLP can successfully support discover the massive parts of knowledge held as unstructured textual content in normal language, hence augmenting the true content material of the Semantic internet in an important and lasting approach. The booklet covers the fundamentals of NLP, with a spotlight on ordinary Language figuring out (NLU), concerning semantic processing, details extraction and information acquisition, that are noticeable because the key hyperlinks among the SW and NLP groups. significant emphasis is put on mining sentences looking for entities and family. during this “quest", demanding situations might be encountered for numerous textual content research initiatives, together with part-of-speech tagging, parsing, semantic disambiguation, named entity popularity and relation extraction. commonplace algorithms linked to those initiatives are awarded to supply an figuring out of the elemental thoughts. in addition, the significance of experimental layout and consequence research is emphasised, and consequently, such a lot chapters contain small experiments on corpus information with quantitative and qualitative research of the results.
This ebook is split into 4 components. half I “Searching for Entities in textual content” is devoted to the quest for entities in textual facts. subsequent, half II “Working with Corpora” investigates corpora as important assets for NLP paintings. In flip, half III “Semantic Grounding and Relatedness” makes a speciality of the method of linking floor varieties present in textual content to entities in assets. eventually, half IV “Knowledge Acquisition” delves into the realm of relatives and relation extraction. The e-book additionally contains 3 appendices: “A check out the Semantic net” provides a quick assessment of the Semantic internet and is meant to convey readers much less conversant in the Semantic internet on top of things, in order that they can also totally enjoy the fabric of this booklet. “NLP instruments and systems” presents information regarding NLP structures and instruments, whereas “Relation Lists” gathers lists of family lower than diversified different types, exhibiting how family should be various and serve varied reasons. and eventually, the booklet incorporates a thesaurus of over 2 hundred phrases standard in NLP.
The ebook deals a helpful source for graduate scholars focusing on SW applied sciences and execs trying to find new instruments to enhance the applicability of SW options in way of life – or, in brief, all people seeking to find out about NLP with the intention to extend his or her horizons. It offers a wealth of knowledge for readers new to either fields, supporting them comprehend the underlying ideas and the demanding situations they could encounter.
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Extra info for Natural Language Understanding in a Semantic Web Context
2 Gazetteers One approach to finding named entities in text is to have lists of the individuals, often referred to as gazetteers. , in Wikipedia), we can find lists of just about anything imaginable: varieties of rice, car brands, romantic symphonies, countries, and so on. Let us take art museums as an example. X rdf:type dbr:Art_museum . } 26 3 Searching for Named Entities This list could easily become a gazetteer for an ArtMuseum entity type to be used for searches in text. 1 provides some examples.
That sentence contains the misspelled form Betthoven which was not included in the known variations. We will further explore misspelling errors in Chap. 4. For now, let us continue on our exploration of name variations. , garden, laptop computer). Similar to specific entities, the surface forms provided by naming predicates for generic entities are usually insufficient for searches in text. For example, if we want to know about mobile phones, the only label provided in DBpedia is Mobile phone. However, people tend to be very creative in how they refer to particular concepts.
Try another language of your choice and observe again. 5 (Use of pronouns). a. Take a news article at random from a newspaper of your choice. Find the pronouns. Do you find them easy to solve to earlier mentions? How would you quantify their use: frequent or infrequent? For a single explicit mention, are there many further anaphoric mentions? Chapter 3 Searching for Named Entities In the previous chapter, we searched for the specific composer Ludwig van Beethoven. But what if we wanted to find sentences about any classical music composer, or even more generally, about any composer?