Named entities are typically multiword expressions that denote proper and geographical names, names of institutions, companies, etc. The main challenge here is finding out that the respective group of words represents a single entity.
By "semantic role labeling" you may mean a task called "word sense disambiguation", i.e. assigning a particular sense of a polysemous word according to the respective context.
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The answer above about NER is correct: it is the task of finding spans of text referring to named entities (people, places, organizations, ...) and classifying them into those categories (e.g. recognizing that Inter is an instance of ORG in "Intel did not go bankrupt").
Semantic Role Labeling (SRL) is something else, and different from word sense disambiguation: it is the task of assigning a semantic role, such as agent or patient, to the arguments of a predicate. For example, the agent of 'read' (the reader) can be expressed as a subject ("Kim was reading") or a passive by-phrase ("read by Kim"). In SRL, Kim would be labeled as the agent (sometimes called ARG-0) of both instances of 'read'.
Thank you very much. That was a great help. I also want to know, when do we use named entity recognition and when to use semantic role labeling. Because, while named entity can directly give us the instance of a particular word, why at all we need semantic role of the word.
They are rather different things, but both have their uses. If you're thinking about information extraction, NER allows us to say which people or places are mentioned in which documents, how often, etc. SRL can tell us who all agents of a predicate like 'sell' are in a financial data set, for example. They are also useful for research on how people formulate specific relations, and they can be used for Natural Language Generation as well.
I have few questions related to parsers in NLP
Named Entity Recognition (NER) identifies mentioned names such as person, place or organization from general text and gene/protein, drug, disease etc. from biomedical literature. In order to do so NER system should be developed by considering both syntax and semantics of the sentence or sentences (anaphora) in both local and global context.
Semantic Role Labeling focuses only the semantics, to assign different roles like agent, role, predicate or verb etc. in a sentence. Here not only NER (for agent, recipient) but also other mentions like theme or verb will be labeled by considering the semantics of the sentence.
Named entity recognition (NER) ‒ also called entity identification or entity extraction ‒ is an information extraction technique that automatically identifies named entities in a text and classifies them into predefined categories. Entities can be names of people, organizations, locations, times, quantities, monetary values, percentages, and more.’
Semantic Role Labelling (SRL) identifies the relations between predicates and arguments. In natural language processing (NLP) semantic role labelling (also called shallow semantic parsing (*) or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicate their semantic role in the sentence, such as that of an agent, goal or result. It consists of the detection of the semantic arguments associated with the predicate or verb of a sentence and their classification into their specific roles.
https://www.quora.com/What-is-the-difference-between-semantic-role-labeling-and-named-entity-recognition
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