Can there ever be a functionally complete set of grammar rules which can parse any statement in English (locale-specific) accurately and which can be possibly implemented for use in AI-based projects?
Parse it yes, accurately most likely no.
According to my understanding on how we derive meaning from sounds, there are 2 complementary strategies:
The following survey article by researchers from IIT Bombay summarizes recent advances in sarcasm detection: Arxiv link.
In reference to your question, I do not think it is considered either extraordinarily difficult or open-ended. While it does introduce ambiguity that computers cannot yet handle, Humans are easily able to understand sarcasm, and are thus ...
In Natural Language Processing (NLP), you are dealing with knowledge/intents/... expressed in linguistic structures. For example, if you have a text understanding system, you would read the text, usually sentence by sentence, analyse the syntactic structure, and then the meaning (composed of the word meaning and the structural meaning, plus the pragmatic ...
I strongly disagree with all the former comments. Not because they are wrong, -which they are not - but because they are misleading - though unintentionally.
For example: If one looks at these problems from an academic position, the problems will always seem insurmountable. This is because everything is coldly assessed and calculated in isolation to ...
We've concluded that it is a two-faceted, circular problem: structure cannot be inferred without context but knowing the structure also helps infer the context. So, here is your complex solution: start with the context, which is determined by the combination of words in sentence (combinatorics and search problem), from there determine your structure, or "...
I'm pretty sure that the answer is "no" in the strictest sense, since English simply doesn't have a formal definition. That is, nobody controls English and publishes a formal grammar that everyone is required to adhere to. English is built up through an experiential process and it has contradictions and flaws, but the probabilistic nature of the human mind ...
There has been a recent work in the same domain where neural networks(CNNs to be accurate) are used for the same purpose. Some info. about the research is:
To learn that context, the paper describes a method by which the
neural network finds the user’s “embeddings” — i.e. contextual cues
like the content of previous tweets, related interests and ...
If I understand correctly, what you are looking for is called "common sense reasoning" in NLP research.
Research in this field revolves around benchmark data sets, where good performance indicates some ability to do common sense reasoning. Here is a nice collection of data sets and research by Sebastian Ruder:
There are several aspects to this.
Firstly, content. I guess a further comparison would be to the monkeys on typewriters coming up with the complete works of Shakespeare eventually. You will probably have a huge mass of tedious text, with the odd nugget in it. One would hope that the signal-to-noise ratio would be better with human authors, though looking ...
In supervised learning the semantic gets injected into the NN via the supervision signal
For example, a typical pedestrian detection NN trained in a supervised learning way, has no knowledge about what the label "pedestrian" actually means before training, this semantic is injected during training by means of the supervision signal
However this "top-down"...
You can generally identify the mood of a verb by looking at grammatical structures; you don't need any language model for it. The three major moods in English are declarative, interrogative, and imperative. Assuming English is the language you will be working with, here are some questions:
Does he like coffee?
Is this a piece of chocolate?
When did you go ...
There may be a conceptual disconnect between the term Semantic Detection and the task of Head Tracking, since sequential recognition of an object in a set of visual samples representing continuous movement isn't technically a semantics problem. Although a mapping strategy that works with semantic processing may somehow apply to, with appropriate ...
Find the category of technical text on a surface-semantic-level.
The requirement is to classify the input text of paragraphs into their respective categories.
The categories given as predefined are as follows.
Some document types that would be added in place of 'etc' might ...
You can solve something rationally or with emotions/intuition.
Intelligence can be rational or intuitive. Rational is the newest more accurate form of intelligence.
Humans use both types of intelligences.
I recall someone (my prof probably) saying that the difference is that intelligence is a problem-solving capability, while rationality more-so refers the capability to apply one's intelligence.
ex: You are smart for knowing that sleeping late is bad for your health, but if you still sleep late then you are irrational.
In that sense then, rationality is ...
"semantic network" is way of representing "semantic" relations in form of a "graph" . where as "lexical semantic network" is a type of semantic network which represents the relations between words , sub-words or some-other linguistic related terms. so in other words , lexical semantic networks are a type of semantic networks dealing with language ...
According to Wikipedia and
,By John F. Sowa
This is an updated version of an article in the Encyclopedia of Artificial Intelligence, Wiley, 1987, second edition, 1992.
A semantic network is used when one has knowledge that is best
understood as a set of concepts that are related to one another.
Most semantic ...