A Treebank is an extensively annotated text corpus where every sentence is mapped out according to its syntactic or semantic structure. Unlike a standard corpus containing raw text, a Treebank provides a "gold standard" or a structural blueprint of language. This is typically represented as a hierarchical tree, illustrating how individual words aggregate into phrases and how those phrases function together to form a complete sentence.
The Essential Role in Parsing
In Natural Language Processing
1.Big-O Notation O (Upper Bound) Definition: Big-O gives the maximum growth rate of a function. It represents the worst-case complexity. f(n) = O(g(n)) 0≤ f(n) < c. g(n) for all n no Meaning: Function f(n) does not grow faster than g(n). Example: 3n² + 5n + 2 = O(n²)
2.Big-Omega Notation Ω (Lower Bound) Definition: Big-Omega gives the minimum growth rate. It represents the best-case complexity. f(n) = Ω(g(n)) 0≤c.g(n) ≤ f(n) for all n ≥ по Meaning: Function f(n) grows at least as
SOUTHERN EU: Spain (const.monarchy), Portugal, Italy (rep), Greece (rep), Malta (republic), Southern Cyprus; Smallest states: Vatican – papal state, San Marino – republic, Andorra – principality (Spanish bishop + French president), Malta – republic; Climate: subtropical Mediterranean; Crops: vine, rice (Italy – largest producer EU), olives (Spain – olive oil), cork (Portugal); SPAIN: languages – Spanish, Catalan, Basque; religion – Roman Catholic; car industry – SEAT; ITALY: religion
...C.EU. POLAND Population: 95% Polish (homogenous), strong Roman Catholic. Industry: Upper Silesia (industrial heart), Gdańsk/Gdynia (seaports). Resources: Hard coal, copper, salt, sulfur. Airports: Warsaw, Kraków. Term: Conurbation (interlinked cities like Katowice).
CZ 3 Regions: Bohemia, Moravia, Silesia. Religion: Mostly Atheist.Spas: Karlovy Vary, Mariánské Lázně, Františkovy Lázně. Industry: Auto (Škoda/Mladá Boleslav), Beer (Plzeň, Budějovice), Glass (Jablonec).
HU Religion: Catholic
Mass (m) = amount of matter / measure of inertia
→ unit: kg
Weight (W) = gravitational force
→ W = mg
→ unit: N
Force (F) = push or pull
→ unit: Newton (N)
Speed = distance / time (m/s)
Velocity (v) = speed + direction (vector)
Acceleration (a) = change in velocity / time (m/s²)
Vector = magnitude + direction
(velocity, acceleration, force, momentum)
Scalar = magnitude only
(mass, speed, energy)
the wheelbarrow being "glazed" with rain water suggests careful attention / beauty. the wheelbarrow is being looked at, not explained. the poem highlights the reliance of rural life on simple mundane labour
the red wheelbarrow: a wheelbarrow, chickens, rain. the contrast between the red wheelbarrow and the white chickens...
SET-2
1.List the applications and challenges in NLP.
Applications of Natural Language Processing (NLP):
Natural Language Processing is widely used across industries to enable machines
to understand and generate human language. Major applications include machine
translation, which converts text between languages; sentiment analysis, used to
detect opinions in reviews and social media; chatbots and virtual assistants for
automated customer support; speech recognition in voice-controlled systems;
set-1
1.Discuss the structure word and their components.
A.In linguistics and NLP, a structured word (or word structure) refers to
how a word is internally organized using meaningful building blocks. Words
are not always indivisible; many are formed by combining smaller units
called morphemes, the smallest units of meaning.
1. Root / Base
The core element carrying the primary meaning.
Example: play in replay, player, playful.
2. Stem
The form to which affixes attach.
1. What are the main challenges in NLP?
Ambiguity (lexical, syntactic, semantic, pragmatic)
Context understanding
Sarcasm/irony detection
Multilinguality & low-resource languages
2. Define Sentiment Analysis.
Sentiment analysis is the process of identifying and classifying
opinions or emotions expressed in text as positive, negative,
or neutral.
3. What are chatbots?
Chatbots are AI systems designed to simulate human conversation
through text or voice interactions.
4. Define Machine Translation.