🗣️Movement Phenomena in Language

Movement phenomena refer to structural changes in sentences where certain elements (like
words or phrases) appear to be "moved" from their original positions to form questions or
other constructions.

1. ✅ Yes/No Questions and Subject-Auxiliary Inversion

 Basic Observation:
Yes/no questions are similar to their corresponding assertions, except the subject
noun phrase (NP) and the first auxiliary verb are swapped.
 Examples:
o Jack is giving Sue a back rub. → Is Jack giving Sue a back rub?
o He will run in the marathon. → Will he run in the marathon?
 If there is no auxiliary, insert "do/does/did" (known as do-support):
o John went to the store. → Did John go to the store?
o Henry goes to school every day. → Does Henry go to school every day?
 📌 This swapping is called Subject-Auxiliary Inversion.

2. ✅ Wh-Questions and Wh-Movement

 Wh-questions ask about specific information (e.g., who, what, where, how, why).
 To form a wh-question:
1. Replace the questioned element with a wh-phrase.
2. Move the wh-phrase to the beginning of the sentence.
3. Apply subject-auxiliary inversion (unless the subject is being questioned).
  Examples:
Assertion: The fat man will put the book in the corner.

o How will the fat man put the book in the corner?
o What will the fat man angrily put in the corner?
o Where will the fat man angrily put the book?
o In what corner will the fat man angrily put the book?
 🔁 Still follows the subject-auxiliary inversion rule, and do-support is used if no
auxiliary is present:
o I found a bookcase.
→ Did I find a bookcase?
→ What did I find?
o

3. ✅ Terminology from Linguistics

 Movement: Refers to rearranging sentence elements (like moving auxiliaries or wh-

phrases).
 Subject-Auxiliary Inversion: Swapping the subject and the auxiliary verb.
 Do-Support: Inserting a "do" form when no auxiliary is present.
 Wh-Movement: Moving a wh-phrase to the start of the sentence.

4. ✅ Transformational Grammar (TG)

 Introduced by Noam Chomsky.

 TG proposes two levels of sentence structure:
o Deep Structure: The basic sentence (e.g., The cat will scratch John).
o Surface Structure: The final sentence we speak/write after transformations
(e.g., Will the cat scratch John?).
 Transformations: Rules that convert the deep structure to the surface structure (e.g.,
moving auxiliary, inserting wh-words).
 


Summary Table:

Assertion Sentence Yes/No Question Wh-Question Example

Is Jack giving Sue a back

Jack is giving Sue a back rub. Who is Jack giving a back rub to?
rub?

He will run in the marathon next Will he run in the When will he run in the
year. marathon? marathon?

John went to the store. Did John go to the store? Where did John go?

I found a bookcase. Did I find a bookcase? What did I find?

 Assertion Sentence Yes/No Question Wh-Question Example

Handling Questions in Context-Free Grammars

Definition: A context-free grammar consists of a set of rules or productions, each of which

expresses theways that symbols of the language can be grouped and ordered together, and a
lexicon ofwords and symbols.

Context Free Grammar (CFG) - Formal Definition

Context-free grammar G is a 4-tuple.

G = (V, T, S, P)

These parameters are as follows;

V – Set of variables (also called asNon-terminal symbols)

T – Set of terminal symbols (lexicon)

o The symbols that refer to words in a language are calledterminal symbols.

o Lexicon is a set of rules that introduce these symbols

. S – Designated start symbol (one of the non-terminals, S∈ V)

 P – Set of productions (also called asrules).

o Each rule in P is of the formA → s, where

o A is a non-terminal (variable) symbol

.o Each rule can have only one non-terminal symbol on the left hand side of therule.

o s is a sequence of terminals and non-terminals. It is from (T U V)*, infinite setof strings.

🧠
2. Sample CFG and Derivation
Grammar Example:
S → NP VP
NP → Det Noun | NP PP
VP → Verb NP
PP → Pre NP
Det → 'a' | 'the'
Noun → 'cake' | 'child' | 'fork'
Verb → 'ate'
Pre → 'with'

Sample Sentence Derivation:

S → NP VP

→ Det Noun VP

→ the Noun VP

→ the child VP

→ the child Verb NP

→ the child ate NP

→ the child ate Det Noun

→ the child ate a Noun

→ the child ate a cake

✅ This shows that "the child ate a cake" is generated by the grammar and hence is
grammatically correct.

❓ 3. Extending CFGs to Handle Questions

CFGs as described above typically handle declarative sentences (statements).
But natural languages also contain interrogative sentences (questions), such as:

 Yes/No Questions: Did the child eat the cake?

 Wh-Questions: What did the child eat?

Goal:
➡️Extend the grammar minimally to support questions, while reusing as much of the

original grammar as possible.

✅ 4. Yes/No Questions
🛠 Mechanism: Subject–Auxiliary Inversion

In yes/no questions, the auxiliary verb moves in front of the subject noun phrase (NP).

 Statement: The child has eaten the cake.

 Question: Has the child eaten the cake?

📐 Extended Rule:

We add a rule like this:

S[+inv] → (AUX AGR ?a SUBCAT ?v) (NP AGR ?a) (VP VFORM ?v)

Where:

 AUX: Auxiliary verb

 NP: Subject noun phrase
 VP: Verb phrase
 AGR: Ensures subject-verb agreement
 VFORM: Ensures correct verb form follows the auxiliary

This single rule, when added, enables the grammar to produce valid yes/no questions by just
changing the order of subject and auxiliary. All other grammar components remain
unchanged.
❓ 5. Wh-Questions
These are more complex than yes/no questions because they involve gaps — missing
constituents in the sentence.

🔎 Example:

 Statement: You put the book in the shelf.

 Wh-question: What did you put the book in __?

Here, "what" replaces a noun phrase and leaves behind a gap.

🧩 Features Needed:

5.1 GAP Feature:

 Keeps track of missing elements (like the object NP in the example).

 Propagates through the sentence structure to indicate where something is missing.

5.2 WH Feature:

 Identifies wh-words (who, what, where, etc.) and their corresponding phrase types.
 These words serve as fillers for the gaps left in the sentence.

🧮 6. Categories of WH-Words
WH Word Phrase Type Replaced Example

who, whom, what Noun Phrase (NP) Who ate the cake?

what, which Determiner in NP What book did he steal?

where, when Prepositional Phrase (PP) Where did you put the book?
 WH Word Phrase Type Replaced Example

how Adverbial Phrase (AdvP) How quickly did he run?

whose Possessive NP Whose book did you find?

🔄 7. How to Extend CFG for Wh-Questions

Step-by-step:

1. Introduce GAP feature to non-terminal rules (e.g., NP, PP) to allow missing
elements.
2. Add WH feature to identify wh-phrases and restrict them to appropriate roles.
3. Prevent gap propagation into the subject position in inverted clauses (e.g.,
auxiliary questions).
4. Allow fronted wh-phrases to act as fillers for the gap in the sentence body.

5.

🔚 8. Summary: CFG Extension Strategy

Aspect Declarative CFG Extended CFG for Questions

Sentence Form S → NP VP S[+inv] → AUX NP VP

Question Type Not handled Yes/No, Wh-questions

Auxiliary Handling None AUX comes before subject

Gap Tracking Not needed GAP feature in rules

WH-Phrase Handling Not needed WH feature for fronting

Grammar Reuse Full Full (with minimal additions)

 Aspect Declarative CFG Extended CFG for Questions

Here’s a brief and clear set of notes on Augmented Transition Networks (ATNs) and hold
mechanisms based on your content:

Augmented Transition Networks (ATNs) – Brief Notes

What are ATNs?

 ATNs are a type of transition network used in natural language parsing.

 They extend finite state machines (FSMs) by:
o Allowing recursive procedures (networks can call themselves or others).
o Using registers (features) to store and manipulate data during parsing.

Why ATNs are Needed

 Traditional FSMs can only handle regular languages, not enough for complex
natural language constructs.
 Natural language involves hierarchical and context-sensitive structures.
 ATNs are designed to handle context-free and some context-sensitive aspects of
language.

Key Features of ATNs

 Registers (features): Store info like parts of speech, agreement, etc.

 Recursive calls: Networks can call sub-networks for complex constituents.
 Actions: Executed when arcs are followed, used to:
o Set registers (e.g., DET := *)
o Copy features (e.g., AGR := AGR*)
 Tests: Conditions to check if a transition is valid (e.g., agreement checks).
 Constituent building: When a network finishes, its registers form a constituent
structure.

Hold Mechanism

 ATNs use a special variable "*" to hold the result of following an arc.
 Example:
o If a word like "the" is accepted via a lexical arc, it's stored in "*".
o Then an action like DET := * stores it in the DET register.
o Similarly, AGR := AGR* copies the agreement info from * into the current
network.
Simple NP (Noun Phrase) ATN Example

 Lexical arc (e.g., for a determiner):

o Actions:
1. DET := * – Store the matched word.
2. AGR := AGR* – Copy agreement features.
 Tests on arcs enforce rules like subject-verb agreement.

ATN – Tests, Push Arcs & Agreement (Compact Notes)

 Arc Tests:
o An arc is followed only if its test passes.
o Example: Arc 2 checks if AGR of the current network and the noun in "*" have
a non-empty intersection.
 Push Arcs:
o Used to call sub-networks (e.g., NP).
o The result is stored in "*".
o Example: SUBJ := * assigns the parsed NP to the subject register.
 Agreement Check (e.g., Subject-Verb):
o Test compares AGR of SUBJ (noun phrase) with AGR of the verb (in "*").
o Arc proceeds only if their features match (i.e., agree).
 Ensures grammatical consistency, such as subject-verb agreement.
Consider an example. A trace of a parse of the sentence "The dog saw Jack"

Here's a concise and structured summary of the new points you've provided:
ATN – Parse Flow Summary

 Parsing starts in the S network.

 Arc 4 triggers a call to the NP network.
 NP network parses the phrase "The dog":
o Checks number agreement.
o Builds NP with AGR = plural.
 On returning (via pop arc), arc 4 is completed.
 NP is stored in SUBJ register.
 Arc 3 is then followed:
o Checks if SUBJ.AGR agrees with verb’s AGR.
 Another NP, "Jack", is accepted in a similar call to the NP network.

1. Gap Threading as a Parsing Concept

If you're referring to syntax or parsing, “gap threading” might relate to movement/gap

phenomena in natural language syntax — for example, in wh-movement (e.g., What did you
say that John bought ___?). In such sentences, parsers need to thread the gap left by a
displaced element (like "what") back to its original syntactic position.

In this context, gap threading is the process of:

 Keeping track of the "gap" (i.e., the missing constituent).

 Ensuring grammatical dependencies (e.g., subject/object roles) are maintained during
parsing.

It's common in transformational grammar and unification-based grammar frameworks

like HPSG or LFG, where gaps are handled using features that “thread” through syntactic
trees.

2. Multithreading in NLP (Gap + Threading?)

If by “gap threading” you're referring to parallelism or multithreading in NLP tasks, then it

could mean:

 Using threading to fill gaps in data, e.g., during coreference resolution, ellipsis
recovery, or missing data reconstruction.
 Using multithreading (in code) to speed up NLP tasks, like tokenization, tagging,
parsing, etc., especially for large corpora.

3. Text Gap Filling

In tasks like cloze tests, gap threading might refer to connecting gaps (blanks) to potential
fillers using context — in models like masked language models (e.g., BERT). Here,
threading refers metaphorically to connecting context before and after the gap.