LING 1100 Lecture Notes - Lecture 4: Colorless Green Ideas Sleep Furiously, Cream Cheese, Binoculars

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productiverosie

13 Feb 2023

School

Carleton University

Department

Linguistics

Course

LING 1100

Professor

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Document Summary

Speakers know the meaning of sentences they have never heard before. Compositionality meaning of larger expressions depends on the meanings of their parts and the way they are put together. Syntax studies how larger expressions are built out of smaller expressions. Consider the following pair of utterances: colorless green ideas sleep furiously. Doesn"t make sense: john morning up get, eat with cream cheese bagel, coffee too hot drink now therefore thermos put, and riding bus on to school. Sentence (1) doesn"t make sense, but otherwise is a well-formed sentence of english . Sentence (2) makes perfect sense, but is not a well-formed sentence of english. This illustrates that syntax is separate from semantics compare also pairs of verbs with similar meanings but different syntax: fear vs. frighten; eat vs. devour. Words in sentences are not beads on a string (just like morphemes in words)! The two structures have different sub-parts, or constituents: it"s either unlock+able, or un+lockable.

Related Questions

Analyze and write about the results of a visual and an auditory lexical decision task. Analyze a large data set that includes students’ responses for both the visual and auditory modality. Lab is a write-up of these findings.

One large data file that includes all experimental trial responses for both the Visual and Auditory modality tasks. Note that the sample size for the visual experiment is 16 and for the auditory experiment is 15.

Presentedz with visual words and pseudo-words in one experiment and auditory words and pseudo-words in a second experiment. Respond whether you saw/heard a real word of English or not. In the visual task, remained fixated on a cross (i.e., ‘+’) presented in the centre of the screen, while the stimulus was visually presented either to the left or right of the fixation point. In the auditory experiment, you heard words and pseudo-words presented either to your left or right ear.

Words Pseudo-words Number 60 60 Pronounceable N/A Yes Hemi-field/Ear Presentation 30 left/30 right 30 left/30 right String Length

Range: 5-7

Mean: 6.01 (SD = 0.71)

Range: 5-7

Mean: 5.85 (SD = 0.78)

Phoneme Length

Range: 5-7

Mean: 5.45 (SD = 0.59)

Range: 3-6

Mean: 4.58 (SD = 0.87)

Number of Morphemes 1 N/A

In each experiment, there were 60 words and 60 pseudo-words. It was the same 120 items presented in each experiment. Only their modality of presentation changed. Thirty words and 30 pseudo-words were presented to the left (side of the screen in the visual experiment, ear in the auditory experiment), while the other 30 words and other 30 pseudo-words are presented to the right. Side of presentation was balanced across participants using two lists. Each list contained the same set of words and pseudo-words; only presentation side was counter-balanced across lists.

Independent samples t-tests revealed no difference in orthographic length between the words and pseudo-words (t(117) = -1.59, p = 0.11); there is, however, a reliable phoneme length difference between the words and pseudo-words (t(104.28) = -6.37, p < 0.001). We also recorded whether the participant was a native speaker of English or not and whether they are right- or left-hand dominant. Feel free to use these factors in your analysis.

Given what you know about the organization of the visual and auditory pathways and lateralization of language processing, state, in very clear terms, what your predictions are. Provide specific predictions for your observation (dependent) variable(s), and select and name your predictor (independent) variable(s).
Provide a detailed list of the processing steps in R that is performed to arrive at your results. For example, do you want to include incorrect trials trials in your analysis of both dependent variables? Do not provide the R code, but instead write out these steps in plain English.
Provide a series of plots that provide an illustration of your results. You should minimally include a plot of the accuracy and reaction time results and this should be done for each modality. You may also included histograms of your reaction time distributions, but this is not necessary. Be sure your axes are appropriately labeled.
Provide a table that presents your numerical findings in a clear manner. Remember to include both the sample-level averages and standard deviations per condition.
Discuss your results in light of the predictions in Q1 and your findings in Q3 and Q4. Make specific reference to your predictions and the extent to which they are borne out.
Get creative: Incorporate one other factor (e.g., native language, handedness). State your predictions and provide a breakdown of the data in terms of these factors.
Design your own research question: Consider how you might use the same experimental design to understand another linguistic phenomenon and population (e.g. stop VOT, vowel length, lexical tone, Mandarin speakers, French speakers etc.). State your research question, and provide and motivate specific predictions for your dependent variable(s) as a function of your predictor variable(s). Note: Ideally, the new research question will still involve language lateralization. Consult the Gazzaniga readings online for ideas based on the particular specializations of each hemisphere.

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Do you want me to send you the whole 24 questions was on the test and tell you which topics they said we got it wrong? I can try to put it here. If not I can email it to you.

Here is the 24 questions with my answers.

2/20/2022

Question 1 of 24

You have a Speech service resource.

You need to develop an application that creates real-time captions for audio.

What should you include in the code?

Select only one answer.

1. SpeakerRecognizer

2. KeywordRecognizer

3. SpeechRecognizer

4. SpeechSynthesizer

My Answer is "3. SpeechRecognizer"

Question 2 of 24

You have an application that performs Speech to Text services for users by using the Speech SDK.

You need to add the ability to perform Text to Speech in the application.

Besides the text, which other values should you include in your queries?

Select only one answer.

1. the pronunciation

2. the voice

3. keyword

4. the phrase list

My answer was 1. The Pronunciation

Question 3 of 24

You are building a text to speech solution.

You need to ensure that your solution supports different name pronunciations.

What Speech Synthesis Markup Language (SSML) element should you use?

Select only one answer.

1. phoneme

2. mstts:express-as

3.break

4. lang

My answer was 1. Phoneme

Question 4 of 24

You develop an application by using the Speech SDK. The application translates the English language version of podcasts into French and German.

You need to create the code to provide the English version of the podcast for translation.

Which two objects should you add to the code?

Select all answers that apply.

1. TranslationRecognizer

2. SpeechRecognizer

3. SpeechSynthesizer

4. AutoDetect SourceLanguageConfig

5. SpeechTranslationConfig

My answer is 2. SpeechRecognizer

3. SpeechSynthesizer

Question 5 of 24

You have a Azure Video Analyzer for Media service that provides a search interface for video content.

You need to extract what is said in the videos so that it can be translated into other languages.

What should you extract?

Select only one answer.

1. labels

2. brands

3. transcripts

4. keywords

The answer is 3. Transcripts

Question 6 of 24

You build an application that uses Language Understanding.

You need to ensure that the application support speech inputs by using the Speech service.

What should you configure for Language Understanding?

Select only one answer.

1. Enable the Speech priming setting during model publishing.

2. Add additional utterances to the intents in the model before publishing.

3. Set the endpoint URL parameter verbose to true.

4. Enable the Normalize punctuation setting on the application.

Answer: 1. Enable the Speech priming setting during model publishing.

Question 7 of 24

You plan to use a Language Understanding container for an application that you previously exported.

Which two values from the underlying Language Understanding resource should you retrieve before you can containerize the application?

Select all answers that apply.

1. the subscription key

2. the endpoint URI

3. the prediction key

4. the resource location

My Answer: 2. the endpoint URI

4. the resource location

Question 8 of 24

You make a Text Analytics API call.

You call the following URL.

You send the following body.

{

"documents": [

{

"id": "1",

"countryHint":"US",

"text": "We analyzed text to see what would happen"

{

"id": "2",

"countryHint":"UK",

"text": "We analysed text to see what would happen"

]

}

You receive the following response.

{

"statistics": {

"documentsCount": 2,

"validDocumentsCount": 1,

"erroneousDocumentsCount": 1,

"transactionsCount": 1

"documents": [{

"id": "1",

"detectedLanguages": [{

"name": "English",

"iso6391Name": "en",

"score": 1.0

}],

"statistics": {

"charactersCount": 41,

"transactionsCount": 1

}

}],

"errors": [{

"id": "2",

"message": "Country hint is not valid. Please specify an ISO 3166-1 alpha-2 two letter country code."

}]

}

Which three of the following statements correctly describe the code?

Select all answers that apply.

1. The text fragment that has ID 1 is identified as English.

2. US is a valid value for countryHint.

3. The document that has countryHint=UK does not generate a billable transaction.

4. UK is a valid value for name.

5. The document that has countryHint=US does not generate a billable transaction.

My answer: 1. The text fragment that has ID 1 is identified as English.

2. US is a valid value for countryHint.

3. The document that has countryHint=UK does not generate a billable transaction.

Question 9 of 24

You plan to use Text Analytics to detect the language of reviews.

You need to route the reviews that are multi-lingual to a human translator.

Which field in the JSON response should you use to filter records?

Select only one answer.

1. score

2. errors

3. statistics

4. name

My answer: 3. statistics

Question 10 of 24

You develop a product review feature for an international e-commerce application.

When reviews are received by the application, the application posts the reviews to an Azure Cosmos DB database.

You need to develop an Azure Function that uses the Text Analytics API to notify a team when a negative review is posted. The notification must be sent as quickly as possible.

Which two components should you use in the function to identify a negative review?

Select all answers that apply.

1. the Sentiment endpoint

2. an Azure Cosmos DB trigger

3. a schedule triggert

4. he Key Phrase endpoint

5.the Detect Language endpoint

6.an Azure Cosmos DB output

My answer: 1. the Sentiment endpoint

6.an Azure Cosmos DB output

Question 11 of 24

You make a Text Analytics API call.

You call the following URL.

You send the following body.

{

"documents": [

{

"language": "en",

"id": "1",

"text": "Our tour guide took us up the Space Needle during our trip to Seattle last week."

}

]

}

You receive the following response body.

{

"statistics": {

"documentsCount": 1,

"validDocumentsCount": 1,

"erroneousDocumentsCount": 0,

"transactionsCount": 1

"documents": [{

"id": "1",

"score": 0.5,

"statistics": {

"charactersCount": 80,

"transactionsCount": 1

}

}],

"errors": []

}

The text fragment that has ID 1 contains 80 alphanumeric characters.

Select Yes if the statement is true. Otherwise, select No.

Select only one answer.

1. Yes

2. No

My answer is Yes

Question 12 of 24

You are designing a chatbot for a restaurant.

You plan to include a series of questions that will be required to complete a reservation.

What type of dialog should you use?

Select only one answer.

1. Waterfall

2. Prompt

3. QnA Maker

4. Action

My answer was Prompt ( I know you said Qna Maker)

Question 13 of 24

You are building a chatbot by using the Microsoft Bot Framework Composer.

The chatbot will include a dialog that will be used to enter prices of products.

Users will perform an action for the products.

The products and prices will resemble the following:

Butter 1.99
Bread 1.01
Salt 3.50

Which type of recognizer should you use?

Select only one answer.

1. Regex

2. None

3. LUIS

My answer was 3. LUIS

Question 14 of 24

You are building a chatbot that uses the Bot Framework Composer.

You need to collect user contact information.

What should you use to validate the user phone numbers?

Select only one answer.

1. Language Generator

2. Recognizer

3. Trigger

4. Dialog

My answer is 2. Recognizer

Question 15 of 24

You are building a chatbot by using the Microsoft Bot Framework Composer.

You need to test the bot.

What two tools could you use?

Select all answers that apply.

1. Bot Framework CLI

2. Bot Framework Composer

3. Bot Framework SDK

4. Bot Framework Emulator

My answer is

3. Bot Framework SDK

4. Bot Framework Emulator

Question 16 of 24

You are building an Azure Cognitive Search solution.

What should you use to increase the amount of concurrent query requests that can be handled by the solution?

Select only one answer.

1. Replicas

2. Partitions

3. Indexes

4. Pricing tier

My answers 4. Pricing Tier

Question 17 of 24

You have 20,000 PDF documents that must be indexed by using Azure Cognitive Search.

You need to recommend a storage solution for the PDFs.

What should you recommend?

Select only one answer.

1. Azure Storage

2. Microsoft SQL Server

3. Azure Cosmos DB

4. Amazon AWS S3

My answer is s1. Azure Storage

Question 18 of 24

You are building an Azure Cognitive Search solution.

You are defining an index for blog posts.

Searches must query the description field and return the descriptions in search results.

What field options should you select for the description field?

Select only one answer.

1. Retrievable and Searchable

2. Filterable and Searchable

3. Retrievable and Sortable

4. Facetable and Sortable

My answer is 2. Filterable and Searchable

Question 19 of 24

You are building an Azure Cognitive Search solution.

You plan to build an API for a custom skillset.

What data format should you use for the API to accept and return?

Select only one answer.

1. JSON

2. XML

3. YAML

4. HTML

My answer is 1. JSON

Question 20 of 24

You plan to build an Azure Cognitive Search solution that will search images and PDF documents.

Users of the solution will search for text, captions, key phrases, identified objects, or named entities.

Each day, you will need to index and enrich 5,000 images and 10,000 PDFs.

Which type of resource should you create for the planned solution?

Select only one answer.

1. Computer Vision

2. Text Analytics

3. Azure Machine Learning

4.Azure Cognitive Services

My Answer is 1. Computer Vision

Question 21 of 24

You have an Azure Cognitive Search solution.

You need to extract text from PDFs during indexing.

Which skill should you use?

Select only one answer.

1. key phrase extraction

2. content moderation

3. sentiment

4. optical character recognition (OCR)

My Answer is :. optical character recognition (OCR)

Question 22 of 24

You have an Azure Cognitive Search solution.

You have a custom skillset that receives a complex set of fields in a response.

What should you use to simplify and restructure the fields?

Select only one answer.

1. A shaper skill

2. An object projection

3. An indexer

4. A data source

My answer is A. A shaper skill

Question 23 of 24

You have an enrichment pipeline that uses Azure Cognitive Search.

The pipeline ingests Azure Cosmos DB data and identifies key phrases.

The application that generates the data must be able to retrieve key phrases and confidence by querying an API.

What should you use to persist the data?

Select only one answer.

1. a table store

2. a file storea

3.an object store

The correct answer is 3. an Object store

Question 24 of 24

You have an enrichment pipeline in Azure Cognitive Services.

The enrichment pipeline has a custom skill that calls an API.

You need to ensure that all responses from the API are structured consistently.

Which skill should you add to the skillset?

Select only one answer.

1. Merge

2. Shaper

3. ConditionalText

4. Split

My answer is 2. Shaper

I would appreciate if you can help me to get this over. I need 65% to pass and i only got 45%.

Here is what parts I am wrong.

mmyint1234

A

1. Load sa3.txt and save it in the variable sa3. This file is tab-separated and has a header.

2. For each level of the independent variable, calculate the mean, median, and standard deviation of the dependent variable.

Store your results as {level}.mean, {level}.median, and {level}.sd. Where it says {level}, put the exact name of the level as it is in the column, without {…} or anything else around it. E.g., if the level was called island, you’d write island.mean.

3. For each level of the independent variable, calculate a 92.8% confidence interval of the mean for the dependent variable.

Store the confidence interval as {level}.ci.left and {level}.ci.right, depending on whether you’ve calculated the minimal endpoint (least) or the maximal end-point (greatest). You must use the confidence interval formula to do this calculation, as seen in class: CI = ¯x − (t × SE) and CI = ¯x + (t × SE).

Sometimes, if there’s a sudden shift in the sense of a word, that can be an indication a word might be polysemous. For instance, if I enjoyed the book but it was heavy seems a little off to you, it might be because you’ve suddenly switched senses for book.

4. For each level of the independent variable, check that the dependent variable is normally distributed. First, produce a relevant plot with appropriate labels, scaling, and so on. Then, use an appropriate statistical test to check for normality. Store the result of the test in {level}.normality.test.

5. Produce a side-by-side boxplot that visualizes the dependent variable for each level of the independent variable. (There’s nothing to store for this step.)

6. Take the levels of the independent variable, and perform an F-test on the dependent variable. Store the result of the test in variance.test.

Use your findings from the previous section to answer the following questions.

1. For this question, you’ll complete your check for normality. For each level of the independent variable, report the result of the normality test (in the appropriate style), and include your plot.

2. For this question, use your barplot to compare the dispersions and central tendencies of the groups in the experiment. In a few sentences, (i) evaluate whether the dispersions are similar or not (and how you can tell), and (ii) evaluate whether the central tendencies are similar or not (and how you can tell). Make sure you’re specific about what measure of dispersion and what measure of central tendency you’re talking about, and include your barplot in the answer.

3. Based on your confidence intervals of the means calculated in Part A, would you conclude that the means for these groups in this experiment are different? In a sentence or two, explain your reasoning.

4. Report the results of the F-test in the appropriate style and then briefly state whether this is evidence that polysemous words are more variable in their reaction times than monosemous words.

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LING 1100 Lecture Notes - Lecture 4: Colorless Green Ideas Sleep Furiously, Cream Cheese, Binoculars

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