XML 程序代写代写XML 、代写Preprocessing text data

日期：2018-05-19 07:34

Assessment 3: Preprocessing text data

Please carefully review all the requirements below to ensure you have a good

understanding of what is required for your assessment.

1. Due Date

2. Instructions & Brief

3. Assessment Resources

4. Assessment Criteria

1. Grading Rubric

2. Penalties

5. How to Submit

1. Due Date

This specific due date and time can be viewed below in the Grading

Summary.

2. Assessment Description

Text documents, such as long recordings and meeting transcripts, are usually

comprised of topically coherent text segments, each of which contains some

number of text passages. Within each topically coherent segment, one would

expect that the word usage demonstrates more consistent lexical

distributions than that across segments. A linear partition of texts into topic

segments can be used for text analysis tasks, such as passage retrieval in IR,

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FIT5196 Data wrangling S1 2018

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Assignment 18518 14:21

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document summarization, and discourse analysis. In this assessment, you

are required to write Python code to preprocess a set of meeting transcripts

and convert them into numerical representations suitable for input into topic

segmentation algorithms.

This is an individual assignment and worth 30% of your total mark for

FIT5196.

The detailed tasks are as follows:

Task 1: Reconstruct meeting transcripts with topical boundaries. The

original meeting transcripts are stored in three different types of XML

files, which are ending with ".words.xml", ".topic.xml" and

".segments.xml". (The details about the three types of files can be found

in Section 3 below). The task here is to reconstruct the original meeting

transcripts with the corresponding topical and paragraph boundaries

from these files. Please note that

A meeting transcript must be generated for each of the "*.topic.xml"

file. For example, "ES2002a.txt" will be generated for

"ES2002a.topic.xml".

All the generated meeting transcripts with the ".txt" file extension

must be saved in the folder "txt_files".

The topical boundaries must be denoted with "**********"(i.e., 10

asterisks).

All the tokens, including punctuations, must be separated by a white

space. For example, "Alright , okay . Okay ."

Besides the topical boundaries, the paragraph boundaries must also

be reconstructed with the "*.segments.xml" file.

The input files to your notebook "task_1.ipynb" must be the three

types of XML files. The output must be the meeting transcripts saved

in a set of txt files.

A sample meeting transcript is provided in the "txt_file" folder.

Task 2: Generate sparse representations for the meeting transcripts.

The aim of this task is to build sparse representations for the meeting

transcripts generated in task 1, which includes word tokenization,

vocabulary generation, and the generation of sparse representations.

Please note that

The word tokenization must use the following regular expression,

"\w+(?:[-']\w+)?", and all the words must be converted into the lower

case.

The stop words list (i.e, stopwords_en.txt) provided in the zip file

must be used.

The words, whose document frequencies are greater than 132, must

be removed.

Generating multi-word phrases (i.e., collocations) are not needed.

The output of this task must contain the following files:

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vocab.txt: It contains the unigram vocabulary in the following

format, word_string:integer_index. Words in the vocabulary

must be sorted in alphabetic order. For example, "absolute:22" in

the following figure means that the 23rd word in the vocabulary is

"absolute".

topic_seg.txt: It contains the topic boundaries encoded in

boolean vectors. For example, if a meeting transcript,

"ES2018d.txt" contains 10 paragraphs in total after being

preprocessed, and there are topic boundaries after the 2nd,

5th, and 7th paragraphs, the boolean vector must be

"ES2018d:0,1,0,0,1,0,1,0,0,1". Every line in topic_seg.txt

corresponds to one meeting transcript.

./sparse_files/*.txt : Each txt file in the "sparse_files" folder

corresponds to one of the meeting transcripts in the "txt_files"

folder, and they have the same file name. For example,

"./sparse_files/ES2002a.txt" corresponds to

"./txt_files/ES2002a.txt". Each file in "/sparse_files" contains the

sparse representations for all its paragraphs as

22 Apr)

Assignment 18518 14:21

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where 1) each line is a paragraph and the order of the lines must

match the paragraph order in the corresponding meeting

transcript. 2) the integer before ":" is the word index in the

vocabulary and the one after is the frequency of the word in the

corresponding paragraph; 3) empty paragraphs after

preprocessing must be excluded.

3. Assessment Resources

Before you start writing your code, you will need to download the file

meeting_transcripts

Unzipping the file, you will find that

There are three types of XML files in the given folder :

1. ./topics/*.topic.xml contains the information about topic segments.

Each topic tag directly linked to the root indicates one topic

segment that is required in text segmentation task. Each topic

segment can contain a number of paragraphs given by different

meeting attendees. It can also contain sub-topics.

2. ./words/*.words.xml contains the word tokens generated with the

force alignment technique. Each word is associated with its start

time and end time in the meeting transcript.

3. ./segments/*.segments.xml contains the paragraph boundaries,

the start and end of which are denoted by the corresponding word

IDs.

./spase_files: the file folder used to store the generated sparse

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representations for all the meeting transcripts.

./txt_files: the file folder used to save the reconstructed meeting

transcripts.

./stopwords_en.txt: the stopword list used in word tokenization.

./topic_segs.txt: the file used to save the topical boundaries.

./vocab.txt: the file used to save the vocabulary.

./task_1.ipynb: the python code you are going to write for task 1

./task_2.ipynb: the python code you are going to write for task 2

4. Assessment Criteria

The following outlines the criteria which you will be assessed against.

4.1 Mark allocation and general marking criteria

1. The submitted scripts in the notebook should work without any errors

and must give the correct results. If the submitted notebook cannot be

run by the assessor, which will be double-checked by the head tutor

and the lecturer, zero marks will then be given to the corresponding

task.

task 1: 14 out of 30

task 2: 14 out of 30

task 2 will be assessed if and only if task 1 is successfully finished

and receives a full mark (i.e., 14).

2. The code should be well structured and properly commented. (1 out of

30)

3. The notebook should be structured in a logical way so that it clearly

shows how students finish the tasks in the assessment. (1 out of 30)

4. Criteria 2 and 3 will be assessed if and only if the mark for criteria 1 is

greater than and equal to 25.

4.2 Penalties

Late submission: for all assessment items handed in after the official due

date, and without an approved extension, a 10% penalty applies to the

student's mark for each day after the due date (including weekends, and

public holidays) for up to 5 days. Assessment items handed in after 5

days will not be considered!

Submission: please do follow Section 5 How to Submit to submit your

assignment. Otherwise, a 5% penalty will be applied.

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Add submission

Make changes to your submission

5. How to Submit

Once you have completed your work, take the following steps to submit your

work.

1. Only one zip file needs to be submitted: Once you finished the

tasks, please zip the folder only contains the files specified in

Section 3, including the original XML files. The zip file should be

named as 5196_assessment_3_Surname_StudentID.zip

2. Click the Add Submission button below to submit and upload

your assignment. Please do remember to accept the

submission statement! Only the submitted assignments will

be marked. Those shown as a draft won't be marked!

If you need further guidance on how to submit an assessment item, please

review the Submitting an assessment overview. If you need further

assistance, please go to Help & Support.

Submission status

Attempt number This is attempt 1.

Submission status No attempt

Grading status Not graded

Due date Sunday, 27 May 2018, 11:55 PM

Time remaining 9 days 9 hours

Last modified -

Submission

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Assignment 18518 14:21