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CSCI544 Assignment
Creation date：2024-06-20 17:43:42
Belonging category：计算机computer science
Tag CSCI544
Assignment

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CSCI544 Assignment

Introduction
This assignment gives you hands-on experience on building deep learning
models on named entity recognition (NER). We will use the CoNLL-2003
corpus to build a neural network for NER. The same as HW3, in the folder
named data, there are three files: train, dev and test. In the files of train and
dev, we provide you with the sentences with human-annotated NER tags.
In the file of test, we provide only the raw sentences. The data format is
that, each line contains three items separated by a white space symbol. The
first item is the index of the word in the sentence. The second item is the
word type and the third item is the corresponding NER tag. There will be a
blank line at the end of one sentence. We also provide you with a file named
glove.6B.100d.gz, which is the GloVe word embeddings [1].
We also provide the official evaluation script conll03eval to evaluate the
results of the model. To use the script, you need to install perl and prepare
your prediction file in the following format:
idx word gold pred (1)
where there is a white space between two columns. gold is the gold-standard
NER tag and pred is the model-predicted tag. Then execute the command
line:
perl conll03eval < {predicted file}
where {predicted file} is the prediction file in the prepared format.
1
Task 1: Simple Bidirectional LSTM model (40
points)
The first task is to build a simple bidirectional LSTM model (see slides page
43 in lecture 12 for the network architecture) for NER.
Task. Implementing the bidirectional LSTM network with PyTorch. The
architecture of the network is:
Embedding→ BLSTM→ Linear→ ELU→ classifier
The hyper-parameters of the network are listed in the following table:
embedding dim 100
number of LSTM layers 1
LSTM hidden dim 256
LSTM Dropout 0.33
Linear output dim 128
Train this simple BLSTMmodel with the training data on NER with SGD
as the optimizer. Please tune other parameters that are not specified in the
above table, such as batch size, learning rate and learning rate scheduling.
What are the precision, recall and F1 score on the dev data? (hint: the
reasonable F1 score on dev is 77%.
Task 2: Using GloVe word embeddings (60
points)
The second task is to use the GloVe word embeddings to improve the BLSTM
in Task 1. The way we use the GloVe word embeddings is straight forward:
we initialize the embeddings in our neural network with the corresponding
vectors in GloVe. Note that GloVe is case-insensitive, but our NER model
should be case-sensitive because capitalization is an important information
for NER. You are asked to find a way to deal with this conflict. What are
the precision, recall and F1 score on the dev data? (hint: the reasonable F1
score on dev is 88%.
2
Bonus: LSTM-CNN model (10 points)
The bonus task is to equip the BLSTM model in Task 2 with a CNN module
to capture character-level information (see slides page 45 in lecture 12 for the
network architecture). The character embedding dimension is set to 30. You
need to tune other hyper-parameters of CNN module, such as the number of
CNN layers, the kernel size and output dimension of each CNN layer. What
are the precision, recall and F1 score on the dev data? Predicting the NER
tags of the sentences in the test data and output the predictions in a file
named pred, in the same format of training data. (hint: the bonus points are
assigned based on the ranking of your model F1 score on the test data).
Submission
Please follow the instructions and submit a zipped folder containing:
1. A model file named blstm1.pt for the trained model in Task 1.
2. A model file named blstm2.pt for the trained model in Task 2.
3. Predictions of both dev and test data from Task 1 and Task 2. Name
the file with dev1.out, dev2.out, test1.out and test2.out, respectively.
All these files should be in the same format of training data.
4. You also need to submit your python code and a README file to
describe how to run your code to produce your prediction files. In the
README file, you need to provide the command line to produce the
prediction files. (We will execute your cmd to reproduce your reported
results on dev).
5. A PDF file which contains answers to the questions in the assignment
along with a clear description about your solution, including all the
hyper-parameters used in network architecture and model training.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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