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Introduction des, we pifinble yot wath the mentracen with humat-anotated juet-of-4pecth taps. In the fle of fest, ne parovide only the fiow menterens that vou

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Introduction des, we pifinble yot wath the mentracen with humat-anotated juet-of-4pecth taps. In the fle of fest, ne parovide only the fiow menterens that vou nexd to piratict the part-af-ownest taps. The datg ecgat b that, eacfing mains three itros eparialed ly the fab y ymbed 31. The first ifus is the indry of the end of oov ament Task 1: Vocabulary Creation ( 20 points) The litst task in trefrate i vocabalary tieing the trainiag alata) In H393, Traxk. Crating a veralmelery ueing 3 he training deda in the file trein and vutjput the vacabulary inde a txt file samed mopab tat. The format of the ocabulary Bile is that each line contalns a word types, its index ia 'ht' Xie first dimp spould be the special token ' 4 nok > ' asd the followiag biefestionla be morted by itin occurrences in descending order. Note this we ean enly nse the training data to qeate the vocatslary, witheat louding the drevbepairat aiel tost data [14leat i the selrated? Alter roflaceinet? Task 2: Model Learning (20 points) the molution of the entisile atd transition parameters in HMM ate in the follancitu fuctutatise: Introduction des, we pifinble yot wath the mentracen with humat-anotated juet-of-4pecth taps. In the fle of fest, ne parovide only the fiow menterens that vou nexd to piratict the part-af-ownest taps. The datg ecgat b that, eacfing mains three itros eparialed ly the fab y ymbed 31. The first ifus is the indry of the end of oov ament Task 1: Vocabulary Creation ( 20 points) The litst task in trefrate i vocabalary tieing the trainiag alata) In H393, Traxk. Crating a veralmelery ueing 3 he training deda in the file trein and vutjput the vacabulary inde a txt file samed mopab tat. The format of the ocabulary Bile is that each line contalns a word types, its index ia 'ht' Xie first dimp spould be the special token ' 4 nok > ' asd the followiag biefestionla be morted by itin occurrences in descending order. Note this we ean enly nse the training data to qeate the vocatslary, witheat louding the drevbepairat aiel tost data [14leat i the selrated? Alter roflaceinet? Task 2: Model Learning (20 points) the molution of the entisile atd transition parameters in HMM ate in the follancitu fuctutatise

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