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Difference between autusuggest dictionary & uplift fragment suggest function

John Narins

Can someone explain the difference please?

In terms of: word count required, the types of suggests you get as a result, etc.?

Thanks in advance!
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    Hi John,

    1. You must have 10,000 translation units in your selected TM to build an Autosuggest dictionary.
    To enable upLift, you need at least 1,000 translation units in your selected TM (however, it is recommended to have at least 5,000 for good results).

    2. Once an Autosuggest dictionary is built, the entries are fixed and you have to rebuild it every time you want to add new entries.
    upLift can reuse entries you just updated into the TM, as alignment is done automatically.

    3. Autosuggest by default provides suggestions from one word, while fragment suggest defaults to two words. You can change this to one word for fragment matching, but this can cause too many unnecessary results.

    4. The fragment matching window does not provide suggestions if there are results from the TM while the Autosuggest dictionary will still provide suggestions.

    5. Both use statistical methods to provide suggestions, but there results are not exactly the same. You will have to try out both to see what types of suggestions you get as a result.
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    Hi John,

    1. You must have 10,000 translation units in your selected TM to build an Autosuggest dictionary.
    To enable upLift, you need at least 1,000 translation units in your selected TM (however, it is recommended to have at least 5,000 for good results).

    2. Once an Autosuggest dictionary is built, the entries are fixed and you have to rebuild it every time you want to add new entries.
    upLift can reuse entries you just updated into the TM, as alignment is done automatically.

    3. Autosuggest by default provides suggestions from one word, while fragment suggest defaults to two words. You can change this to one word for fragment matching, but this can cause too many unnecessary results.

    4. The fragment matching window does not provide suggestions if there are results from the TM while the Autosuggest dictionary will still provide suggestions.

    5. Both use statistical methods to provide suggestions, but there results are not exactly the same. You will have to try out both to see what types of suggestions you get as a result.
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