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SOAIA 8.452481132 9.434430619 9.279111431 8.968164103 7.559441612 7.921829895 8.105009294 7.360353238 7.883951629 7.336052174 8.71376381 5.571503369 8.452481132 9.434430619 9.279111431 8.968164103 7.559441612 7.921829895 FAFAS 11.88328955 13.62255165 11.89887247 11.26683332 11.43322377 12.76809939 12.54268335 12.4426213 11.55626316 12.82527397 10.91247676 13.08269083 11.88328955 13.62255165 11.89887247 11.26683332 11.43322377 12.76809939 FEASA 9.118054151 11.46669239 11.04935621 10.15976462 10.5056035 10.4921699 11.02110101 9.486705236 11.13060371 9.791248707 8.370747774 9.82247289 9.118054151 11.46669239 11.04935621 10.15976462 10.5056035 10.4921699
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Figure 9.2. The Network section lets you know how your connection is doing.
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more than a shallow impression of the complexity of the challenges at each level. Since today s IE applications mainly work with documents and other written text, speech recognition and other tasks on the phonological level are omitted in this context. Tokenization Tokenization is the task of dividing a text into single lexical tokens. It is an absolutely vital task for any IE system that goes beyond very primitive forms of crude pattern matching, and a task that is often underestimated in its complexity. Besides simple white space identi cation, tokenization includes activities such as dehyphenation, sentence boundary detection, proper name recognition, and for multi-lingual systems language detection. There is a range of different techniques for solving these problems. Whereas some systems employ statistical techniques (Palmer and Hearst, 1994) for disambiguating sentence boundaries (the full stop vs. abbreviation period problem), others only make use of heuristics like regular expressions (Grefenstette and Tapanainen, 1994). Lexical/Morphological Analysis After tokenization, most IE systems perform a lexical analysis on the tokenized text. The main task at this level is the POS tagging. POS tagging is a prerequisite for dividing grammatical words like determiners (the, a, those) or prepositions, from lexical words like nouns, verbs or adjectives, since the latter tend to be the content-bearing units in a text. Stemming of in ected words is a major challenge in this context, raising the problem of deciding between a full form lexicon or tools for morphological analysis. Most of today s POS tagging systems use statistical methods as well as heuristics for this task, employing both suf x-stripping algorithms, word form tables and frequency lists. In addition to the pure POS tagging, many IE systems perform other forms of morphological analysis on the tokens, for example, compounds analysis. Syntactic Analysis The goal of syntactic analysis is to determine the underlying structure in a string of words. There is a wide range of grammar formalisms applied in computational linguistics. On the one hand, there are declarative uni cation-based formalisms like, for example, lexical functional grammar (LFG) (Kaplan and Bresnan, 1982) or head-driven phrase structure grammar (HPSG)
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Data Type Char(n) Nchar(n) varchar(n) varchar(max) nvarchar(n) Description Fixed-length character data up to 8,000 characters long using collation character set Unicode fixed-length character data Variable-length character data up to 8,000 characters long using collation character set Variable-length character data up to 2GB in length using collation character set Unicode variable-length character data up to 8,000 characters long using collation character set Unicode variable-length character data up to 2GB in length using collation character set Variable-length character data up to 2,147,483,647 characters in length Unicode variable-length character data up to 1,073,741,823 characters in length A Microsoft user-defined data type used for table and column names that is the equivalent of nvarchar(128) Size in Bytes Defined length * 1 byte Defined length * 2 bytes 1 byte per character 1 byte per character 2 bytes per character
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6. Enter any notes about this task that you want to make in the Notes text box.
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Terminal provides access to the command-line interface for the Mac operating system. If you re not familiar with the conventions of typing commands and working with text-based feedback, Terminal may seem like a foreign land to you. Hang in there: I ll introduce the command line gradually throughout this book. With enough practice, you ll find yourself appreciating the advantages of the command line and know when it provides the best solution for your needs. For remote administration, Terminal is a necessity, so I ll spend as much time as possible using it for working with online resources.
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obtain locks either by explicitly locking these resources, or more conveniently, by a begin of transaction (BOT) that is accompanied with a list of needed resources. If not all resources can be assigned to the calling client, the BOT fails and the transaction is immediately aborted. Otherwise the server locks the needed resources for the client, so that no other client can manipulate them until the end of the transaction is reached. The client can now manipulate the locked resources until it commits the transaction. After a commit, all locked resources are freed again and the operations performed in the body of the transaction are actually applied to the data model. Afterwards, events are created to inform the other clients of the modi cations performed. If the transaction needs to be aborted by the client, all operations are undone, all locks are removed, and no events are red. Transactions may be nested to make complex operations possible without the need for rollback mechanisms. For example, the data model procedure of moving a concept from one super-concept to another one consists of two subtransactions (remove a super-concept relationship to the rst super-concept and establish a new one for the second concept) that must be performed all together or none at all. Because of the necessity for nested transactions, we implemented a strict two phase locking protocol (S2PL). In this protocol, additional resources can be achieved (and locked) within the body of a transaction. Our implementation of the S2PL allows for arbitrarily nested transactions. The execution of inner transactions and the release of all locked resources is postponed until the outermost commit or abort is nally reached. Again, only after the nal commit are events sent to the other clients. We employ the S2PL because (i) it allows for nested transactions and (ii) prevents cascading aborts. Thus, clients can be immediately informed if a planned operation will commit or is prohibited due to unavailable resources. (iii) S2PL also prevents deadlocks since resources are only locked in a BOT if all locks can be achieved. Other locking protocols are either too in exible (like conservative locking (C2PL) that cannot lock resources in addition to the locks of the BOT and thus, is not suitable for nested transactions) or provide chances for deadlocks that must be appropriately handled. To reduce communication overhead, save bandwidth and because transactions are relatively short lived, no information about transactions (especially not about locked objects within a BOT) is communicated from the server to other clients; that is, the local view on locking information within a client (cf. Figure 7.4) contains all resources that are locked by this client (by a BOT) but none that have been locked by a BOT of any other client. Nevertheless, another kind of locking information isdistributed to all clients: An ontologist can lock a whole sub-tree of the concept hierarchy. The server informs all clients of this locking operation.
The JavaScript code between the <script> tags tells users which browser and browser version they are using. Every user sees something different, depending on the particular browser type. What is output to the user's browser is generated the moment the page is called into the browser. Cross For an outstanding book on JavaScript, see JavaScript Reference Bible (published by Hungry Minds, Inc.) by Danny Goodman and Brendan Eich.
Part IV
Figure 7-46: Selecting handout documents.
Is your site visually attractive Is your site s topic apparent at first glance Are your file names based on relevant keywords Try to avoid frames. If you can t or don t want to, use appropriate workarounds Include a site-map page. Include a robots.txt file. Avoid delays and dead ends.
30.24 24: GSM Global System for Mobile Communications
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