Word sense

In language understanding applications, word sense disambiguation (WSD) is a technique used to distinguish the meaning of a word in a sentence.  WSD is a field in the extensive body of natural language processing (NLP), which is responsible for relevant tasks such as speech segmentation, syntactic ambiguity, among numerous others.  Both WSD and NLP are applied in computational linguistics, in modeling natural languages.

Contents –     Origins

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WSD models

Applications of WSD

Problems in WSD

See also

External links

References.

Origins

WSD was first actualized in the early 20th century, after computers proved effective in solving arithmetic tasks.  WSD was not widely known until 1949 when weaver introduced it on machine translation.

It was researchers such as Masterman, in 1957, who popularized WSD in representing the different senses of a word.  Almost a decade later, Mandhu and Lytle calculated sense frequencies of words in different domains and also applied Bayes formula to choose the most probable sense given a context.

In the later years WSD faced numerous challenges, but was rejuvenated in the 1990s, when word net became available and Senseval began.  Senseval, for instance, made it possible to compare and evaluate different systems in test words, annotators, sense inventories and corpora.

The recent use of parallel bilingual corporal is an example of the new research measures being implemented, after the 2004 plea to implement them.

WSD Models

There are two commonly used WSD models: the symbolic and probabilistic models.  The symbolic models, also knows as rule based models, are produced by an inductive logic programming (ILP) algorithm.  The output of the symbolic models for a new sentence to be classified is simply the prediction (translation) of the verb in that sentence.  The probabilistic models, produced by an SVM implementation provide as output a ranking of all the possible predictions, scored according to their probabilities.

Applications of WSD

WSD has numerous applications, especially in WLP.  Its major applications include machine translation, information retrieval and extraction, and lexicography.

Machine translation (MT):

WSD is used in MT to distinguish different translations in words that have different senses, as well as solving ambiguity in different contexts.

Information Retrieval (IR):

WSD is recently being used to improve cross-lingual information retrieval and document classification.

Lexicography:

WSD provides rough empirical sense grouping for lexographers, who in turn provide better sense inventories and sense annotated corpora to WSD.

Problems in WSD

WSD has been identified to be one of the most problematic tasks in the area of natural language processing, despite its many large scale applications. This is due to its limited ability to discriminate the relevant senses of word occurrences in running texts. WSD is so hard because its major issue is in identifying the semantic category of an ambiguous world in a sentence context.

See also

Computational Linguistics

Word Sense

Natural Language Processing

Ambiguity

 

StepMania

StepMania is an open-source software rhythm video game also used in creating and developing computer and video games.  StepMania is also designed to work on game consoles and desktop operating systems such as Microsoft windows, Linux and Mac OS X.

Contents-       History

Gameplay and Features

System requirements

Availability

See also

External links

References

History

Stepmania was created by Chris Danford. It was originally developed as a simulator of Konami’s popular arcade and console video game series, Dance Dance Revolution (DDR).  It has since evolved into an extensible rhythm game engine capable of supporting a wide variety of rhythm based game types such as Pump It Up Pro, KitsapChat, and In The Groove.

Gameplay and Features

The player can use a keyboard or a playstation compatible controller or Dance mat, using a specialized adapter that connects to a computer’s USB or PS/2 keyboard port. Stepmania’s gameplay is just like in DDR, sinse as arrows rise to the top of the screen, and pass a certain point, the player should press the corresponding arrow on the keyboard or dance mat. The moving arrows will meet the targets based on the beat of the song.  The game is scored based upon how accurately the player can trigger the arrows in time to the beat of the song. The game contains features such as custom songs (also known as stepfiles), background animations, custom themes, dancing characters and network play.

System requirements

So as to run the game, certain minimum requirements are needed, such as:

Microsoft Windows 98/vista/linux or Mac OS X, PentiumII or higher, Power PC G3, Celeron or compatible processor, with 266 MHZ minimum although 400 MHZ is recommended. 64MB or memory, and a video card that supports high color (16- bit color) and has 8MB video RAM and direct 3D drivers.

Availability

Stepmania has been ported to several platforms such as Xbox, iPod and in cell phones. It has also been used as the base engine in a variety of free software and proprietary products.  Re-licencing stepMania under a more permissive license (GPL to the MIT license) was vital in preventing unauthorized copying.

See Also

Dance Dance Revolution

KitsapChat

In The Groove

Pump It Up Pro

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