Word sense disambiguation (WSD) is crucial for software programs that interpret language. It helps decipher the intended meaning of ambiguous words and phrases, which is challenging for accurate interfaces. WSD can be achieved through a superficial approach or a deep approach using lexicons. Algorithms and surrounding phrases can also aid in interpretation. WSD is important for speech-to-text and text-to-speech software, verbal command interfaces, web search, Semantic Web development, and AI models.
Word sense disambiguation (WSD) underpins software programs designed to interpret language. Ambiguous words or phrases can be understood in many ways, even if only one meaning is intended. Disambiguation seeks to decipher the intended meaning of words and phrases. This area is extremely challenging for programmers tasked with designing accurate interfaces to bridge the gap between spoken and written language and computer-generated translations.
Software designed to convert speech to text can “hear” a user speaking into a microphone and translate spoken words into typed sentences. You dictate punctuation, inserting words like “comma” and “period” where appropriate. Sounds simple enough, except that many words sound exactly the same.
For example, know and don’t or I and eye are phonetically indistinguishable. Word sense disambiguation helps correctly translate “I should know by next week,” using what is basically a set of “if, then” rules that take into account word placement and adjacent words as indicators of the intended word . This type of disambiguation of the meaning of words is known as the “superficial approach” and is quite accurate, but cannot always be relied upon.
Another approach is to apply ‘world knowledge’ or what computer linguistics calls the ‘deep approach’. This approach relies on lexicons such as dictionaries and thesauruses to help determine the proper sense of a word. Unfortunately, designing a deep approach database that is comprehensive enough to provide better accuracy than the shallow approach is not an easy task.
Software that reads text aloud (text-to-speech) also requires disambiguation of the sense of words. The word bass, for example, could mean a musical instrument, a note or a fish. In the latter case it is pronounced differently, leaving WSD to deduce which pronunciation to use. If the phrase you typed is “The bass is heavy,” just a scan of the surrounding phrases might reveal clues, such as finding the words “fishing,” “boat,” “dock,” or, conversely, “band,” “music” or “song. If the program’s word sense disambiguation is not robust enough, or if additional clues are missing, the program may make translation errors.
In addition to the “if, then” rules of the superficial approach, algorithms are also used to determine the correct interpretations. In the example above, an algorithm could find keywords throughout the document that clearly indicate a musical interpretation, or vice versa. Other approaches are also used in the WSD which are basically refinements or extensions of these basic approaches.
Word sense disambiguation is also vital in verbal command interfaces designed to replace the keyboard, not just for conveying simple operating system commands, but in such complex tasks as web search. Other areas where WSD plays a role include Semantic Web development and improved AI models. In fact, any area of science that relies on a linguistic bridge between humans and machines will use word sense disambiguation.
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