LINKING WORDS AND PHRASES ARE

participle ofspin.spur ["sp#] 1. n. a sharp objectworn on the heel of a boot, used tomake a horse that one is riding gofaster. 2. n. a highway or railroadtrack that branches from the mainone. 3. tv. to poke a horse with Q.→ on the spur of the momentspy ["spaI] 1. n. someone whose jobis to secretly w[r]

typical of someone or something.unlikely [@n "laIk li] adj. not proba-ble; not likely; likely to fail; notlikely to succeed.unlimited [@n "lIm @ tId] adj. notlimited; without limits; notrestricted. (Adv: unlimitedly.)unlock [@n "lak] tv. to open a lock.unlucky [@n "l^k i] 1. adj. notlucky; not havin[r]

→ stuck in traffictraffic jam ["trAf Ik dZAm] n. a sit-uation in which vehicles on a roadhave stopped or slowed downbecause there are too many ofthem.traffic light ["trAf Ik laIt] n. a sig-nal, usually found at intersections,used to control traffic by a systemof lights. (Can be shortened toli[r]

chip NOT computer, siliconis treated differently from a query forchip NOT computer, potato.Vector negation is capable of realising that for thefirst query, the two negative terms are referring tothe same general topic area, but in the second casethe task is to remove radically different meanings[r]

has frozen into small white flakesthat fall from the sky. (No plural.)2. n. an instance of Q falling fromthe sky; a coating of Q on theground. 3. iv. [for Q] to fall fromthe sky.→ (as) white as the driven snowsnow blower ["sno blo #] n. amachine that clears snow fromwalks and pavements.snow so[r]

somethingout to obliteratesomething by rubbing.rub (up) against someone orsomething to bump or scrapeagainst someone or something.rubber ["r^b #] 1. n. a waterproofmaterial that goes back to its origi-nal shape when stretched orpressed. (No plural.) 2. adj. madefrom Q.rubber band ["r^b # "bAnd] n. a[r]

the semantics of topics and it performs well on topic spotting task. It is well known that human expert, whose most prominent characteristic is the ability to understand text documents, have a strong natural ability to spot topics in documents. We are, however, unclear about the nature[r]

better at identifying multi-word clusters withinfixes (72.72% - compare with 9.09% forAdamson).The results suggest that the Two-stagealgorithm preserves the strengths of Adamsonand Boreham (1994), whilst adding a markedadvantage in recognising infixes. The outcomeof the evaluation on fourth and