Micropachycephalosaurus - Facts and Figuress

Micropachycephalosaurus - Facts and Figuress Name: Micropachycephalosaurus (Greek for tiny thick-headed lizard); pronounced MY-cro-PACK-ee-SEFF-ah-low-SORE-us Habitat: Woodlands of Asia Historical Period: Late Cretaceous (80-70 million years ago) Size and Weight: About two feet long and 5-10 pounds Diet: Plants Distinguishing Characteristics: Small size; bipedal posture; unusually thick skull    About Micropachycephalosaurus The nine-syllable name Micropachycephalosaurus may sound like a mouthful, but its not so bad if you break it down into its constituent Greek roots: micro, pachy, cephalo, and saurus. That translates into tiny thick-headed lizard, and fittingly, Micropachycephalosaurus seems to have been the smallest of all the known pachycephalosaurs (otherwise known as bone-headed dinosaurs). For the record, one of the dinosaurs with the shortest given namesMeiwas also bite-sized; make of that what you will! But hold the Jurassic phone: despite its imposing name, Micropachycephalosaurus may turn out not to have been a pachycephalosaur at all, but a very small (and very basal) ceratopsian, or horned, frilled dinosaur. In 2011, paleontologists closely examined the bone-headed dinosaur family tree and were unable to find a convincing place for this multisyllabic dinosaur; they also re-examined the original fossil specimen of Micropachycephalosaurus, and were unable to confirm the existence of a thickened skull (that part of the skeleton was missing from the museum collection). What if, despite this recent classification, Micropachycephalosaurus is re-re-assigned as a true bonehead? Well, because this dinosaur has been reconstructed from a single, incomplete fossil discovered in China (by the famous paleontologist Dong Zhiming), the possibility looms that it may one day be downgradedthat is, paleontologists will agree that its another type of pachycephalosaur entirely. (The skulls of pachycephalosaurs changed as these dinosaurs aged, meaning that a juvenile of a given genus is often incorrectly assigned to a new genus). If Micropachycephalosaurus winds up losing its place in the dinosaur record books, some other multisyllabic dinosaur (possibly Opisthocoelicaudia) will rise up to assume the worlds longest name title.

Disambiguation - Definition and Examples in Language Studies

Disambiguation s in Language Studies In linguistics, the process of determining which sense of a word is being used in a particular context. In computational linguistics, this discriminative process is called word-sense disambiguation (WSD). Examples and Observations: It so happens that our communication, in different languages alike, allows the same word form to be used to mean different things in individual communicative transactions. The consequence is that one has to figure out, in a particular transaction, the intended meaning of a given word among its potentially associated senses. While the ambiguities arising from such multiple form-meaning associations are at the lexical level, they often have to be resolved by means of a larger context from the discourse embedding the word. Hence the different senses of the word service could only be told apart if one could look beyond the word itself, as in contrasting the players service at Wimbledon with the waiters service in Sheraton. This process of identifying word meanings in a discourse is generally known as word sense disambiguation (WSD).(Oi Yee Kwong, New Perspectives on Computational and Cognitive Strategies for Word Sense Disambiguation. Springer, 2013) Lexical Disambiguation and Word-Sense Disambiguation (WSD) Lexical disambiguation in its broadest definition is nothing less than determining the meaning of every word in context, which appears to be a largely unconscious process in people. As a computational problem, it is often described as AI-complete, that is, a problem whose solution presupposes a solution to complete natural-language understanding or common-sense reasoning (Ide and VÃ ©ronis 1998).In the field of computational linguistics, the problem is generally called word sense disambiguation (WSD) and is defined as the problem of computationally determining which sense of a word is activated by the use of the word in a particular context. WSD is essentially a task of classification: word senses are the classes, the context provides the evidence, and each occurrence of a word is assigned to one or more of its possible classes based on the evidence. This is the traditional and common characterization of WSD that sees it as an explicit process of disambiguation with respect to a fix ed inventory of word senses. Words are assumed to have a finite and discrete set of senses from a dictionary, a lexical knowledge base, or an ontology (in the latter, senses correspond to concepts that a word lexicalizes). Application-specific inventories can also be used. For instance, in a machine translation (MT) setting, one can treat word translations as word senses, an approach that is becoming increasingly feasible because of the availability of large multi-lingual parallel corpora that can serve as training data. The fixed inventory of traditional WSD reduces the complexity of the problem, but alternative fields exist . . ..(Eneko Agirre and Philip Edmonds, Introduction. Word Sense Disambiguation: Algorithms and Applications. Springer, 2007) Homonymy and Disambiguation Lexical disambiguation is well suited particularly for cases of homonymy, for instance, an occurrence of bass must be mapped onto either of the lexical items bass1 or bass2, depending on the intended meaning. Lexical disambiguation implies a cognitive choice and is a task that inhibits comprehension processes. It should be distinguished from processes that lead to a differentiation of word senses. The former task is accomplished fairly reliably also without much contextual information while the latter is not (cf. Veronis 1998, 2001). It has also been shown that homonymous words, which require disambiguation, slow down lexical access, while polysemous words, which activate a multiplicity of word senses, speed up lexical access (Rodd e.a. 2002).However, both the productive modification of semantic values and the straightforward choice between lexically different items have in common that they require additional non-lexical information.(Peter Bosch, Productivity, Polysemy, and Predicate Indexicality. Logic, Language, and Computation: 6th International Tbilisi Symposium on Logic, Language, and Computation, ed. by Balder D. ten Cate and Henk W. Zeevat. Springer, 2007) Lexical Category Disambiguation and the Principle of Likelihood Corley and Crocker (2000) present a broad-coverage model of lexical category disambiguation based on the Principle of Likelihood. Specifically, they suggest that for a sentence consisting of words w0 . . . wn, the sentence processor adopts the most likely part-of-speech sequence t0 . . . tn. More specifically, their model exploits two simple probabilities: (i) the conditional probability of word wi given a particular part of speech ti, and (ii) the probability of ti given the previous part of speech ti-1. As each word of the sentence is encountered, the system assigns it that part-of-speech ti, which maximizes the product of these two probabilities. This model capitalizes on the insight that many syntactic ambiguities have a lexical basis (MacDonald et al., 1994), as in (3): (3) The warehouse prices/makes are cheaper than the rest. These sentences are temporarily ambiguous between a reading in which prices or makes is the main verb or part of a compound noun. After being trained on a large corpus, the model predicts the most likely part of speech for prices, correctly accounting for the fact that people understand price as a noun but makes as a verb (see Crocker Corley, 2002, and references cited therein). Not only does the model account for a range of disambiguation preferences rooted in lexical category ambiguity, it also explains why, in general, people are highly accurate in resolving such ambiguities.(Matthew W. Crocker, Rational Models of Comprehension: Addressing the Performance Paradox. Twenty-First Century Psycholinguistics: Four Cornerstones, ed. by Anne Cutler. Lawrence Erlbaum, 2005) Also Known As: lexical disambiguation