What is a noun? It's easier to show than explain--and this book is brimming with examples. Author Brian Cleary and illustrator Jenya Promitsky creatively clarify the concept of nouns for young readers. Nouns are printed in color for easy identification, and the playful rhymes and illustrations combine to highlight key words.
A simple sentence has one clause, beginning with a noun group called the subject. The subject is the person or thing that the sentence is about. This is followed by a verb group, which tells you what the subject is doing, or describes the subject's situation.
Knowledge of the anaphoricity of a noun phrase might be proﬁtably exploited by a coreference system to bypass the resolution of non-anaphoric noun phrases. Perhaps surprisingly, recent attempts to incorporate automatically acquired anaphoricity information into coreference systems, however, have led to the degradation in resolution performance. This paper examines several key issues in computing and using anaphoricity information to improve learning-based coreference systems. In particular, we present a new corpus-based approach to anaphoricity determination.
In machine translation and man-machine dialogue, it is important to clarify referents of noun phrases. We present a method for determining the referents of noun phrases in Japanese sentences by using the referential properties, modifiers, and possessors 1 of noun phrases. Since the Japanese language has no articles, it is difficult to decide whether a noun phrase has an antecedent or not. We had previously estimated the referential properties of noun phrases that correspond to articles by using clue words in the sentences (Murata and Nagao 1993). ...
Noun phrases consisting of a sequence of nouns (sometimes referred to as nominal compounds) pose considerable difficulty for language analyzers but are common in many technical domains. The problems are compounded when some of the nouns in the sequence are ambiguously also verbs. The phrasal approach to language analysis, as implemented in PHRAN (PHRasal ANalyzer), has been extended to handle the recognition and partial analysis of such constructions. The phrasal analysis of a noun sequence is performed to an extent sufficient for continued analysis of the sentence in which it appears.
Prepositions are a class of words that indicate relationships between nouns, pronouns and other words in a sentence. Most often they come before a noun. They never change their form, regardless of the case, gender etc. of the word they are referring to. Each definition of a preposition is followed by one or more patterns, which indicate the
word order appropriate for the definition.
The verbs in each pattern can be changed to other tenses.
∙ Danh từ đếm được: Là danh từ có thể dùng được với số đếm, do đó nó có 2 hình thái số ít và số nhiều. Nó dùng được với a hay với the.
VD: one book, two books
∙ Danh từ không đếm được: Không dùng được với số đếm, do đó nó không có hình thái số ít, số nhiều. Nó không thể dùng được với a, còn the chỉ trong một số trường hợp đặc biệt.
PreSchool-Grade 2–This ambitious book provides colorful collages, hidden letters, word pictures, and alphabet sentences presented in rhyme while avoiding many of the pitfalls of the genre. By melding together nonsensical sentences that are as wacky as the illustrations, Cleary opens up the field to using verbs and adjectives as well as nouns. E is for each evergreen Elvis potted. K starts karate and kangaroos kissing, and kilt-wearing kittens whose kickstands are missing. Both upper- and lowercase are highlighted. The pre-title page invites readers to play along.
I love the humor and wit in all of Brian Cleary's language books. The picture and verse are hilarious and the examples are clear. Kids really enjoy these books and, because they are so engaging, they make a good introduction to teaching parts of speech. I have them all and use them with students ages 7/8 - High School.
Simultaneous actions described by absolute phrases:
An absolute phrase consists of a head - word (often a
noun) plus at least one other word. Note that the head
word in the absolute phrase denotes something which is a
part of, or belong to the thing or person that is the subject
of the finite verb of the sentence.
A Learner's Polish-English Dictionary contains over 27,000 entries. It is intended primarily for the use
of the English-speaking reader of Polish, interested in arriving at the central or commonest meaning of
a word, not in an exhaustive set of usages and definitions. It does not attempt to cover technical or
scientific terms, or the names of uncommon plants and animals. Most terms related to the social sciences
and the humanities are included. It is expected that the user will be familiar with the principles of
A great deal of work has been done demonstrating the ability of machine learning algorithms to automatically extract linguistic knowledge from annotated corpora. Very little work has gone into quantifying the difference in ability at this task between a person and a machine. This paper is a first step in that direction.
We describe the early stage of our methodology of knowledge acquisition from technical texts. First, a partial morpho-syntactic analysis is performed to extract "candidate terms". Then, the knowledge engineer, assisted by an automatic clustering tool, builds the "conceptual fields" of the domain. We focus on this conceptual analysis stage, describe the data prepared from the results of the morpho-syntactic analysis and show the results of the clustering module and their interpretation.
The research focus of computational coreference resolution has exhibited a shift from heuristic approaches to machine learning approaches in the past decade. This paper surveys the major milestones in supervised coreference research since its inception ﬁfteen years ago.
Resolving polysemy and synonymy is required for high-quality information extraction. We present ConceptResolver, a component for the Never-Ending Language Learner (NELL) (Carlson et al., 2010) that handles both phenomena by identifying the latent concepts that noun phrases refer to. ConceptResolver performs both word sense induction and synonym resolution on relations extracted from text using an ontology and a small amount of labeled data. Domain knowledge (the ontology) guides concept creation by deﬁning a set of possible semantic types for concepts.
Statistical parsing of noun phrase (NP) structure has been hampered by a lack of goldstandard data. This is a signiﬁcant problem for CCGbank, where binary branching NP derivations are often incorrect, a result of the automatic conversion from the Penn Treebank.(N (N/N lung) (N (N/N cancer) (N deaths) ) )This structure is correct for most English NPs and is the best solution that doesn’t require manual reannotation. However, the resulting derivations often contain errors.
This paper addresses the automatic classiﬁcation of semantic relations in noun phrases based on cross-linguistic evidence from a set of ﬁve Romance languages. A set of novel semantic and contextual English– Romance NP features is derived based on empirical observations on the distribution of the syntax and meaning of noun phrases on two corpora of different genre (Europarl and CLUVI). The features were employed in a Support Vector Machines algorithm which achieved an accuracy of 77.9% (Europarl) and 74.
A layered approach to information retrieval permits the inclusion of multiple search engines as well as multiple databases, with a natural language layer to convert English queries for use by the various search engines. The NLP layer incorporates morphological analysis, noun phrase syntax, and semantic expansion based on WordNet.