This paper proposes a principled approach for analysis of semantic relations between constituents in compound nouns based on lexical semantic structure. One of the difﬁculties of compound noun analysis is that the mechanisms governing the decision system of semantic relations and the representation method of semantic relations associated with lexical and contextual meaning are not obvious.
This paper describes research toward the automatic interpretation of compound nouns using corpus statistics. An initial study aimed at syntactic disambiguation is presented. The approach presented bases associations upon thesaurus categories. Association data is gathered from unambiguous cases extracted from a corpus and is then applied to the analysis of ambiguous compound nouns. While the work presented is still in progress, a first attempt to syntactically analyse a test set of 244 examples shows 75% correctness.
There is little consensus on a standard experimental design for the compound interpretation task. This paper introduces wellmotivated general desiderata for semantic annotation schemes, and describes such a scheme for in-context compound annotation accompanied by detailed publicly available guidelines. Classiﬁcation experiments on an open-text dataset compare favourably with previously reported results and provide a solid baseline for future research.
We propose a novel method for automatically interpreting compound nouns based on a predeﬁned set of semantic relations. First we map verb tokens in sentential contexts to a ﬁxed set of seed verbs using WordNet::Similarity and Moby’s Thesaurus. We then match the sentences with semantic relations based on the semantics of the seed verbs and grammatical roles of the head noun and modiﬁer. Based on the semantics of the matched sentences, we then build a classiﬁer using TiMBL.
Research on the discovery of terms from corpora has focused on word sequences whose recurrent occurrence in a corpus is indicative of their terminological status, and has not addressed the issue of discovering terms when data is sparse. This becomes apparent in the case of noun compounding, which is extremely productive: more than half of the candidate compounds extracted from a corpus are attested only once. We show how evidence about established (i.e.
There are several theories regarding what inﬂuences prominence assignment in English noun-noun compounds. We have developed corpus-driven models for automatically predicting prominence assignment in noun-noun compounds using feature sets based on two such theories: the informativeness theory and the semantic composition theory. The evaluation of the prediction models indicate that though both of these theories are relevant, they account for different types of variability in prominence assignment. ...
We present an algorithm for automatically disambiguating noun-noun compounds by deducing the correct semantic relation between their constituent words. This algorithm uses a corpus of 2,500 compounds annotated with WordNet senses and covering 139 different semantic relations (we make this corpus available online for researchers interested in the semantics of noun-noun compounds). The algorithm takes as input the WordNet senses for the nouns in a compound, ﬁnds all parent senses (hypernyms) of those senses, and searches the corpus for other compounds containing any pair of those senses.
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.
The automatic interpretation of noun-noun compounds is an important subproblem within many natural language processing applications and is an area of increasing interest. The problem is difﬁcult, with disagreement regarding the number and nature of the relations, low inter-annotator agreement, and limited annotated data. In this paper, we present a novel taxonomy of relations that integrates previous relations, the largest publicly-available annotated dataset, and a supervised classiﬁcation method for automatic noun compound interpretation.
There are some sorts of ‘Preposition + Noun’ combinations in Farsi that apparently a Prepositional Phrase almost behaves as Compound Prepositions. As they are not completely behaving as compounds, it is doubtful that the process of word formation is a morphological one. The analysis put forward by this paper proposes “incorporation” by which an No is incorporated to a Po constructing a compound preposition. In this way tagging prepositions and parsing texts in Natural Language Processing is defined in a proper manner. ...
This paper investigates the use of machine learning algorithms to label modifier-noun compounds with a semantic relation. The attributes used as input to the learning algorithms are the web frequencies for phrases containing the modifier, noun, and a prepositional joining term. We compare and evaluate different algorithms and different joining phrases on Nastase and Szpakowicz’s (2003) dataset of 600 modifier-noun compounds.
A variety of statistical methods for noun compound anMysis are implemented and compared. The results support two main conclusions. First, the use of conceptual association not only enables a broad coverage, but also improves the accuracy. Second, an analysis model based on dependency grammar is substantially more accurate than one based on deepest constituents, even though the latter is more prevalent in the literature.
A method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. The resulting quasi-semantic classification of nouns demonstrates the plausibility of the distributional hypothesis, and has potential application to a variety of tasks, including automatic indexing, resolving nominal compounds, and determining the scope of modification. 1. I N T R O D U C T I O N A variety of linguistic relations apply to sets of semantically similar words. ...
Compounded words are a challenge for NLP applications such as machine translation (MT). We introduce methods to learn splitting rules from monolingual and parallel corpora. We evaluate them against a gold standard and measure their impact on performance of statistical MT systems. Results show accuracy of 99.1% and performance gains for MT of 0.039 BLEU on a German-English noun phrase translation task.
We are paricular grateful to jeanne McCarten and geraldine mark at cambridge university Press who provide us with so much clear-sighted help anh creative guidance at all stages during the wring of this book, we should also like to thank stuart redman for his thorough anh invaluable report on the initial manuscript.
A gerund phrase is a gerund plus any complements or modifiers: Singing the national
anthem is traditional at many sports events. An infinitive is a verbal that is usually preceded by the word to. It is used as a noun, an
adjective, or an adverb: I never learned to dance. (noun) She has an errand to
run. (adjective) I will be happy to help. (adverb).
Trong tiếng Anh những chữ như "room number" (số phòng) gồm hai phần: "room" và "number". Từ "room" bổ nghĩa cho từ "number", ngược với thứ tự tiếng Việt là tính từ thường đi sau danh từ. Tuy nhiên, những chữ gốc Hán Việt thì tính từ đi trước danh từ như: "tiểu học".
"Trường tiểu học" = trườnghọc], "tiểu học" bổ nghĩa cho "trường" (Vietnamese order), nhưng trong "tiểu học", "tiểu" bổ nghĩa cho "học" (Chinese order)....
There is an answer key for all the exercises apart from the
translation exercise at the end of each unit test.
Each test has a total score of 100.
These tests may be photocopied freely for classroom use.
They may not be adapted, printed, or sold without the
permission of Oxford University Press. Complete the sentences with a compound noun or
adjective formed from life, house, or home. Make sure
you spell the word correctly (one word, two words, or
with a hyphen).
By the end of the lesson, the students are able to make compound nouns with the words space and air. II. By the end of the lesson, the students are able to distinguish and use can, could and be able to. Lexical items : - foggy - disturb - stuck - grammar
WORD STRESS 2 Trong bài học này chúng ta sẽ xem xét về cách nhấn trọng âm trong các từ ghép gồm: - Danh từ ghép (Compound nouns) - Động từ ghép/ Cụm động từ (Compound verbs/ Phrasal verbs) - Tính từ ghép