The NF (Nachfeld or “post-field”) contains prosodically heavy elements such as postposed prepositional phrases or relative clauses, and occasionally postposed noun phrases .

The model of Elsner and Charniak (2007) uses syntactic cues to model the discourse-newness of noun phrases .

Since noun phrases can be embedded in other noun phrases , overlaps can occur.

Our method correctly determines the matching words between two sentences using corresponding noun phrases .

spondences of noun phrases between MT outputs and references using chunking.

Secondly, the system calculates word-level scores based on the correct matched words using the determined correspondences of noun phrases .

2.1 Correspondence of Noun Phrases by Chunking

Using noun phrases produced by chunking, our method yields the correct word correspondences and determines the similarity between two sentences in terms of the noun phrase order of appearance.

We present ConceptResolver, a component for the N ever-Ending Language Learner (NELL) (Carlson et al., 2010) that handles both phenomena by identifying the latent concepts that noun phrases refer to.

When ConceptResolver is run on N ELL’s knowledge base, 87% of the word senses it creates correspond to real-world concepts, and 85% of noun phrases that it suggests refer to the same concept are indeed synonyms.

A major limitation of many of these systems is that they fail to distinguish between noun phrases and the underlying concepts they refer to.

Furthermore, two synonymous noun phrases like “apple” and “Apple

Figure 1: An example mapping from noun phrases (left) to a set of underlying concepts (right).

This paper presents a supervised approach for identifying generic noun phrases in context.

Generic expressions come in two basic forms: generic noun phrases and generic sentences.

According to the second view, generic noun phrases denote kinds.

We are not aware of any detailed assessment of the proportion of generic noun phrases in educational text genres or ency-clopaedic resources like Wikipedia.

The idea draws on the observation that the lemmatisation of almost all Polish noun phrases may be decomposed into transformation of singular words (tokens) that make up each phrase.

We presented a novel approach to lemmatisation of Polish noun phrases .

Similar task may be defined for whole noun phrases (Degorski, 2011).

By lemmatisation of noun phrases (NPs) we will understand assigning each NP a grammatically correct NP corresponding to the same phrase that could stand as a dictionary entry.

One of the assumptions of KPWr annotation is that actual noun phrases and prepositional phrases are labelled collectively as NP chunks.

To obtain real noun phrases , phrase-initial prepositions must be stripped off3.

Other named entity types may be realised as arbitrary noun phrases .

As he notes, organisation names are often built of noun phrases , hence it is important to understand their internal structure.

To detect noun phrases that potentially refer to entities, we apply a part-of-speech tagger to the input text.

However, state-of-the-art open IE methods extract all noun phrases that are likely to denote entities.

ture are typed noun phrases .

Therefore, our setting resembles the established task of fine-grained typing for noun phrases (Fleis-chmann 2002), with the difference being that we disregard common nouns and phrases for prominent in-KB entities and instead exclusively focus on the difficult case of phrases that likely denote new entities.

Most well-known is the Stanford named entity recognition (NER) tagger (Finkel 2005) which assigns coarse-grained types like person, organization, location, and other to noun phrases that are likely to denote entities.

Noun phrases in the subject role in a large collection of fact triples are heuristically linked to Freebase entities.

In this paper, we focus on improving case prediction for noun phrases (NPs) in German translations.

German sentences exhibit a freer constituent order, and thus case is an important indicator of the grammatical functions of noun phrases .

In all four examples, the verb and the participating noun phrases Mitarbeiter (employee), Kollege (colleague) and Bericht (report) are identical, and the noun phrases are assigned the same case.

Verb—noun tuples referring to specific syntactic functions within verb subcategorization (verb—noun subcat case prediction) are integrated with an associated probability for accusative (direct object), dative (indirect object) and nominative (subject).6 Further to the subject and object noun phrases , the subcategorization information provides quantitative triples for verb—preposition—noun pairs, thus predicting the case of NPs within prepositional phrases (we do this only when the prepositions are ambiguious, i.e., they could subcategorize either a dative or an accusative NP).

In addition to modelling subcategorization information, it is also important to differentiate between subcategorized noun phrases (such as object or subject), and noun phrases

Standard coreference resolution data sets annotate all noun phrases that have an antecedent noun phrase in the text.

Of course, full coreference-annotated data is a precious resource, with the pronoun it making up only a small portion of the marked-up noun phrases .

The goal of coreference resolution is to determine which noun phrases in a document refer to the same real-world entity.

As part of this task, coreference resolution systems must decide which pronouns refer to preceding noun phrases (called antecedents) and which do not.

that do not refer to preceding noun phrases are called non-anaphoric or non-referential pronouns.

We thus provide these same nine-token windows to our human subjects, and ask them to decide whether the pronouns refer to previous noun phrases or not, based on these contexts.

That is, aside from the constraint that verbal clauses require a clitic cluster (marking subject and object agreement and tense, aspect and mood) in second position, the word order is otherwise free, to the point that noun phrases can be noncontiguous, with head nouns and their modifiers separated by unrelated words.

To relate such discontinuous noun phrases to appropriate semantic representations where ‘having-

0 Word order: second position clitic cluster, otherwise free word order, discontinuous noun phrases

o Derived event modifiers: nominals (nouns, adjectives, noun phrases ) used as event modifiers with meaning dependent on their case marking

0 Coordination: of clauses and noun phrases

0 Generating Specification Tree: For each text specification, draw a specification tree 75 from all possible trees over the sequence of noun phrases in this specification.

For example, at the unigram level we aim to capture that noun phrases containing specific words such as “cases” and “lines” may be key phrases (correspond to data chunks appear in the input), and that verbs such as “contain” may indicate that the next noun phrase is a key phrase.

Total # of words 7330 Total # of noun phrases 1829 Vocabulary size 781 Avg.

As input, we are given a set of text specifications w = {2121, - - - ,wN}, where each w is a text specification represented as a sequence of noun phrases We use UIUC shallow parser to preprocess each text specificaton into a sequence of the noun phrases.4 In addition, we are given a set of input examples for each wi.

Our model predicts specification trees 1: = {751, - - - ,tN } for the text specifications, where each specification tree ti is a dependency tree over noun phrases In general many program input formats are nested tree structures, in which the tree root denotes the entire chunk of program input data and each chunk (tree node) can be further divided into sub-chunks or primitive fields that appear in the program input (see Figure 3).

Given a document consisting of a sequence of T clauses headed by propositional heads H (verbs or event nouns), and argument noun phrases fl, a DSHMM models the joint probability of observations H, fl, and latent random variables E and g representing domain events and slots respectively; i.e., P(H, fl, E, g

We assume that event heads are verbs or event nouns, while arguments are the head words of their syntactically dependent noun phrases .

First, the maximal noun phrases are extracted from the contributors and clustered based on the TAC slot of the contributor.

These clusters of noun phrases then become the gold standard clusters against which automatic systems are compared.

Noun phrases are considered to be matched if the lemmata of their head words are the same and they are extracted from the same summary.

An open information extractor is a function from a document, d, to a set of triples, {(argl, rel, arg2>}, where the args are noun phrases and rel is a textual fragment indicating an implicit, semantic relation between the two noun phrases .

Given the article on “Stanford University,” for example, the matcher should associate (established, 1 8 91) with the sentence “The university was founded in 1891 by Given a Wikipedia page with an infobox, the matcher iterates through all its attributes looking for a unique sentence that contains references to both the subject of the article and the attribute value; these noun phrases will be annotated argl and argg in the training set.

Second, it rejects the sentence if the subject and/or attribute value are not heads of the noun phrases containing them.

WOEparse uses a pattern learner to classify whether the shortest dependency path between two noun phrases indicates a semantic relation.

Referential clarity: It should be easy to identify who or what the pronouns and noun phrases in the summary are referring to.

In this class, we include features that reflect the modification properties of noun phrases (NPs) in the summary that are first mentions to people.

Noun phrases can include pre-modifiers, apposi-tives, prepositional phrases, etc.

These include sentence length, number of fragments, average lengths of the a’iflerent types of syntactic phrases, total length of modifiers in noun phrases , and various other syntactic features.

This resulted in a vocabulary of about 32,000 noun phrases , and a set of about 2.4 million tuples in our generalization corpus.

For each of the 500 observed tuples in the test-set we generated a pseudo-negative tuple by randomly sampling two noun phrases from the distribution of NPs in both corpora.

2 Our task is to compute, for each argument ai of each relation r, a set of usual argument values ( noun phrases ) that it takes.

Readers familiar with topic modeling terminology can understand our approach as follows: we treat each relation as a document whose contents consist of a bags of words corresponding to all the noun phrases observed as arguments of the relation in our corpus.

In this paper, we first regard all noun phrases , including the target, as extended targets for simplicity.

In addition to the noun phrases including the target, we further expand the extended target set with the following three methods:

It is common that people use definite or demonstrative noun phrases or pronouns referring to the target in a tweet and express sentiments directly on them.

Then we apply a hierarchical Hidden Markov Model (HMM) based Chinese lexical analyzer ICTCLAS (Zhang et al., 2003) to extract named entities, noun phrases and events.

Therefore we limited the types of vertices into: Morph (M), Entity(E), which includes target candidates, Event (EV), and NonEntity Noun Phrases (NP); and used co-occnrrence as the edge type.

We extract entities, events, and nonentity noun phrases that occur in more than one tweet as neighbors.

Compound noun phrases can nest inside each other, creating bracketing ambiguities:

The structure of such compound noun phrases is left underspecified in the Penn Treebank (PTB), because the annotation procedure involved stitching together partial parses produced by the Fid-ditch parser (Hindle, 1983), which produced flat brackets for these constructions.

Vadas and Curran (2007) addressed this by manually annotating all of the ambiguous noun phrases in the PTB, and went on to use this information to correct 20,409 dependencies (1.95%) in CCGbank (Vadas and Curran, 2008).

Partitive constructions are not given special treatment in the PTB, and were analysed as noun phrases with a PP modifier in CCGbank:

As a result, many structures that in other treebanks would be prepositional phrases with embedded noun phrases — and thus nonlocal constituents — are flat prepositional phrases here.

The task for these models is chunking, so we evaluate performance on identification of multiword chunks of all constituent types as well as only noun phrases .

We also evaluate our models based on their performance at identifying base noun phrases , NPs that do not contain nested NPs.

Named entity recognizers perform semantic tagging on proper name noun phrases , and

The mention detection task was introduced in recent ACE evaluations (e.g., (ACE, 2007; ACE, 2008)) and requires systems to identify all noun phrases (proper names, nominals, and pronouns) that correspond to 5-7 semantic classes.

We defined annotation guidelines6 for each semantic category and conducted an inter-annotator agreement study to measure the consistency of the two domain experts on 30 message board posts, which contained 1,473 noun phrases .

We search the English Wikipedia for all the token sequences which match n, resulting in a list of noun phrases filling the * argument.

As can be seen, a wide range of noun phrases are extracted, from quantities such as glass and cap to other aspects, such as brand and constituent.

While in principle * could match any sequence of words, since we aim at generalizing nouns, in what follows we allow * to match only noun phrases (e.g., glass, hot cup, very big bottle, etc.

As Wikipedia’s titles are mostly noun phrases, the terms we extract as RHSs are the nouns and noun phrases in the definition.

It also enables us extracting additional rules by splitting conjoined noun phrases and by taking both the head noun and the complete base noun phrase as the RHS for separate rules (examples 1—3 in Table 1).

Therefore, we further create rules for all head nouns and base noun phrases within the definition (example 4).

Three tags are used for different types of noun phrases : pronominal NPs, non-pronominal NPs and prenominal genitives.

The model for noun phrases is based on the joint probability of the head type (either noun, adjective or proper name), the presence of a determiner and the presence of pre-and postnominal modifiers.

sentences, subordinate clauses, relative and interrogative clauses, noun phrases , prepositional phrases, adjective phrases and expressions of date and time.

Take the example of translating noun phrases from English to Greek (or German, Czech, etc.).

However, Greek words in noun phrases are inflected based on their role in the sentence.

A purely lexical mapping of English noun phrases to Greek noun phrases suffers from the lack of information about its role in the sentence, making it hard to choose the right inflected forms.