Index of papers in Proc. ACL 2013 that mention
  • noun phrases
Radziszewski, Adam
Abstract
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.
Conclusions and further work
We presented a novel approach to lemmatisation of Polish noun phrases .
Introduction
Similar task may be defined for whole noun phrases (Degorski, 2011).
Introduction
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.
Phrase lemmatisation as a tagging problem
One of the assumptions of KPWr annotation is that actual noun phrases and prepositional phrases are labelled collectively as NP chunks.
Phrase lemmatisation as a tagging problem
To obtain real noun phrases , phrase-initial prepositions must be stripped off3.
Related works
Other named entity types may be realised as arbitrary noun phrases .
Related works
As he notes, organisation names are often built of noun phrases , hence it is important to understand their internal structure.
noun phrases is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Nakashole, Ndapandula and Tylenda, Tomasz and Weikum, Gerhard
Detection of New Entities
To detect noun phrases that potentially refer to entities, we apply a part-of-speech tagger to the input text.
Introduction
However, state-of-the-art open IE methods extract all noun phrases that are likely to denote entities.
Introduction
ture are typed noun phrases .
Introduction
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.
Related Work
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.
Related Work
Noun phrases in the subject role in a large collection of fact triples are heuristically linked to Freebase entities.
noun phrases is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Weller, Marion and Fraser, Alexander and Schulte im Walde, Sabine
Introduction
In this paper, we focus on improving case prediction for noun phrases (NPs) in German translations.
Introduction
German sentences exhibit a freer constituent order, and thus case is an important indicator of the grammatical functions of noun phrases .
Introduction
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.
Using subcategorization information
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).
Using subcategorization information
In addition to modelling subcategorization information, it is also important to differentiate between subcategorized noun phrases (such as object or subject), and noun phrases
noun phrases is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Cheung, Jackie Chi Kit and Penn, Gerald
Distributional Semantic Hidden Markov Models
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
Distributional Semantic Hidden Markov Models
We assume that event heads are verbs or event nouns, while arguments are the head words of their syntactically dependent noun phrases .
Guided Summarization Slot Induction
First, the maximal noun phrases are extracted from the contributors and clustered based on the TAC slot of the contributor.
Guided Summarization Slot Induction
These clusters of noun phrases then become the gold standard clusters against which automatic systems are compared.
Guided Summarization Slot Induction
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.
noun phrases is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Lei, Tao and Long, Fan and Barzilay, Regina and Rinard, Martin
Model
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.
Model
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.
Model
Total # of words 7330 Total # of noun phrases 1829 Vocabulary size 781 Avg.
Problem Formulation
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.
Problem Formulation
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).
noun phrases is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Huang, Hongzhao and Wen, Zhen and Yu, Dian and Ji, Heng and Sun, Yizhou and Han, Jiawei and Li, He
Target Candidate Ranking
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.
Target Candidate Ranking
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.
Target Candidate Ranking
We extract entities, events, and nonentity noun phrases that occur in more than one tweet as neighbors.
noun phrases is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Flati, Tiziano and Navigli, Roberto
Large-Scale Harvesting of Semantic Predicates
We search the English Wikipedia for all the token sequences which match n, resulting in a list of noun phrases filling the * argument.
Large-Scale Harvesting of Semantic Predicates
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.
Preliminaries
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.
noun phrases is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: