Index of papers in Proc. ACL that mention
  • extraction system
Liao, Shasha and Grishman, Ralph
Abstract
Most current event extraction systems rely on local information at the phrase or sentence level.
Conclusion and Future Work
Experiments show that document-level information can improve the performance of a sentence-level baseline event extraction system .
Cross-event Approach
Our event extraction system is a two-pass system where the sentence-level system is first applied to make decisions based on local information.
Introduction
Most current event extraction systems are based on phrase or sentence level extraction.
Introduction
Several recent studies use high-level information to aid local event extraction systems .
Introduction
We extend these approaches by introducing cross-event information to enhance the performance of multi-event-type extraction systems .
Related Work
Almost all the current ACE event extraction systems focus on processing one sentence at a time (Grishman et al., 2005; Ahn, 2006; Hardy et al.
Related Work
They used this technique to augment an information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints.
extraction system is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Berg-Kirkpatrick, Taylor and Gillick, Dan and Klein, Dan
Data
To train the extractive system described in Section 2, we use as our labels y* the extractions with the largest bigram recall values relative to the sets of references.
Experiments
But, importantly, the gains achieved by the joint extractive and compressive system in content-based metrics do not come at the cost of linguistic quality when compared to purely extractive systems .
Experiments
The joint extractive and compressive system fits more word types into a summary than the extractive systems , but also produces longer sentences on average.
Experiments
Reading the output summaries more carefully suggests that by learning to extract and compress jointly, our joint system has the flexibility to use or create reasonable, medium-length sentences, whereas the extractive systems are stuck with a few valuable long sentences, but several less productive shorter sentences.
Introduction
For example, Zajic et al (2006) use a pipeline approach, preprocessing to yield additional candidates for extraction by applying heuristic sentence compressions, but their system does not outperform state-of-the-art purely extractive systems .
Introduction
A second contribution of the current work is to show a system for jointly learning to jointly compress and extract that exhibits gains in both ROUGE and content metrics over purely extractive systems .
Introduction
learns parameters for compression and extraction jointly using an approximate training procedure, but his results are not competitive with state-of-the-art extractive systems , and he does not report improvements on manual content or quality metrics.
Joint Model
Learning weights for Objective 1 where Y(:c) is the set of extractive summaries gives our LEARNED EXTRACTIVE system .
extraction system is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Cheung, Jackie Chi Kit and Penn, Gerald
Experiments
In our study, we compared the characteristics of summaries generated by the eight human summarizers with those generated by the peer summaries, which are basically extractive systems .
Experiments
Purely extractive systems would thus be expected to score 1.0, as would systems that perform text compression by remov-
Experiments
Peer 2 shows a relatively high level of aggregation despite being an extractive system .
Introduction
In automatic summarization, centrality has been one of the guiding principles for content selection in extractive systems .
Related Work
Domain-dependent template-based summarization systems have been an alternative to extractive systems which make use of rich knowledge about a domain and information extraction techniques to generate a summary, possibly using a natural language generation system (Radev and McKeown, 1998; White et al., 2001; McKeown et al., 2002).
Related Work
Several studies complement this paper by examining the best possible extractive system using current evaluation measures, such as ROUGE (Lin and Hovy, 2003; Conroy et al., 2006).
Related Work
They find that the best possible extractive systems score higher or as highly than human summarizers, but it is unclear whether this means the oracle summaries are actually as useful as human ones in an extrinsic setting.
extraction system is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Almeida, Miguel and Martins, Andre
Conclusions
Results show that the state of the art is improved in automatic and manual metrics, with speeds close to extractive systems .
Experiments
The bottom rows show the results achieved by our implementation of a pure extractive system (similar to the learned extractive summarizer of Berg-Kirkpatrick et al., 2011); a system that post-combines extraction and compression components trained separately, as in Martins and Smith (2009); and our compressive summarizer trained as a single task, and in the multitask setting.
Experiments
The ROUGE and Pyramid scores show that the compressive summarizers (when properly trained) yield considerable benefits in content coverage over extractive systems , confirming the results of Berg-Kirkpatrick et al.
Experiments
Our ROUGE-2 score (12.30%) is, to our knowledge, the highest reported on the TAG-2008 dataset, with little harm in grammaticality with respect to an extractive system that preserves the original sentences.
Introduction
Up to now, extractive systems have been the most popular in multi-document summarization.
Introduction
However, extractive systems are rather limited in the summaries they can produce.
Introduction
All approaches above are based on integer linear programming (ILP), suffering from slow runtimes, when compared to extractive systems .
extraction system is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Feng, Yansong and Lapata, Mirella
Experimental Setup
We randomly selected 12 document-image pairs from the test set and generated captions for them using the best extractive system , and two abstractive systems (word-based and phrase-based).
Results
Table 3 reports mean ratings for the output of the extractive system (based on the KL divergence), the two abstractive systems, and the human-authored gold standard caption.
Results
It is significantly worse than the phrase-based abstractive system (0c < 0.01), the extractive system (0c < 0.01), and the gold standard (0c < 0.01).
Results
Unsurprisingly, the phrase-based system is significantly less grammatical than the gold standard and the extractive system , whereas the latter is perceived as equally grammatical as the gold standard (the difference in the means is not significant).
extraction system is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Hasan, Kazi Saidul and Ng, Vincent
Analysis
Although a few researchers have presented a sample of their systems’ output and the corresponding gold keyphrases to show the differences between them (Witten et al., 1999; Nguyen and Kan, 2007; Medelyan et al., 2009), a systematic analysis of the major types of errors made by state-of-the-art keyphrase extraction systems is missing.
Analysis
To fill this gap, we ran four keyphrase extraction systems on four commonly-used datasets of varying sources, including Inspec abstracts (Hulth, 2003), DUC-2001 news articles (Over, 2001), scientific papers (Kim et al., 2010b), and meeting transcripts (Liu et al., 2009a).
Corpora
Automatic keyphrase extraction systems have been evaluated on corpora from a variety of
Evaluation
In this section, we describe metrics for evaluating keyphrase extraction systems as well as state-of-the-art results on commonly-used datasets.
Evaluation
To score the output of a keyphrase extraction system , the typical approach, which is also adopted by the SemEval—2010 shared task on keyphrase extraction, is (1) to create a mapping between the keyphrases in the gold standard and those in the system output using exact match, and then (2) score the output using evaluation metrics such as precision (P), recall (R), and F-score (F).
Keyphrase Extraction Approaches
A keyphrase extraction system typically operates in two steps: (1) extracting a list of words/phrases that serve as candidate keyphrases using some
extraction system is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Li, Peifeng and Zhu, Qiaoming and Zhou, Guodong
Baseline
As the baseline, we choose a state-of-the-art Chinese event extraction system , as described in Li et al.
Experimentation
For fair comparison, we adopt the same experimental settings as the state-of-the-art event extraction system (Li et al.
Experimentation
Besides, all the experiments on argument extraction are done on the output of the trigger extraction system as described in Li et al.
Experimentation
Table 3 shows the performance of the baseline trigger extraction system and Line 1 in Table 4 illustrates the results of argument identification and role determination based on this system.
Introduction
Section 3 describes a state-of-the-art Chinese argument extraction system as the baseline.
extraction system is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Zhou, Deyu and Chen, Liangyu and He, Yulan
Abstract
However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy.
Experiments
In this section, we first describe the Twitter corpus used in our experiments and then present how we build a baseline based on the previously proposed TwiCal system (Ritter et al., 2012), the state-of-the-art open event extraction system on tweets.
Experiments
To evaluate the performance of the propose approach, we use preeison, recall, and F —measure as in general information extraction systems (Makhoul et al., 1999).
Experiments
It can be observed from Table 2 that by using NW—NER, the performance of event extraction system is improved significantly by 7.5% and 3% respectively on F-measure when evaluated on 3-tuples (without keywords) or 4-tuples (with keywords).
Introduction
We have conducted experiments on a Twitter corpus and the results show that our proposed approach outperforms TwiCal, the state-of-the-art open event extraction system , by 7.7% in F-measure.
extraction system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Mehdad, Yashar and Carenini, Giuseppe and Ng, Raymond T.
Experimental Setup
Moreover, we compare our abstractive system with the first part of our framework (utterance extraction in Figure l), which can be presented as an extractive query-based summarization system (our extractive system ).
Experimental Setup
We also show the results of the version we use in our pipeline (our pipeline extractive system ).
Experimental Setup
In contrast, in the stand alone version ( extractive system ) we limit the number of retrieved sentences to the desired length of the summary.
extraction system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Kim, Seokhwan and Lee, Gary Geunbae
Abstract
This paper proposes a novel graph-based projection approach and demonstrates the merits of it by using a Korean relation extraction system based on projected dataset from an English—Korean parallel corpus.
Conclusions
The feasibility of our approach was demonstrated by our Korean relation extraction system .
Implementation
To demonstrate the effectiveness of the graph-based projection approach for relation extraction, we developed a Korean relation extraction system that was trained with projected annotations from English resources.
Implementation
We obtained 155,409 positive instances from the English sentences using an off-the-shelf relation extraction system , ReVerb 2 (Fader et al., 201 l).
Introduction
To obtain training examples in the resource-poor target language, this approach exploited a cross-lingual annotation projection by propagating annotations that were generated by a relation extraction system in a resource-rich source language.
extraction system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Sun, Ang and Grishman, Ralph and Sekine, Satoshi
Abstract
We present a simple semi-supervised relation extraction system with large-scale word clustering.
Conclusion and Future Work
We have described a semi-supervised relation extraction system with large-scale word clustering.
Feature Based Relation Extraction
(2005), a state-of—the-art feature based relation extraction system .
Introduction
For example, a relation extraction system needs to be able to extract an Employment relation between the entities US soldier and US in the phrase US soldier.
Introduction
The performance of a supervised relation extraction system is usually degraded by the sparsity of lexical features.
extraction system is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Krishnamurthy, Jayant and Mitchell, Tom
Background: Never-Ending Language Learner
NELL is an information extraction system that has been running 24x7 for over a year, using coupled semi-supervised learning to populate an ontology from unstructured text found on the web.
Background: Never-Ending Language Learner
As in other information extraction systems , the category and relation instances extracted by NELL contain polysemous and synonymous noun phrases.
Discussion
In order for information extraction systems to accurately represent knowledge, they must represent noun phrases, concepts, and the many-to-many mapping from noun phrases to concepts they denote.
Introduction
Many information extraction systems construct knowledge bases by extracting structured assertions from free text (e.g., NELL (Carlson et al., 2010), TextRunner (Banko et al., 2007)).
extraction system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Nguyen, Minh Luan and Tsang, Ivor W. and Chai, Kian Ming A. and Chieu, Hai Leong
Introduction
Instead, it can be more cost-effective to adapt an existing relation extraction system to the new domain using a small set of labeled data.
Introduction
This paper considers relation adaptation, where a relation extraction system trained on many source domains is adapted to a new target domain.
Introduction
There are at least three challenges when adapting a relation extraction system to a new domain.
Related Work
Among these studies, Plank and Moschitti’s is the closest to ours because it adapts relation extraction systems to new domains.
extraction system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Pasupat, Panupong and Liang, Percy
Abstract
In order to extract entities of a fine- grained category from semistructured data in web pages, existing information extraction systems rely on seed examples or redundancy across multiple web pages.
Approach
We represent each web page 212 as a DOM tree, a common representation among wrapper induction and web information extraction systems (Sahuguet and Azavant, 1999; Liu et al., 2000; Crescenzi et al., 2001).
Approach
In the literature, many information extraction systems employ more versatile extraction predicates (Wang and Cohen, 2009; Fumarola et al., 2011).
Discussion
In contrast to information extraction systems that extract homogeneous records from web pages (Liu et al., 2003; Zheng et al., 2009), our system must choose the correct field from each record and also identify the relevant part of the page based on the query.
extraction system is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Miyao, Yusuke and Saetre, Rune and Sagae, Kenji and Matsuzaki, Takuya and Tsujii, Jun'ichi
Abstract
Our approach is to measure the impact of each parser when it is used as a component of an information extraction system that performs protein—protein interaction (PPI) identification in biomedical papers.
Evaluation Methodology
In our approach to parser evaluation, we measure the accuracy of a PPI extraction system , in which
Experiments
In the following experiments, we used AImed (Bunescu and Mooney, 2004), which is a popular corpus for the evaluation of PPI extraction systems .
extraction system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Wang, Chang and Fan, James
Identifying Key Medical Relations
The first step in building a relation extraction system for medical domain is to identify the relations that are important for clinical decision making.
Introduction
To construct a medical relation extraction system , several challenges have to be addressed:
Introduction
The medical corpus underlying our relation extraction system contains 80M sentences (ll gigabytes pure text).
extraction system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Banko, Michele and Etzioni, Oren
Abstract
Second, when the number of target relations is small, and their names are known in advance, we show that O-CRF is able to match the precision of a traditional extraction system , though at substantially lower recall.
Relation Extraction
The unique nature of the open extraction task has led us to develop O-CRF, an open extraction system that uses the power of graphical models to identify relations in text.
Relation Extraction
The set of features used by O-CRF is largely similar to those used by O-NB and other state-of-the-art relation extraction systems , They include part-of-speech tags (predicted using a separately trained maximum-entropy model), regular expressions (e. g.detecting capitalization, punctuation, eta), context words, and conjunctions of features occurring in adjacent positions within six words to the left and six words to the right of the current word.
extraction system is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: