Index of papers in Proc. ACL that mention
  • semantic role
Connor, Michael and Gertner, Yael and Fisher, Cynthia and Roth, Dan
Abstract
A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents.
Abstract
To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents.
Abstract
In this paper we focus on the parsing and argument-identification steps that precede Semantic Role Labeling (SRL) training.
Conclusion and Future Work
have the luxury of treating part-of-speech tagging and semantic role labeling as separable tasks.
Conclusion and Future Work
An SRL classifier used simple representations built from these identified arguments to extract useful abstract patterns for classifying semantic roles .
Introduction
In this paper we present experiments with an automatic system for semantic role labeling (SRL) that is designed to model aspects of human language acquisition.
Introduction
n Semantic Role Labeling
Introduction
Previous computational experiments with a system for automatic semantic role labeling (BabySRL: (Connor et al., 2008)) showed that it is possible to learn to assign basic semantic roles based on the shallow sentence representations proposed by the structure-mapping view.
Model
We model language learning as a Semantic Role Labeling (SRL) task (Carreras and Marquez, 2004).
Model
The candidate arguments and predicates identified in each input sentence are passed to an SRL classifier that uses simple abstract features based on the number and order of arguments to learn to assign semantic roles .
semantic role is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Kozhevnikov, Mikhail and Titov, Ivan
Abstract
Semantic Role Labeling (SRL) has become one of the standard tasks of natural language processing and proven useful as a source of information for a number of other applications.
Background and Motivation
Semantic role labeling has proven useful in many natural language processing tasks, such as question answering (Shen and Lapata, 2007; Kaisser and Webber, 2007), textual entailment (Sammons et al., 2009), machine translation (Wu and Fung, 2009; Liu and Gildea, 2010; Gao and Vogel, 2011) and dialogue systems (Basili et al., 2009; van der Plas et al., 2009).
Background and Motivation
Multiple models have been designed to automatically predict semantic roles , and a considerable amount of data has been annotated to train these models, if only for a few more popular languages.
Background and Motivation
A number of approaches to the construction of semantic role labeling models for new languages
Setup
The purpose of the study is not to develop a yet another semantic role labeling system — any existing SRL system can (after some modification) be used in this setup — but to assess the practical applicability of cross-lingual model transfer to this problem, compare it against the alternatives and identify its strong/weak points depending on a particular setup.
Setup
2.1 Semantic Role Labeling Model
Setup
We consider the dependency-based version of semantic role labeling as described in Hajic et al.
semantic role is mentioned in 19 sentences in this paper.
Topics mentioned in this paper:
Titov, Ivan and Klementiev, Alexandre
Abstract
Specifically, we consider unsupervised induction of semantic roles from sentences annotated with automatically-predicted syntactic dependency representations and use a state-of-the-art generative Bayesian nonparametric model.
Introduction
Semantic role labeling (SRL) (Gildea and Juraf-sky, 2002) involves predicting predicate argument structure, i.e.
Introduction
and their assignment to underlying semantic roles .
Introduction
Though syntactic representations are often predictive of semantic roles (Levin, 1993), the interface between syntactic and semantic representations is far from trivial.
Monolingual Model
In this section we describe one of the Bayesian models for semantic role induction proposed in (Titov and Klementiev, 2012).
Problem Definition
As we mentioned in the introduction, in this work we focus on the labeling stage of semantic role labeling.
Problem Definition
In the labeling stage, semantic roles are represented by clusters of arguments, and labeling a particular argument corresponds to deciding on its role cluster.
Problem Definition
In sum, we treat the unsupervised semantic role labeling task as clustering of argument keys.
semantic role is mentioned in 22 sentences in this paper.
Topics mentioned in this paper:
Lo, Chi-kiu and Wu, Dekai
Abstract
We introduce a novel semiautomated metric, MEANT, that assesses translation utility by matching semantic role fillers, producing scores that correlate with human judgment as well as HTER but at much lower labor cost.
Abstract
We first show that when using untrained monolingual readers to annotate semantic roles in MT output, the nonautomatic version of the metric HMEANT achieves a 0.43 correlation coefficient with human adequacy judgments at the sentence level, far superior to BLEU at only 0.20, and equal to the far more expensive HTER.
Abstract
We then replace the human semantic role annotators with automatic shallow semantic parsing to further automate the evaluation metric, and show that even the semiautomated evaluation metric achieves a 0.34 correlation coefficient with human adequacy judgment, which is still about 80% as closely correlated as HTER despite an even lower labor co st for the evaluation procedure.
semantic role is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Lang, Joel and Lapata, Mirella
Abstract
In this paper we describe an unsupervised method for semantic role induction which holds promise for relieving the data acquisition bottleneck associated with supervised role labelers.
Abstract
We present an algorithm that iteratively splits and merges clusters representing semantic roles , thereby leading from an initial clustering to a final clustering of better quality.
Abstract
By combining role induction with a rule-based component for argument identification we obtain an unsupervised end-to-end semantic role labeling system.
Introduction
The term is most commonly used to describe the automatic identification and labeling of the semantic roles conveyed by sentential constituents (Gildea and J urafsky, 2002).
Introduction
Semantic roles describe the relations that hold between a predicate and its arguments, abstracting over surface syntactic configurations.
Introduction
window occupies different syntactic positions — it is the object of broke in sentences (la,b), and the subject in (IC) — while bearing the same semantic role , i.e., the physical object affected by the breaking event.
semantic role is mentioned in 36 sentences in this paper.
Topics mentioned in this paper:
Chambers, Nathanael and Jurafsky, Dan
Abstract
Our algorithm instead learns the template structure automatically from raw text, inducing template schemas as sets of linked events (e.g., bombings include detonate, set ofi, and destroy events) associated with semantic roles .
Introduction
A template defines a specific type of event (e.g., a bombing) with a set of semantic roles (or slots) for the typical entities involved in such an event (e.g., perpetrator, target, instrument).
Introduction
Our approach learns narrative-like knowledge in the form of IE templates; we learn sets of related events and semantic roles , as shown in this sample output from our system:
Introduction
A semantic role , such as target, is a cluster of syntactic functions of the template’s event words (e.g., the objects of detonate and explode).
Learning Templates from Raw Text
The previous section clustered events from the MUC-4 corpus, but its 1300 documents do not provide enough examples of verbs and argument counts to further learn the semantic roles in each
Learning Templates from Raw Text
4.3 Inducing Semantic Roles (Slots)
Learning Templates from Raw Text
Having successfully clustered event words and retrieved an IR-corpas for each cluster, we now address the problem of inducing semantic roles .
Previous Work
(2006) integrate named entities into pattern learning (PERSON won) to approximate unknown semantic roles .
Previous Work
Our approach draws on this idea of using unlabeled documents to discover relations in text, and of defining semantic roles by sets of entities.
Previous Work
Scripts are sets of related event words and semantic roles learned by linking syntactic functions with coreferring arguments.
semantic role is mentioned in 19 sentences in this paper.
Topics mentioned in this paper:
Gormley, Matthew R. and Mitchell, Margaret and Van Durme, Benjamin and Dredze, Mark
Abstract
We explore the extent to which high-resource manual annotations such as treebanks are necessary for the task of semantic role labeling (SRL).
Approaches
A typical pipeline consists of a POS tagger, dependency parser, and semantic role labeler.
Introduction
The goal of semantic role labeling (SRL) is to identify predicates and arguments and label their semantic contribution in a sentence.
Introduction
The problem of SRL for low-resource languages is an important one to solve, as solutions pave the way for a wide range of applications: Accurate identification of the semantic roles of entities is a critical step for any application sensitive to semantics, from information retrieval to machine translation to question answering.
Introduction
We examine approaches in a joint setting where we marginalize over latent syntax to find the optimal semantic role assignment; and a pipeline setting where we first induce an unsupervised grammar.
Related Work
Our work builds upon research in both semantic role labeling and unsupervised grammar induction (Klein and Manning, 2004; Spitkovsky et a1., 2010a).
Related Work
Previous related approaches to semantic role labeling include joint classification of semantic arguments (Toutanova et a1., 2005; J 0-hansson and Nugues, 2008), latent syntax induction (Boxwell et a1., 2011; Naradowsky et a1., 2012), and feature engineering for SRL (Zhao et a1., 2009; Bjorkelund et a1., 2009).
Related Work
(2013) extend this idea by coupling predictions of a dependency parser with predictions from a semantic role labeler.
semantic role is mentioned in 19 sentences in this paper.
Topics mentioned in this paper:
Hermann, Karl Moritz and Das, Dipanjan and Weston, Jason and Ganchev, Kuzman
Abstract
Additionally, we report strong results on PropBank-style semantic role labeling in comparison to prior work.
Argument Identification
From a frame lexicon, we look up the set of semantic roles Ry that associate with y.
Argument Identification
7By overtness, we mean the non-null instantiation of a semantic role in a frame-semantic parse.
Frame Identification with Embeddings
5 The frame lexicon stores the frames, corresponding semantic roles and the lexical units associated with the frame.
Introduction
According to the theory of frame semantics (Fillmore, 1982), a semantic frame represents an event or scenario, and possesses frame elements (or semantic roles ) that participate in the
Introduction
Most work on frame-semantic parsing has usually divided the task into two major subtasks: frame identification, namely the disambiguation of a given predicate to a frame, and argument identification (or semantic role labeling), the analysis of words and phrases in the sentential context that satisfy the frame’s semantic roles (Das et al., 2010; Das et al., 2014).1 Here, we focus on the first subtask of frame identification for given predicates; we use our novel method (§3) in conjunction with a standard argument identification model (§4) to perform full frame-semantic parsing.
Introduction
Second, we present results on PropBank-style semantic role labeling (Palmer et al., 2005; Meyers et al., 2004; Marquez et al., 2008), that approach strong baselines, and are on par with prior state of the art (Punyakanok et al., 2008).
Overview
2004; Carreras and Marquez, 2005) on PropBank semantic role labeling (SRL), it has been treated as an important NLP problem.
Overview
PropBank The PropBank project (Palmer et al., 2005) is another popular resource related to semantic role labeling.
Overview
Like FrameNet, it also has a lexical database that stores type information about verbs, in the form of sense frames and the possible semantic roles each frame could take.
semantic role is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Li, Junhui and Marton, Yuval and Resnik, Philip and Daumé III, Hal
Introduction
Rather than introducing reordering models on either the word level or the translation phrase level, we propose a unified approach to modeling reordering on the linguistic unit level, e. g., syntactic constituents and semantic roles .
Introduction
The reordering unit falls into multiple granularities, from single words to more complex constituents and semantic roles , and often crosses translation phrases.
Introduction
To show the effectiveness of our reordering models, we integrate both syntactic constituent reordering models and semantic role reordering models into a state-of-the-art HPB system (Chiang, 2007; Dyer et al., 2010).
Unified Linguistic Reordering Models
As mentioned earlier, the linguistic reordering unit is the syntactic constituent for syntactic reordering, and the semantic role for semantic reordering.
Unified Linguistic Reordering Models
Note that we refer all core arguments, adjuncts, and predicates as semantic roles ; thus we say the PAS in Figure 1 has 4 roles.
Unified Linguistic Reordering Models
Treating the two forms of reorderings in a unified way, the semantic reordering model is obtainable by regarding a PAS as a CFG rule and considering a semantic role as a constituent.
semantic role is mentioned in 23 sentences in this paper.
Topics mentioned in this paper:
Lo, Chi-kiu and Beloucif, Meriem and Saers, Markus and Wu, Dekai
Introduction
XMEANT is obtained by (1) using simple lexical translation probabilities, instead of the monolingual context vector model used in MEANT for computing the semantic role fillers similarities, and (2) incorporating bracketing ITG constrains for word alignment within the semantic role fillers.
Related Work
MEANT (Lo et al., 2012), which is the weighted f-score over the matched semantic role labels of the automatically aligned semantic frames and role fillers, that outperforms BLEU, NIST, METEOR, WER, CDER and TER in correlation with human adequacy judgments.
Related Work
MEANT is easily portable to other languages, requiring only an automatic semantic parser and a large monolingual corpus in the output language for identifying the semantic structures and the lexical similarity between the semantic role fillers of the reference and translation.
Related Work
There is a total of 12 weights for the set of semantic role labels in MEANT as defined in Lo and Wu (2011b).
XMEANT: a cross-lingual MEANT
The weights can also be estimated in unsupervised fashion using the relative frequency of each semantic role label in the foreign input, as in UMEANT.
XMEANT: a cross-lingual MEANT
To aggregate individual lexical translation probabilities into phrasal similarities between cross-lingual semantic role fillers, we compared two natural approaches to generalizing MEANT’s method of comparing semantic parses, as described below.
XMEANT: a cross-lingual MEANT
3.1 Applying MEANT’s f-score within semantic role fillers
semantic role is mentioned in 17 sentences in this paper.
Topics mentioned in this paper:
Merlo, Paola and van der Plas, Lonneke
Abstract
Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning.
Abstract
Capturing the nature and the number of semantic roles in a sentence is therefore fundamental to correctly describing the interface between grammar and meaning.
Abstract
In this paper, we compare two annotation schemes, PropBank and VerbNet, in a task-independent, general way, analysing how well they fare in capturing the linguistic generalisations that are known to hold for semantic role labels, and consequently how well they grammaticalise aspects of meaning.
Introduction
Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning.
Introduction
Capturing the nature and the number of semantic roles in a sentence is therefore fundamental to correctly describe the interface between grammar and meaning, and it is of paramount importance for all natural language processing (NLP) applications that attempt to extract meaning representations from analysed text, such as question-answering systems or even machine translation.
Introduction
The role of theories of semantic role lists is to obtain a set of semantic roles that can apply to any argument of any verb, to provide an unambiguous identifier of the grammatical roles of the participants in the event described by the sentence (Dowty, 1991).
semantic role is mentioned in 46 sentences in this paper.
Topics mentioned in this paper:
Matsubayashi, Yuichiroh and Okazaki, Naoaki and Tsujii, Jun'ichi
Abstract
A number of studies have presented machine-learning approaches to semantic role labeling with availability of corpora such as FrameNet and PropBank.
Abstract
These corpora define the semantic roles of predicates for each frame independently.
Abstract
Thus, it is crucial for the machine-learning approach to generalize semantic roles across different frames, and to increase the size of training instances.
Introduction
Semantic Role Labeling (SRL) is a task of analyzing predicate-argument structures in texts.
Introduction
More specifically, SRL identifies predicates and their arguments with appropriate semantic roles .
Introduction
These corpora define a large number of frames and define the semantic roles for each frame independently.
semantic role is mentioned in 41 sentences in this paper.
Topics mentioned in this paper:
Chambers, Nathanael and Jurafsky, Dan
Abstract
We describe an unsupervised system for learning narrative schemas, coherent sequences or sets of events (arrested(POLICE,SUSPECT), convicted( JUDGE, SUSPECT)) whose arguments are filled with participant semantic roles defined over words (JUDGE = {judge, jury, court}, POLICE = {police, agent, authorities}).
Abstract
Unlike most previous work in event structure or semantic role learning, our system does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles.
Abstract
By jointly addressing both tasks, we improve on previous results in narrative/frame learning and induce rich frame-specific semantic roles .
Background
This paper addresses two areas of work in event semantics, narrative event chains and semantic role labeling.
Background
2.2 Semantic Role Labeling
Background
The task of semantic role learning and labeling is to identify classes of entities that fill predicate slots; semantic roles seem like they’d be a good model for the kind of argument types we’d like to learn for narratives.
Introduction
Instead, modern work on understanding has focused on shallower representations like semantic roles , which express at least one aspect of the semantics of events and have proved amenable to supervised learning from corpora like PropBank (Palmer et al., 2005) and Framenet (Baker et al., 1998).
Introduction
Even unsupervised attempts to learn semantic roles have required a predefined set of roles (Grenager and Manning, 2006) and often a hand-labeled seed corpus (Swier and Stevenson, 2004; He and Gildea, 2006).
Introduction
This paper shows that verbs in distinct narrative chains can be merged into an improved single narrative schema, while the shared arguments across verbs can provide rich information for inducing semantic roles .
semantic role is mentioned in 16 sentences in this paper.
Topics mentioned in this paper:
Boxwell, Stephen and Mehay, Dennis and Brew, Chris
Abstract
We describe a semantic role labeling system that makes primary use of CCG-based features.
Abstract
This analysis also suggests that simultaneous incremental parsing and semantic role labeling may lead to performance gains in both tasks.
Combinatory Categorial Grammar
We will show this to be a valuable tool for semantic role prediction.
Introduction
Semantic Role Labeling (SRL) is the process of assigning semantic roles to strings of words in a sentence according to their relationship to the semantic predicates expressed in the sentence.
Introduction
An effective semantic role labeling system must recognize the differences between different configurations:
Introduction
We use Propbank (Palmer et al., 2005), a corpus of newswire text annotated with verb predicate semantic role information that is widely used in the SRL literature (Marquez et al., 2008).
Potential Advantages to using CCG
An argument mapping is a link between the CCG category and the semantic roles that are likely to go with each of its arguments.
This is easily read off of the CCG PARG relationships.
some of the non-modifier semantic roles that a verb is likely to express.
semantic role is mentioned in 25 sentences in this paper.
Topics mentioned in this paper:
van Schijndel, Marten and Elsner, Micha
Abstract
Specifically, this model, trained on part-of-speech tags, represents the preferred locations of semantic roles relative to a verb as Gaussian mixtures over real numbers.
Assumptions
The model presented here learns a single, non-recursive ordering for the semantic roles in each sentence relative to the verb since several studies have suggested that early child grammars may consist of simple linear grammars that are dictated by semantic roles (Diessel and Tomasello, 2001; J ackendoff and Wittenberg, in press).
Assumptions
how many semantic roles it confers).
Assumptions
Since infants infer the number of semantic roles , this work further assumes they already have expectations about where these roles tend to be realized in sentences, if they appear.
Background
This finding suggests both that learners will ignore canonical structure in favor of using all possible arguments and that children have a bias to assign a unique semantic role to each argument.
Background
BabySRL is a computational model of semantic role acquistion using a similar set of assumptions to the current work.
Background
to acquire semantic role labelling while still exhibiting 1-1 role bias.
Introduction
In particular, the model described in this paper takes chunked child-directed speech as input and learns orderings over semantic roles .
semantic role is mentioned in 17 sentences in this paper.
Topics mentioned in this paper:
Tian, Ran and Miyao, Yusuke and Matsuzaki, Takuya
Generating On-the-fly Knowledge
A path is considered as joining two germs in a DCS tree, where a germ is defined as a specific semantic role of a node.
Generating On-the-fly Knowledge
The abstract denotation of a germ is defined in a top-down manner: for the root node p of a DCS tree ’2', we define its denotation [[p]]7 as the denotation of the entire tree [[7]]; for a non-root node 7' and its parent node a, let the edge (a, 7') be labeled by semantic roles (r, r’), then define
The Idea
The labels on both ends of an edge, such as SUBJ (subject) and OBJ (object), are considered as semantic roles of the cor-
The Idea
where read, student and book denote sets represented by these words respectively, and wr represents the set 21) considered as the domain of the semantic role r (e.g.
The Idea
1The semantic role ARG is specifically defined for denoting nominal predicate.
semantic role is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Huang, Fei and Yates, Alexander
Abstract
Semantic role labeling techniques are typically trained on newswire text, and in tests their performance on fiction is as much as 19% worse than their performance on newswire text.
Abstract
We investigate techniques for building open-domain semantic role labeling systems that approach the ideal of a train-once, use-anywhere system.
Abstract
We leverage recently-developed techniques for learning representations of text using latent-variable language models, and extend these techniques to ones that provide the kinds of features that are useful for semantic role labeling.
Introduction
In recent semantic role labeling (SRL) competitions such as the shared tasks of CoNLL 2005 and CoNLL 2008, supervised SRL systems have been trained on newswire text, and then tested on both an in-domain test set (Wall Street Journal text) and an out-of-domain test set (fiction).
Introduction
We test our open-domain semantic role labeling system using data from the CoNLL 2005 shared task (Carreras and Marquez, 2005).
Introduction
Owing to the established difficulty of the Brown test set and the different domains of the Brown test and WSJ training data, this dataset makes for an excellent testbed for open-domain semantic role labeling.
semantic role is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Xiong, Deyi and Zhang, Min and Li, Haizhou
Argument Reordering Model
On the source side, the features include the verbal predicate, the semantic role of the argument, the head word and the boundary words of the argument.
Argument Reordering Model
its semantic role A7"
Experiments
To train the proposed predicate translation model and argument reordering model, we first parsed all source sentences using the Berkeley Chinese parser (Petrov et al., 2006) and then ran the Chinese semantic role labeler6 (Li et al., 2010) on all source parse trees to annotate semantic roles for all verbal predicates.
Experiments
After we obtained semantic roles on the source side, we extracted features as described in Section 3.2 and 4.2 and used these features to train our two models as described in Section 3.3 and 4.3.
Predicate Translation Model
tic window, we use its semantic role (i.e., ARGO, ARGM-TMP and so on) A3; and head word A?
Predicate Translation Model
In order to train the discriminative predicate translation model, we first parse source sentences and labeled semantic roles for all verbal predicates (see details in Section 6.1) in our word-aligned bilingual training data.
Related Work
Therefore they either postpone the integration of target side PASs until the whole decoding procedure is completed (Wu and Fung, 2009b), or directly project semantic roles from the source side to the target side through word alignments during decoding (Liu and Gildea, 2010).
Related Work
(2011) incorporate source language semantic role labels into a tree-to-string SMT system.
semantic role is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Croce, Danilo and Giannone, Cristina and Annesi, Paolo and Basili, Roberto
A Distributional Model for Argument Classification
A lexicalized model for individual semantic roles is first defined in order to achieve robust semantic classification local to each argument.
A Distributional Model for Argument Classification
As the classification of semantic roles is strictly related to the lexical meaning of argument heads, we adopt a distributional perspective, where the meaning is described by the set of textual contexts in which words appear.
A Distributional Model for Argument Classification
However, one single vector is a too simplistic representation given the rich nature of semantic roles FE’“.
Abstract
Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples.
Empirical Analysis
The aim of the evaluation is to measure the reachable accuracy of the simple model proposed and to compare its impact over in-domain and out-of-domain semantic role labeling tasks.
Introduction
Semantic Role Labeling (SRL) is the task of automatic recognition of individual predicates together with their major roles (e.g.
Introduction
Semantic Role Labeling
Introduction
More recently, the state-of-art frame-based semantic role labeling system discussed in (Johansson and Nugues, 2008b) reports a 19% drop in accuracy for the argument classification task when a different test domain is targeted (i.e.
semantic role is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Laparra, Egoitz and Rigau, German
Abstract
This paper presents a novel deterministic algorithm for implicit Semantic Role Labeling.
Conclusions and Future Work
In this work we have presented a robust deterministic approach for implicit Semantic Role Labeling.
Conclusions and Future Work
We have shown the importance of this phenomenon for recovering the implicit information about semantic roles .
Conclusions and Future Work
As input it only needs the document with explicit semantic role labeling and Super-Sense annotations.
Introduction
Traditionally, Semantic Role Labeling (SRL) systems have focused in searching the fillers of those explicit roles appearing within sentence boundaries (Gildea and Jurafsky, 2000, 2002; Carreras and Marquez, 2005; Surdeanu et al., 2008; Hajic et al., 2009).
Introduction
for Implicit Semantic Role Labelling
Related Work
The first attempt for the automatic annotation of implicit semantic roles was proposed by Palmer et al.
Related Work
SEMAFOR (Chen et al., 2010) is a supervised system that extended an existing semantic role labeler to enlarge the search window to other sentences, replacing the features defined for regular arguments with two new semantic features.
semantic role is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Abend, Omri and Rappoport, Ari
Abstract
The task of distinguishing between the two has strong relations to various basic NLP tasks such as syntactic parsing, semantic role labeling and subcategorization acquisition.
Core-Adjunct in Previous Work
It takes a different approach from PB to semantic roles .
Core-Adjunct in Previous Work
It does not commit that a semantic role is consistently tagged as a core or a non-core across frames.
Core-Adjunct in Previous Work
For example, the semantic role ‘path’ is considered core in the ‘Self Motion’ frame, but as non-core in the ‘Placing’ frame.
Introduction
Adjuncts form an independent semantic unit and their semantic role can often be inferred independently of the predicate (e.g., [after lunch] is usually a temporal modifier).
Introduction
Distinguishing between the two argument types has been discussed extensively in various formulations in the NLP literature, notably in PP attachment, semantic role labeling (SRL) and subcategorization acquisition.
semantic role is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Chan, Yee Seng and Ng, Hwee Tou
Automatic Evaluation Metrics
2.2 Semantic Roles
Automatic Evaluation Metrics
This metric first counts the number of lexical overlaps SR-Or-t for all the different semantic roles I that are found in the system and reference translation sentence.
Automatic Evaluation Metrics
In their work, the different semantic roles r they considered include the various core and adjunct arguments as defined in the PropBank project (Palmer et al., 2005).
Metric Design Considerations
Besides matching a pair of system-reference sentences based on the surface form of words, previous work such as (Gimenez and Marquez, 2007) and (Rajman and Hartley, 2002) had shown that deeper linguistic knowledge such as semantic roles and syntax can be usefully exploited.
Metric Design Considerations
Possible future directions include adding semantic role information, using the distance between item pairs based on the token position within each sentence as additional weighting consideration, etc.
semantic role is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Kundu, Gourab and Srikumar, Vivek and Roth, Dan
Abstract
In our experiments, using the NLP tasks of semantic role labeling and entity-relation extraction, we demonstrate that with the margin-based algorithm, we need to call the inference engine only for a third of the test examples.
Conclusion
We show via experiments that these methods individually give a reduction in the number of calls made to an inference engine for semantic role labeling and entity-relation extraction.
Experiments and Results
We report the performance of inference on two NLP tasks: semantic role labeling and the task of extracting entities and relations from text.
Experiments and Results
Semantic Role Labeling (SRL) Our first task is that of identifying arguments of verbs in a sentence and annotating them with semantic roles (Gildea and Jurafsky, 2002; Palmer et al., 2010) .
Experiments and Results
For the semantic role labeling task, we need to call the solver only for one in six examples while for the entity-relations task, only one in four examples require a solver call.
Introduction
We evaluate the two schemes and their combination on two NLP tasks where the output is encoded as a structure: PropBank semantic role labeling (Punyakanok et al., 2008) and the problem of recognizing entities and relations in text (Roth and Yih, 2007; Kate and Mooney, 2010).
semantic role is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Mazidi, Karen and Nielsen, Rodney D.
Abstract
In the described system, semantic role labels of source sentences are used in a domain-independent manner to generate both questions and answers related to the source sentence.
Approach
SENNA provides the tokenizing, pos tagging, syntactic constituency parsing and semantic role labeling used in the system.
Approach
SENNA produces separate semantic role labels for each predicate in the sentence.
Approach
The most commonly used semantic roles are A0, A1 and A2, as well as the ArgM modifiers.
Linguistic Challenges
For example in: Plant roots and bacterial decay use carbon dioxide in the process of respiration, the word use was classified as NN, leaving no predicate and no semantic role labels in this sentence.
Related Work
(2013), which used semantic role labeling to identify patterns in the source text from which questions can be generated.
semantic role is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Zou, Bowei and Zhou, Guodong and Zhu, Qiaoming
Baselines
Negation focus identification in *SEM’2012 shared tasks is restricted to verbal negations annotated with MNEG in PropBank, with only the constituent belonging to a semantic role selected as negation focus.
Baselines
For comparison, we choose the state-of-the-art system described in Blanco and Moldovan (2011), which employed various kinds of syntactic features and semantic role features, as one of our baselines.
Baselines
> Semantic features: the syntactic label of semantic role A1; whether A1 contains POS tag DT, JJ, PRP, CD, RB, VB, and WP, as defined in Blanco and Moldovan (2011); whether A1 contains token any, anybody, anymore, anyone, anything, anytime, anywhere, certain, enough, full, many, much, other, some, specifics, too, and until, as defined in Blanco and Moldovan (2011); the syntactic label of the first semantic role in the sentence; the semantic label of the last semantic role in the sentence; the thematic role for AO/Al/AZ/A3/A4 of the negated predicate.
Introduction
Current studies (e.g., Blanco and Moldovan, 2011; Rosenberg and Bergler, 2012) sort to various kinds of intra-sentence information, such as lexical features, syntactic features, semantic role features and so on, ignoring less-obvious inter-sentence information.
semantic role is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Litkowski, Ken
Class Analyses
We believe these analyses may provide a comprehensive characterization of particular semantic roles that can be used for various NLP applications.
Class Analyses
Since dictionary publishers have not previously devoted much effort in analyzing preposition behavior, we believe PDEP may serve an important role, particularly for various NLP applications in which semantic role labeling is important.
Class Analyses
We expect that desired improvements will come from usage in various NLP tasks, particularly word-sense disambiguation and semantic role labeling.
Introduction
(2013); Srikumar and Roth (2011)) have shown the value of prepositional phrases in joint modeling with verbs for semantic role labeling.
Introduction
Section 5 describes how we can use PDEP for the analysis of semantic role and semantic relation inventories.
See http://clg.wlv.ac.uk/proiects/DVC
The occurrence of these invalid instances provides an opportunity for improving taggers, parsers, and semantic role labelers.
semantic role is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Vickrey, David and Koller, Daphne
Abstract
We apply our simplification system to semantic role labeling (SRL).
Experiments
We evaluated our system using the setup of the Conll 2005 semantic role labeling task.2 Thus, we trained on Sections 2-21 of PropBank and used Section 24 as development data.
Introduction
In semantic role labeling (SRL), given a sentence containing a target verb, we want to label the semantic arguments, or roles, of that verb.
Introduction
Current semantic role labeling systems rely primarily on syntactic features in order to identify and
Probabilistic Model
This allows us to learn that “give” has a preference for the labeling {ARGO = Subject NP, ARGI = Postverb NP2, ARGZ = Postverb NP1 Our final features are analogous to those used in semantic role labeling, but greatly simplified due to our use of simple sentences: head word of the constituent; category (i.e., constituent label); and position in the simple sentence.
Related Work
Another area of related work in the semantic role labeling literature is that on tree kernels (Moschitti, 2004; Zhang et al., 2007).
semantic role is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Abend, Omri and Rappoport, Ari
Related Work
(2012) for semantic role labeling and Kwiatkowski et al.
Related Work
Semantic role labeling (SRL) schemes bear similarity to the foundational layer, due to their focus on argument structure.
Related Work
PropBank and NomBank are built on top of the PTB annotation, and provide for each verb (PropBank) and noun (NomBank), a delineation of their arguments and their categorization into semantic roles .
semantic role is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Blanco, Eduardo and Moldovan, Dan
Approach to Semantic Representation of Negation
Instead, we use generic semantic roles .
Approach to Semantic Representation of Negation
Given s: The cow didn’t eat grass with a fork, typical semantic roles encode AGENT(the cow, eat), THEME(grass, eat), INSTRUMENT(With a fork, eat) and NEGATION(n’t, eat).
Approach to Semantic Representation of Negation
Like typical semantic roles , option (2) does not reveal the implicit positive meaning carried by statement s. Options (3—5) encode different interpretations:
Learning Algorithm
Because PropBank adds semantic role annotation on top of the Penn TreeB ank, we have available syntactic annotation and semantic role labels for all instances.
Negation in Natural Language
State-of-the-art semantic role labelers (e.g., the ones trained over PropBank) do not completely represent the meaning of negated statements.
semantic role is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Wang, Lu and Raghavan, Hema and Castelli, Vittorio and Florian, Radu and Cardie, Claire
Experimental Setup
Documents are processed by a full NLP pipeline, including token and sentence segmentation, parsing, semantic role labeling, and an information extraction pipeline consisting of mention detection, NP coreference, cross-document resolution, and relation detection (Florian et al., 2004; Luo et al., 2004; Luo and Zitouni, 2005).
The Framework
semantic role overlap
The Framework
We also derive the semantic role overlap and relation instance overlap between the query and each sentence.
The Framework
Cross-document coreference resolution, semantic role labeling and relation extraction are accomplished Via the methods described in Section 5.
semantic role is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Zapirain, Beñat and Agirre, Eneko and Màrquez, Llu'is
Abstract
We show how automatically generated selectional preferences are able to generalize and perform better than lexical features in a large dataset for semantic role classification.
Abstract
Our findings suggest that selectional preferences have potential for improving a full system for Semantic Role Labeling.
Introduction
Semantic Role Labeling (SRL) systems usually approach the problem as a sequence of two subtasks: argument identification and classification.
Related Work
The application of selectional preferences to semantic roles (as opposed to syntactic functions) is more recent.
Related Work
Other papers applying semantic preferences in the context of semantic roles , rely on the evaluation on pseudo tasks or human plausibility judgments.
semantic role is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Honnibal, Matthew and Curran, James R. and Bos, Johan
Combining CCGbank corrections
Semantic role descriptions generally recognise a distinction between core arguments, whose role comes from a set specific to the predicate, and peripheral arguments, who have a role drawn from a small, generic set.
Noun predicate-argument structure
The semantic roles of Rome and Carthage are the same in (7) and (8), but the noun cannot case-mark them directly, so of and the genitive clitic are pressed into service.
Noun predicate-argument structure
The semantic role depends on both the predicate and subcategorisation frame:
Noun predicate-argument structure
Our analysis requires semantic role labels for each argument of the nominal predicates in the Penn Treebank — precisely what NomBank (Meyers et al., 2004) provides.
semantic role is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Gerber, Matthew and Chai, Joyce
Discussion
As shown, we observed significant losses when excluding features that relate the semantic roles of mentions in c’ to the semantic role
Implicit argument identification
Feature 1 models the semantic role relationship between each mention in c’ and the missing argument position iargn.
Implicit argument identification
semantic roles using SemLink.5 For explanation purposes, consider again Example 1, where we are trying to fill the iargo of shipping.
Introduction
Verbal and nominal semantic role labeling (SRL) have been studied independently of each other (Carreras and Marquez, 2005; Gerber et al., 2009) as well as jointly (Surdeanu et al., 2008; Hajic et al., 2009).
Related work
However, as noted by Iida et al., grammatical cases do not stand in a one-to-one relationship with semantic roles in Japanese (the same is true for English).
semantic role is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Zapirain, Beñat and Agirre, Eneko and Màrquez, Llu'is
Abstract
This paper presents an empirical study on the robustness and generalization of two alternative role sets for semantic role labeling: PropBank numbered roles and VerbNet thematic roles.
Experimental Setting 3.1 Datasets
Our basic Semantic Role Labeling system represents the tagging problem as a Maximum Entropy Markov Model (MEMM).
Introduction
Semantic Role Labeling is the problem of analyzing clause predicates in open text by identifying arguments and tagging them with semantic labels indicating the role they play with respect to the verb.
Introduction
While Arg0 and Argl are intended to indicate the general roles of Agent and Theme, other argument numbers do not generalize across verbs and do not correspond to general semantic roles .
semantic role is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Abend, Omri and Reichart, Roi and Rappoport, Ari
Abstract
The task of Semantic Role Labeling (SRL) is often divided into two subtasks: verb argument identification, and argument classification.
Introduction
Semantic Role Labeling (SRL) is a major NLP task, providing a shallow sentence-level semantic analysis.
Related Work
They then use the unlabeled argument structure (without the semantic roles ) as training data for the ARGID stage and the entire data (perhaps with other features) for the classification stage.
Related Work
They then use these as seed for a bootstrapping algorithm, which consequently identifies the verb arguments in the corpus and assigns their semantic roles .
semantic role is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Cheung, Jackie Chi Kit and Penn, Gerald
Introduction
Caseframes are shallow approximations of semantic roles which are well suited to characterizing a domain by its slots.
Theoretical basis of our analysis
A caseframe is a shallow approximation of the semantic role structure of a proposition-bearing unit like a verb, and are
Theoretical basis of our analysis
In particular, they are (gov, role) pairs, where gov is a proposition-bearing element, and role is an approximation of a semantic role with gov as its head (See Figure 1 for examples).
Theoretical basis of our analysis
Caseframes do not consider the dependents of the semantic role approximations.
semantic role is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Yao, Xuchen and Van Durme, Benjamin and Clark, Peter
Background
Besides Predictive Annotation, our work is closest to structured retrieval, which covers techniques of dependency path mapping (Lin and Pantel, 2001; Cui et al., 2005; Kaisser, 2012), graph matching with Semantic Role Labeling (Shen and Lapata, 2007) and answer type checking (Pinchak et al., 2009), etc.
Background
(2007) proposed indexing text with their semantic roles and named entities.
Background
Queries then include constraints of semantic roles and named entities for the predicate and its arguments in the question.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Li, Jianguo and Brew, Chris
Introduction
Many scholars hypothesize that the behavior of a verb, particularly with respect to the expression of arguments and the assignment of semantic roles is to a large extent driven by deep semantic regularities (Dowty, 1991; Green, 1974; Goldberg, 1995; Levin, 1993).
Introduction
When the information about a verb type is not available or sufficient for us to draw firm conclusions about its usage, the information about the class to which the verb type belongs can compensate for it, addressing the pervasive problem of data sparsity in a wide range of NLP tasks, such as automatic extraction of subcategorization frames (Korhonen, 2002), semantic role labeling (Swier and Stevenson, 2004; Gildea and Juraf-sky, 2002), natural language generation for machine translation (Habash et al., 2003), and deriving predominant verb senses from unlabeled data (Lapata and Brew, 2004).
Related Work
0 Animacy of NPs: The animacy of the semantic role corresponding to the head noun in each syntactic slot can also distinguish classes of verbs.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Parton, Kristen and McKeown, Kathleen R. and Coyne, Bob and Diab, Mona T. and Grishman, Ralph and Hakkani-Tür, Dilek and Harper, Mary and Ji, Heng and Ma, Wei Yun and Meyers, Adam and Stolbach, Sara and Sun, Ang and Tur, Gokhan and Xu, Wei and Yaman, Sibel
Introduction
To solve this problem, a number of different problems in NLP must be addressed: predicate identification, argument extraction, attachment disambiguation, location and time expression recognition, and (partial) semantic role labeling.
Prior Work
The 5W task is also closely related to Semantic Role Labeling (SRL), which aims to efficiently and effectively derive semantic information from text.
The Chinese-English 5W Task
In this task, the 5W’s refer to semantic roles Within a sentence, as defined in Table 1.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Chambers, Nathanael and Jurafsky, Daniel
Discussion
Our reported results include every (vd,n) in the data, not a subset of particular semantic roles .
Discussion
Conditional probability is thus a very strong starting point if selectional preferences are an internal piece to a larger application, such as semantic role labeling or parsing.
Introduction
Selectional preferences are useful for NLP tasks such as parsing and semantic role labeling (Zapirain et al., 2009).
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Kawahara, Daisuke and Peterson, Daniel W. and Palmer, Martha
Conclusion
As applications of the resulting semantic frames and verb classes, we plan to integrate them into syntactic parsing, semantic role labeling and verb sense disambiguation.
Experiments and Evaluations
available on the web site.6 This frame data was induced from the BNC and consists of 1,200 frames and 400 semantic roles .
Related Work
(2012) extended the model of Titov and Klementiev (2012), which is an unsupervised model for inducing semantic roles, to jointly induce semantic roles and frames across verbs using the Chinese Restaurant Process (Aldous, 1985).
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Yang, Bishan and Cardie, Claire
Related Work
Kim and Hovy (2006) identifies opinion holders and targets by using their semantic roles related to opinion words.
Related Work
(2008) argued that semantic role labeling is not sufficient for identifying opinion holders and targets.
Related Work
Joint inference has also been applied to semantic role labeling (Punyakanok et al., 2008; Srikumar and Roth, 2011; Das et al., 2012), where the goal is to jointly identify semantic arguments for given lexical predicates.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Xie, Boyi and Passonneau, Rebecca J. and Wu, Leon and Creamer, Germán G.
Discussion
SemTree features capture the differences between semantic roles for the same frame, and between the same semantic role in different frames.
Methods
FrameNet defines hundreds of frames, each of which represents a scenario associated with semantic roles , or frame elements, that serve as participants in the scenario the frame signifies.
Related Work
Semantic role labeling using FrameNet has been used to identify an opinion with its holder and topic (Kim and Hovy, 2006).
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Weller, Marion and Fraser, Alexander and Schulte im Walde, Sabine
Introduction
The combination of our two models approaches a simplified level of semantic role definition but only relies on dependency information that is considerably easier and cheaper to define and obtain than a very high quality semantic parser and/or a corpus annotated with semantic role information.
Introduction
Integrating semantic role information into SMT has been demonstrated by various researchers to improve translation quality (cf.
Introduction
Wu and Fung (2009b) who demonstrated that on the one hand 84% of verb syntactic functions in a 50-sentence test corpus projected from Chinese to English, and that on the other hand about 15% of the subjects were not translated into subjects, but their semantic roles were preserved across language.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Maxwell, K. Tamsin and Oberlander, Jon and Croft, W. Bruce
Evaluation framework
This is based on work that uses paths between head nouns and verbs (Shen et al., 2005), semantic roles (Moschitti, 2008), and all dependency paths except those that occur between words in the same base chunk (e.g.
Selection method for catenae
We use a pseudo-projective joint dependency parse and semantic role labelling system (J ohansson and
Selection method for catenae
Dependency path features: Part-of-speech tags and semantic roles have been used to filter dependency paths.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Falk, Ingrid and Gardent, Claire and Lamirel, Jean-Charles
Features and Data
In addition we group Verbnet semantic roles as shown in Table 4.
Introduction
From a practical perspective, they support factorisa—tion and have been shown to be effective in various NLP (Natural language Processing) tasks such as semantic role labelling (Swier and Stevenson, 2005) or word sense disambiguation (Dang, 2004).
Lexical Resources Used
Our aim is to accquire a classification which covers the core verbs of French, could be used to support semantic role labelling and is similar in spirit to the English Verbnet.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Feng, Minwei and Peter, Jan-Thorsten and Ney, Hermann
Comparative Study
(Feng et al., 2012) propose two structure features from semantic role labeling (SRL) results.
Comparative Study
During decoding process, the first feature will report how many event layers that one search state violates and the second feature will report the amount of semantic roles that one search state violates.
Introduction
(Feng et al., 2012) present a method that utilizes predicate-argument structures from semantic role labeling results as soft constraints.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Durrett, Greg and Hall, David and Klein, Dan
Example
This observation argues for enforcing agreement of entity-level semantic properties during inference, specifically properties relating to permitted semantic roles .
Experiments
We consider clusterings that take as input pairs (n, 7“) of a noun head n and a string 7“ which contains the semantic role of n (or some approximation thereof) conjoined with its governor.
Models
Throughout this section, let cc be a variable containing the words in a document along with any relevant precomputed annotation (such as parse information, semantic roles , etc.
semantic role is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: