Verb phrase ellipsis

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Verb Phrase Ellipsis Resolution Using Discriminative and Margin Infused Algorithms

Verb Phrase Ellipsis Resolution Using Discriminative and Margin Infused Algorithms

Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informal conversational settings, but despite its evident impact on event coreference reso- lution and extraction, there has been relatively little work on computational methods for iden- tifying and resolving VPE. Here, we present a novel approach to detecting and resolving VPE by using supervised discriminative ma- chine learning techniques trained on features extracted from an automatically parsed, pub- licly available dataset. Our approach yields state-of-the-art results for VPE detection by improving F1 score by over 11%; additionally, we explore an approach to antecedent identifi- cation that uses the Margin-Infused-Relaxed- Algorithm, which shows promising results.
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Exploring the steps of Verb Phrase Ellipsis

Exploring the steps of Verb Phrase Ellipsis

Verb Phrase Ellipsis is a well-studied topic in theoretical linguistics but has received little at- tention as a computational problem. Here we propose a decomposition of the overall resolu- tion problem into three tasks—target detection, antecedent head resolution, and antecedent boundary detection—and implement a num- ber of computational approaches for each one. We also explore the relationships among these tasks by attempting joint learning over different combinations. Our new decomposition of the problem yields significantly improved perfor- mance on publicly available datasets, including a newly contributed one.
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A Neural Network Approach to Verb Phrase Ellipsis Resolution

A Neural Network Approach to Verb Phrase Ellipsis Resolution

Verb Phrase Ellipsis (VPE) is a linguistic phenomenon, where some verb phrases as syntactic constituents are omitted and typically referred by an auxiliary verb. It is ubiquitous in both formal and informal text, such as news articles and dialogues. Previous work on VPE resolution mainly focused on manu- ally constructing features extracted from auxiliary verbs, syn- tactic trees, etc. However, the optimization of feature repre- sentation, the effectiveness of continuous features and the au- tomatic composition of features are not well addressed. In this paper, we explore the advantages of neural models on VPE resolution in both pipeline and end-to-end processes, compar- ing the differences between statistical and neural models. Two neural models, namely multi-layer perception and the Trans- former, are employed for the subtasks of VPE detection and resolution. Experimental results show that the neural models outperform the state-of-the-art baselines in both subtasks and the end-to-end results.
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Verb Phrase Ellipsis detection using Automatically Parsed Text

Verb Phrase Ellipsis detection using Automatically Parsed Text

This paper describes a Verb Phrase Ellipsis (VPE) detection system, built for robust- ness, accuracy and domain independence. The system is corpus-based, and uses a va- riety of machine learning techniques on free text that has been automatically parsed us- ing two different parsers. Tested on a mixed corpus comprising a range of genres, the sys- tem achieves a 72% F1-score. It is designed as the first stage of a complete VPE resolu- tion system that is input free text, detects VPEs, and proceeds to find the antecedents and resolve them.

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Chart Generation

Chart Generation

The string ran fast constitutes a verb phrase by virtue of rule 5 giving the semantics 6, and the phrase ran quickly with the same semantics is put on the agenda when the quickly edge is[r]

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Development of an Open Source Natural Language Generation Tool for Finnish

Development of an Open Source Natural Language Generation Tool for Finnish

The resulting dictionary is then used to determine the transitivity of a verb and the most frequent case for its object(s). This dictionary consists also of a plethora of non- verbs such as Ljubuški and Dodonpa as a result of erroneous parsing in the Parsebank data. This, however, causes no problems in the system because the dictionary also contains a extensive number of real, lemmatized verbs. Given that Syntax Maker operates on the level of surface generation, it is not actively involved in choosing the words in the NLG task. This means that, unless Syntax Maker is specifically instructed to use a non-verb it happens to know as a verb, it won’t. This noise in the verb noun case dictionary, however, has no real effect on the grammaticality of the generated sentences.
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The integration of task and dialogue modelling in the early stages of user interface design : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, N

The integration of task and dialogue modelling in the early stages of user interface design : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Place downwards ellipsis all succeeding lower menemes are replaced by ellipsis Place upwards ellipsis all preceding higher menemes are replaced by ellipsis Hide the currently selected ob[r]

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English French Verb Phrase Alignment in Europarl for Tense Translation Modeling

English French Verb Phrase Alignment in Europarl for Tense Translation Modeling

Features for subjonctif. We first constructed a short list of French verbs that are likely to require in the next verb phrase the presence of the subjonctif mode if followed by a complement clause (clause starting by the FR conjunction que, roughly meaning to or that in English). Such verbs often express non-existing or hypothetical states or events, for instance, souhaiter, esp´erer or supposer (EN: to wish, to hope or to suppose). Besides these verbs, there are other expressions that can trigger the subjonctif in French in their subordinated clauses, such as the following ones: so . . . that, or delighted, clear, vision, way, good, expect, except, pleased, forward followed by to or that. As our features can only come from the English source text, we built a list of English verbs and expressions that could likely require subjunctive mode in French.
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Yet Another Paper about Partial Verb Phrase Fronting in German

Yet Another Paper about Partial Verb Phrase Fronting in German

Yet Another Paper about Partial Verb Phrase Fronting in German Y e t A n o t h e r P a p e r a b o u t P a r t i a l V e r b P h r a s e F r o n t i n g in G e r m a n S t e f a n M f i l l e r * H u[.]

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Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings

Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings

always performed better than linear ones in both a verb disambiguation and a sentence similarity task. The highest performance on the verb dis- ambiguation task was given by a grammar-driven, tensor-based model in a count-based vector space, whereas for the similarity task, the highest per- formance was achieved by the InferSent sentence encoder, followed by a non-linear additive model on a Word2Vec space. Although the neural word embeddings and sentence encoders were largely outperformed on the disambiguation dataset that places more emphasis on syntactic structure than on semantic similarity, they generally performed better in the sentence similarity case, where the distinction between syntactic and semantic simi- larity is more diffuse.
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On the Syntax of Dynamic Verbs in Mandarin Chinese

On the Syntax of Dynamic Verbs in Mandarin Chinese

compute telicity. For example, the telicity of languages such as English depends on the quantity or specificity of the direct object, as shown in (1). According to Slabakova [8], dynamic verbs such as accomplishment and activity verbs contain the feature [ α telic]. For the feature to have a value, the quantity/specificity of the direct object plays an important role. The telicity of a verb is computed under a spec-head relation between the verb and the direct object. The verb first moves to the head of InAspP and then the object to the specifier of this functional phrase. The verb is given the value of [+telic] when the direct object has the feature [+SQA]; in contrast, the verb is assigned with a minus value when the object has the feature [-SQA]. After the verb is assigned a value, it further moves to little vP. Note that according to such studies as Tenny [5,6], it is the affected argument that delimits an event. In other words, those objects that delimit an event must be affected and undergo some sort of change of state over the course of the event.
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Language Based Environment for Natural Language Parsing

Language Based Environment for Natural Language Parsing

In the Figure 2 states labelled "BUILD PHRASE ON RIGHT" and "FIND REGENT ON RIGHT" push the verb to the left stack and pop the right stack for the current constituent.. When the verb is [r]

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Ranking Relevant Verb Phrases Extracted from Historical Text

Ranking Relevant Verb Phrases Extracted from Historical Text

Work project, where historians are storing infor- mation in a database on what men and women did for a living in the Early Modern Swedish soci- ety (i.e. approximately 1550–1800). During this work they have found that working activities in their source material are most often expressed in the form of verb phrases, such as to fish herring or to sell clothes (Ågren et al., 2011). Our approach to information extraction from historical text, and ranking of the extracted results, is however likely to be applicable to other information needs as well. Furthermore, the methods presented in this paper are not dependent on semantically annotated data, since the only information required is a goldstan- dard containing positive and negative phrases.
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Handling verb phrase morphology in highly inflected Indian languages for Machine Translation

Handling verb phrase morphology in highly inflected Indian languages for Machine Translation

Given a language pair (e,f), we extract all verb phrases that occur in the source monolingual data using a verb phrase chunker. Part of speech (POS) tags can be used to extract verb phrases for lan- guages having a good POS tagger. In our ex- periments for English-Hindi language pair, POS tags were used for English verb phrase chunking. Modals in English were included as a part of the verb phrase since their counterparts in Hindi ap- pear as verbs. For Hindi, the verb phrase chun- ker was trained on a small set of 6000 sentences, where the reference markings were obtained by projecting the verb phrases from English. The 6000 sentences were hand aligned with the cor- responding English sentences, hence helping with the accuracy of the projected verb phrases. On this data, we built a CRF based chunker (Lafferty et al., 2001) using word and POS tag features. 3.2 Verb Classing
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A non projective dependency parser

A non projective dependency parser

The rule states: the first noun phrase head label to the right is a subject ©SUBJ, link subj exists and is followed up to the finite verb ©+F in a verb chain v-ch, which is then followed[r]

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Controlling the Voice of a Sentence in Japanese to English Neural Machine Translation

Controlling the Voice of a Sentence in Japanese to English Neural Machine Translation

In Example 4, it was impossible to make a passive sentence because the root verb in the target sen- tence should be an intransitive verb. Most of the active sentences in ALL_PASSIVE should stay active sentences that used intransitive verbs. Like Example 3, there were many sentences that were successfully controlled by using the “be found to be ...” structure when an intransitive verb was included as a root verb. Example 5 showed the case wherein the voice could not be controlled despite the attempt to control it to the active voice. The frequency of the voice of the verb “detect” in the training data consisted of 468 active-voice sentences and 2,858 passive sentences. When we forced the voice of the generated sentence to become active, the result of generation tended to fail sometimes if we input the verb that had few examples of active sentences in the training data. The subject should be generated if we forced the voice of the generated sentence to become active. However, the encoder-decoder model did not know what to generate as a subject if the training data had only a few examples of an active sentence for that verb. On the other hand, when we forced the voice of the generated sentence to become passive, we failed to find any tendencies of this type of the failure. We would like to do some additional investigation on the tendency of this result.
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Parsing with an Extended Domain of Locality

Parsing with an Extended Domain of Locality

However, unification-based extensions of phrase-structures grammars are used because they are able to encode local and non-local syntactic dependencies for example, subject- verb agreeme[r]

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Syntactic Analysis of Arabic Adverb’s between Arabic and English: X Bar Theory

Syntactic Analysis of Arabic Adverb’s between Arabic and English: X Bar Theory

somewhere which has been explained by the syntactic cartography. More newly, literature has arose that offers contradictory findings about Arabic which is elastic but at the same time conversant language, Arabic has a scanty of irregular verbs which are not utilized such as "is" or "are" in the tense present, subtle modifications in the elementary connotation of a verb are emerged by adding to the root.

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Parsing Conjunctions Deterministically

Parsing Conjunctions Deterministically

There are two types of structures in the stack, one type representing clause nuclei the verb group, noun phrase arguments, and adverbs of a clause, and the other representing preposition[r]

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Exploring Nominalization in Physics and Applied Linguistics Textbooks with Different Levels of Difficulty: Implications for English for Specific

Exploring Nominalization in Physics and Applied Linguistics Textbooks with Different Levels of Difficulty: Implications for English for Specific

The distribution of pattern 10 (with the core obligatory elements of nominal group followed by verb phrase, and the optional premodifier) in the two datasets can elucidate specificitie[r]

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