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The present study examined the effects of using automatic speech recognition (ASR) technology on oral complexity in a flipped English as a Foreign Language (EFL) course. A total of 160 undergraduates were enrolled in a 14-week quasi-experiment. The experimental group (EG) and the control group (CG) were taught with a flipped approach, but the EG students needed to undertake an additional pre-class task with ASR technology. In each unit, all students’ in-class task performance was recorded, based on which the metrics of oral complexity were coded and computed. A two-way between- and within-subjects repeated measures design was conducted to examine the effects of the group factor, the time factor and the group × time interaction effects. The results showed that the EG students performed statistically better than their counterparts in the CG on lexical complexity and syntactic complexity. Moreover, significant improvement in phrasal complexity was witnessed over time in both groups. Significant group × time interaction effects were witnessed on overall complexity or subordination complexity. The gradients of the EG trajectories of the two metrics were greater than those of the CG. However, on phrasal complexity, the interaction effect was not significant.

This paper aims to give an account of the multiple determination (determiner spreading) phenomenon in Arabic. Determiner spreading is the syntactic representation and phonological realization of multiple determiners within the same determiner phrase. As a cross-linguistic phenomenon, determiner spreading has been investigated in other languages (e.g., Scandinavian and Greek); different accounts have been proposed. For Scandinavian languages, determiner spreading has been analyzed as a representation of different semantic interpretations. As far as Greek is concerned, some analyses have been proposed; however, two prominent ones have received considerable attention in the literature: (i) a residue of a reduced relative clause and (ii) an instantiation of close appositions. Contrary to those analyses, this paper claims that none of the two analyses is suitable for Arabic; thus, a language-specific analysis is required. To analyze determiner spreading in Arabic, the current paper posits the following research question: What is the linguistic purpose of the multiple determiners found in Arabic determiner phrases? Answering the research question, the paper claims that, in addition to its indispensable role in establishing agreement between nouns and adjectives within the Arabic determiner phrase, determiner spreading demarcates syntactic and semantic phrase boundaries. The paper takes Minimalist Program and Distributed Morphology as a theoretical framework to argue that attributive adjectives are projection of an agreement phrase headed by the definite article ʔal or by the indefinite phonological marker `nunation: -n’. This proposal requires no syntactic movements in the syntax proper. The ultimate linear order is achieved in the phonological components.

Sentiment analysis of short texts is difficult for their simplicity and compactness. This goes a step further when it comes to the Chinese texts. Although deep learning achieved better accuracy in sentiment analysis, there is a lack of explain-ability. Thus, this paper evaluates the effectiveness of techniques for sentiment analysis of Chinese short financial texts with deep learning. For this, we built a Chinese short financial texts corpus (CSFC) and designed an ablation experiment. Beside the CFSC, we used a Chinese review collection and an English short-text repository in the experiment for comparison. There are five techniques involved. They are the Pinyin, the segmentation, the lexical analysis, the phrase structure and the attention mechanism. As results, we found that the phrase structure and the attention mechanism are two of the best. Therefore, the best model in the experiment is called a Phrase Structure and Attention-based Deep network model (PhraSAD). Moreover, to improve the classification accuracy on neutral data, we use a dual classifier strategy for 3-class problems. Experimental results showed that PhraSAD outperformed all other compared models on all experimental datasets.

Evidence from the contemporary translation services market and many centuries of translation practice demonstrate that translation into a non-native language (L2 translation) can be performed effec...

Finding the structure of a sentence—the way its words hold together to convey meaning—is a fundamental step in language comprehension. Several brain regions, including the left inferior frontal gyrus, the left posterior superior temporal gyrus, and the left anterior temporal pole, are supposed to support this operation. The exact role of these areas is nonetheless still debated. In this paper we investigate the hypothesis that different brain regions could be sensitive to different kinds of syntactic computations. We compare the fit of phrase-structure and dependency structure descriptors to activity in brain areas using fMRI. Our results show a division between areas with regard to the type of structure computed, with the left anterior temporal pole and left inferior frontal gyrus favouring dependency structures and left posterior superior temporal gyrus favouring phrase structures.

Natural language understanding (NLU) is a core technique for implementing natural user interfaces. In this study, we propose a neural network architecture to learn syntax vector representation by employing the correspondence between texts and syntactic structures. For representing the syntactic structures of sentences, we used three methods: dependency trees, phrase structure trees, and part of speech tagging. A pretrained transformer is used to propose text-to-vector and syntax-to-vector projection approaches. The texts and syntactic structures are projected onto a common vector space, and the distances between the two vectors are minimized according to the correspondence property to learn the syntax representation. We conducted massive experiments to verify the effectiveness of the proposed methodology using Korean corpora, i.e., Weather, Navi, and Rest, and English corpora, i.e., the ATIS, SNIPS, Simulated Dialogue-Movie, Simulated Dialogue-Restaurant, and NLU-Evaluation datasets. Through the experiments, we concluded that our model is quite effective in capturing a syntactic representation and the learned syntax vector representations are useful.

The goal of this research is to design and implement a chatbot for querying Wordnet semantic relations. The study creates a contextual chatbot named WordnetBot, a web application that utilizes the use of technologies such as Dialogflow, React, NodeJS, Javascript, and MariaDB. The Wordnet database which leverages all other dictionaries due to its semantic relations representation was used as the data source. Phrase Structure Analysis extracts the keyword and the semantic relation from a user’s message or query. It complements the Machine Learning and AI capabilities of Dialogflow in the analysis. The researcher designed an architectural framework for the integration of the different components of WordnetBot.

The study examines phases in developing specification of grammatical marking of emergent clause-combining (CC) as indicative of children’s growing ability to integrate two or more independent predi...

A number of natural language processing tools for Urdu language processing have been developed in the past few years for word segmentation, part of speech tagging, chunking, named entity recognition and parsing. Corpora, especially treebanks, are essential data resources for language processing. This work presents the development and evaluation of an Urdu treebank, the CLE-UTB and a statistical parser. The treebank has been annotated with phrase structure annotation. Part of speech tagging has been performed semi-automatically by using an existing tagger and incorrect tags were corrected manually by annotators. The syntactic annotation has been performed in the Penn Treebank style to mark phrases. The annotation scheme also adds functional labels for grammatical roles. Currently, the treebank contains 7854 annotated sentences and 148,575 tokens. Completeness and correctness of the syntactic labels have been checked automatically after manual annotation. To ensure the annotation consistency of the resource, a grammar-based evaluation and an automatic consistency checking tool have been used to detect linguistically implausible constituents. The inter-annotator agreement is greater than 90%. We have developed a bidirectional long-short term memory (BiLSTM) based parser and a POS tagger which have been trained on the final version of the treebank. We have improved our results by training the word embeddings on a large Urdu text corpus. Our parser produced an f-score of 88.1% and the POS tagger performed with an accuracy of 96.3%.

Previous research suggests that toddlers can rely on distributional cues in the input to track adjacent and nonadjacent grammatical dependencies. It remains unclear whether toddlers understand the ...

Narrow reading has the potential to reduce vocabulary load and to provide rich opportunities for developing collocation knowledge, but these benefits rely on narrow reading increasing lexical repetition within a text. Hence, interest in narrow reading has been limited by the relatively small lexical effect of narrowing reading by topic (Nation, 2013). Nevertheless, research in data-driven learning and teaching and language corpora has reported positively on learners using concordances in a manner comparable to narrow reading. However, the potential for concordances to provide an increased lexical-repetition effect has not been assessed. This study bridges this gap by exploring the degree of lexical repetition available in concordances and identifies corpus composition as a key predictor of lexical repetition. The study uses standardised type-token ratio (sTTR) to analyse concordances extracted from corpora at three different levels of homogeneity/heterogeneity. The results show large, reliable variations in lexical repetition resulting from variation in corpus homogeneity/heterogeneity, and so identifies concordance-based narrow reading as a possible means of overcoming the limitations of traditional narrow reading by topic. The results are discussed with reference to pedagogical implications for language learners, teachers, and researchers.

Sempre foi discutido na literatura o status categorial das Small Clauses (SCs (STOWELL, 1981; MORO 2000 e outros), PrP (BOWERS, 1993), PredP (BAILYN, 2001) etc.) e, já que uma teoria de estrutura de sintagmas exocêntrica pede que Objetos Sintáticos tenham rótulos para que Interpretação Plena os interprete em CI, então é importante perguntarmos qual o rótulo das SCs. O principal objetivo deste artigo é argumentar que o sistema apresentado nos dois artigos recentes, Problems of Projection, PoP (CHOMSKY, 2013; 2015), pode explicar morfologia de caso e concordância dentro das SCs interlinguisticamente, discutindo dados do português e do russo. Proponho que, no Sistema de PoP, SCs podem ser rotuladas como ϕ, quando o sujeito e o predicado compartilham estes traços. De outra forma, quando a morfologia do conjunto sujeito-predicado não combina, o sujeito deve ser alçado, a fim de que o traço de caso do predicado seja checado.

The regularities underlying the structure building of chord sequences, harmonic phrases, and combinations of phrases constitute a central research problem in music theory. This article proposes a formalization of Jazz harmony with a generative framework based on formal grammars, in which syntactic structure tightly corresponds with the functional interpretation of the sequence. It assumes that chords establish nested hierarchical dependencies that are characterized by two core types: preparation and prolongation. The approach expresses diatonic harmony, embedded modulation, borrowing, and substitution within a single grammatical framework. It is argued in the second part that the proposed framework models not only core phrase structure, but also relations between phrases and the syntactic structures underlying the main forms of Jazz standards. As a special case, the Blues form relies heavily on the plagal derivation from the tonic and is analyzed in comparison with other analytical approaches to the Blues. The proposed theory is specified to a sufficient level of detail that it lends itself to computational implementation and empirical exploration, and this way it makes a step towards music theory building that embraces the close links between formal, mathematical, and computational methods.

After explicit phrase structure rules were abandoned in government–binding theory, some account of the distribution of adverbials became necessary. This review surveys two current theories. The first, often called the scopal theory, posits that the main factor is semantics: In general, adverbials can appear wherever they cause no violation of semantic well-formedness. Purely syntactic and morphological factors play a role, but it is a relatively minor one. Though the scopal theory predicts a significant range of adverbial distribution correctly, much of its underlying semantic analysis remains to be developed in explicit terms. The second theory discussed in this review, the cartographic theory, takes syntax as central, proposing that adverbials are individually licensed by dedicated functional heads, arranged in a rigid hierarchy by Universal Grammar. This approach has some empirical successes but also a number of problems; thus, the scopal theory is more likely to represent the right direction.

A 20,000-sentence Burmese (Myanmar) treebank on news articles has been released under a CC BY-NC-SA license. Complete phrase structure annotation was developed for each sentence from the morphologically annotated data prepared in previous work of Ding et al. [1]. As the final result of the Burmese component in the Asian Language Treebank Project , this is the first large-scale, open-access treebank for the Burmese language. The annotation details and features of this treebank are presented.

Past approaches to translate a phrase in a language L1 to a language L2 using a dictionary-based approach require grammar rules to restructure initial translations. This paper introduces a novel method without using any grammar rules to translate a given phrase in L1, which does not exist in the dictionary, to L2. We require at least one L1-L2 bilingual dictionary and n-gram data in L2. The average manual evaluation score of our translations is 4.29/5.00, which implies very high quality., Comment: 5 pages

Bringing together fifty years' worth of cross-linguistic research, this pioneering monograph explores the complex interaction between tense, mood and aspect. It looks at the long way of combining elementary semantic units at the bottom of phrase structure up to and including the top of a sentence. Rejecting ternary tense as blocking compositionality, it introduces three levels obtained by binary tense oppositions. It also counters an outdated view on motion by assuming that change is not expressed as having an inherent goal but rather as dynamic interaction between different number systems that allows us to package information into countable and continuous units. It formally identifies the central role of a verb in a variety of argument structures and integrates adverbial modifiers into the compositional structure at different tense levels of phrase structure. This unique contribution to the field will be essential reading for advanced students and researchers in the syntax-semantics interface.

Complex nominal groups are common in technical English (i.e., English for Specific Purposes, ESP) since they allow lexical items to be tightly packed into a clause which consequently leads to increased lexical density and syntactic ambiguity. In this paper, we analyse (complex) nominal groups in technical English. We propose that, in addition to context and extralinguistic knowledge (i.e. shared technical background that the ESP teacher does not necessarily possess), the structure of the nominal group — or, more precisely, the position of modifiers within the group also plays a role in resolving of syntactic ambiguity and disambiguation of meaning. Thus, modifiers standing farthest from the head have the least specifying potential and are followed by other modifiers that restrict the meaning of the entire nominal group. In this way, the participle reciprocating in steam reciprocating engine (vs.* reciprocating steam engine ) is more specific in meaning and is thus positioned closer to the head of the nominal group. Our results indicate the type of modification (i.e. linear or non-linear) lends support to the disambiguation of complex nominal groups. The paper‘s main contribution is in the field of ESP teacher education in the way that it helps ESP teachers who are not specialists in the field of (marine) engineering to process understand and successfully teach complex nominal groups.

Expression of motion shows systematic inter-typological variability between language types, particularly with respect to manner and path components of motion: speakers of satellite-framed languages (S-language; e.g. German) frequently conflate manner and path into a single clause, while verb-framed language speakers (V-language; e.g. Spanish) typically express manner and path in separate clauses, a pattern that also becomes evident in bilinguals’ expression of motion events in each language type. However, less is known about intra-typological variability within each language type, particularly for the expression of motion events among bilingual speakers. In this study, we examine motion descriptions produced by two groups of bilinguals – with Polish as first language – learning a second language that belongs to the same (Polish–German) or a different language type (Polish–Spanish), in comparison to monolinguals in each language (German, Spanish, Polish). Our results, based on written descriptions of animated motion scenes, showed evidence for both inter-typological and intra-typological variation in the expression of motion, with greater attunement to first-language (L1) patterns in learning a language of the same type, and closer alignment to second-language (L2) patterns in learning a language that belongs to a different language type.

15-month-olds behave as if they comprehend filler-gap dependencies such as wh-questions and relative clauses. On one hypothesis, this success does not reflect adult-like representations but rather ...

It is a common finding across languages that young children have problems in understanding patient-initial sentences. We used Tagalog, a verb-initial language with a reliable voice-marking system and highly frequent patient voice constructions, to test the predictions of several accounts that have been proposed to explain this difficulty: the frequency account, the Competition Model, and the incremental processing account. Study 1 presents an analysis of Tagalog child-directed speech, which showed that the dominant argument order is agent-before-patient and that morphosyntactic markers are highly valid cues to thematic role assignment. In Study 2, we used a combined self-paced listening and picture verification task to test how Tagalog-speaking adults and 5- and 7-year-old children process reversible transitive sentences. Results showed that adults performed well in all conditions, while children's accuracy and listening times for the first noun phrase indicated more difficulty in interpreting patient-initial sentences in the agent voice compared to the patient voice. The patient voice advantage is partly explained by both the frequency account and incremental processing account.

I argue that the ability to compute phrase structure grammars is indicative of a particular kind of thought. This type of thought that is only available to cognitive systems that have access to the computations that allow the generation and interpretation of the structural descriptions of phrase structure grammars. The study of phrase structure grammars, and formal language theory in general, is thus indispensable to studies of human cognition, for it makes explicit both the unique type of human thought and the underlying mechanisms in virtue of which this thought is made possible.

The possibility of formulating conditions that made reference to multiple derivations was a staple of early work in Generative Semantics. In this paper we explore the possibility of having constraints arise from the interaction between derivations in an extended Generative Semantics-type (GS) grammar. In GS syntactic operations apply to semantic material in phrase structure trees; in our extension, these operations can apply within and across derivations. To this end, we will appeal to Tree Adjoining Grammars (TAG; Joshi, 1985 and much subsequent work). The model we propose thus assumes two basic operations, which are the singulary and the generalized versions of a single compositional generative operation. The singulary, tree-internal version is Predicate Raising, which ‘adjoins a predicate to the next higher predicate’ (McCawley, 1973a [1968]: 157); the generalized version is Adjoin, which ‘composes an auxiliary tree β with a tree γ’ (Joshi, 1985: 209), thus applying trans-derivationally. The empirical motivation for this expansion of generative semantics comes from the analysis of an apparently anomalous case of secondary predication in Spanish and English, which –we argue- shares properties with depictive and resultative constructions but in fact is neither.

One of the greatest impacts of corpus linguistics on language teaching has been in the recognition of the importance of collocation. A very influential guide for language teachers with regard to teaching collocation has been the Lexical Approach. Activities pointing students to rich collocational information in monolingual dictionaries, in texts and specifically in collocation dictionaries provided ways for language learners to engage with collocation information: to notice, to remember and to acquire. In recent years, there has been a growing interest in Data Driven Learning and new tools are now available to allow students to access collocation information from corpora for themselves. After introducing some pedagogic considerations, this article presents some of the features of The Prime Machine which were developed to support DDL activities focussed on collocation.

Phrase structure is the arrangement of words in a specific order based on the constraints of a specified language. This arrangement is based on some phrase structure rules which are according to the productions in context free grammar. The identification of the phrase structure can be done by breaking the specified natural language sentence into its constituents that may be lexical and phrasal categories. These phrase structures can be identified using parsing of the sentences which is nothing but syntactic analysis. The proposed system deals with this problem using Deep Learning strategy. Instead of using Rule Based technique, supervised learning with sequence labelling is done using IOB labelling. This is a sequence classification problem which has been trained and modeled using RNN LSTM. The proposed work has shown a considerable result and can be applied in many applications of NLP. Hashi Haris | Misha Ravi "Phrase Structure Identification and Classification of Sentences using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23841.pdf

Treebank is one of the important and useful resources in natural language processing represented in two different annotated schemas: phrase and dependency structures. There are many works that convert a phrase structure into a dependency structure and vice versa. Most of them are based that exploit the handcrafted head percolation table and argument table in predefined deterministic ways. In this article, we propose a method to convert a dependency structure into a phrase structure by enriching a trainable model of former hybrid strategy approach. By adding a classifier to the algorithm and using postprocessing modification, the quality of conversion is increased. We evaluate our method in two different languages, English and Persian, and then analyze the errors. The results of our experiments show a 46.01% reduction of error rate in English and 76.50% for Persian compared to our baseline. We build a new phrase structure treebank by converting 10,000 sentences of Persian dependency treebank into corresponding phrase structures and correcting them manually.

The Discontinuity Model (DM) described in this article proposes that adults can learn part of L2 morphosyntax twice, in two different ways. The same item can be learned as the product of generation...

Corpora are well-known for the affordance to make linguistic regularities salient. Since the coinage of the term ‘data-driven learning’ (DDL) in the 1990s, much has been done to investigate the eff...

This study investigated the optimal conditions under which foreign language learning takes place using a smartphone. The authors proposed that two factors, chunking and imagery, would increase language learning. On the basis of previous findings, they formulated three hypotheses: (a) the use of images will have a positive effect on learning English sentences; (b) the use of chunks will have a positive effect on learning English sentences; and (c) the combined use of images and chunks will have a greater positive effect on learning English sentences than either feature alone. A total of 92 Korean seventh graders participated in this study. To examine the learning effect of chunking (i.e., sentence segmentation unit) and imagery (i.e., visual aid) in an experimental setting, they produced a smartphone learning application that incorporated the two methods. The authors measured learning effect with respect to lexical memory retention (i.e., word retrieval ability) and word order composition (i.e., ability to arrange words according to standard English syntax). The results show that the main effects of both chunking and imagery were significant and that the interaction effect between the two on lexical memory retention was also significant. The interaction effect was greater in the delayed effect measurement than in the immediate effect measurement. These findings suggest optimal conditions for designing a smartphone-based, self-learning application.

This paper investigates three constructions in Mandarin, all of which convey a purposive/teleological meaning, including thelaipurposive, thehaopurposive, and the bare purposive. Despite the fact that each type of purposive clause in Mandarin occurs at the right edge of a sentence, it is argued that none of the purposive clause is a genuine right adjunct in the underlying syntactic structure. On the other hand, our analysis shows that thelaipurposive employs complementation of a secondary predicate, thehaopurposive involves conjunction of two clauses, and the bare purposive should be analyzed as left adjunction that is stranded in the right edge after verb movement. The evidence for our analysis is drawn from subject and object gaps, theba-construction in Mandarin, agentivity, and linear ordering of multiple purposive clauses. This work thus demonstrates representative cases where a structure that appears to involve right adjunction may in fact employ no right adjunction at all. The conclusion is thus consistent with the prediction of Linear Correspondence Axiom (LCA).

During comprehension, the meaning extracted from serial language input can be described by hierarchical phrase structure. Whether our brains explicitly encode hierarchical structure during processing is, however, debated. In this study we recorded Magnetoencephalography (MEG) during reading of structurally ambiguous sentences to probe neural activity for representations of underlying phrase structure. 10 human subjects were presented with simple sentences, each containing a prepositional phrase that was ambiguous with respect to its attachment site. Disambiguation was possible based on semantic information. We applied multivariate pattern analyses (MVPA) to the MEG data using linear classifiers as well as representational similarity analysis to probe various effects of phrase structure building on the neural signal. Using MVPA techniques we successfully decoded both syntactic (part-of-speech) as well as semantic information from the brain signal. Importantly, however, we did not find any patterns in the neural signal that differentiate between different hierarchical structures. Nor did we find neural traces of syntactic or semantic reactivation following disambiguating sentence material. These null findings suggest that subjects may not have processed the sentences with respect to their underlying phrase structure. We discuss methodological limits of our analysis as well as cognitive theories of “shallow processing”, i.e. in how far rich semantic information can prevent thorough syntactic analysis during processing.

This paper aims at utilizing paralinguistic information usually hidden in speech signals, such as pitch, short pause and sarcasm, to disambiguate user intention not easily distinguishable from speech recognition and natural language understanding results provided by a state-of-the-art spoken dialog system (SDS). We propose two methods to address the ambiguities in understanding name entities and sentence structures based on relevant speech cues and nuances. We also propose an approach to capturing sarcasm in speech and generating sarcasm-sensitive responses using an end-to-end neural network. An SDS prototype that directly feeds signal information into the understanding and response generation components has also been developed to support the three proposed applications. We have achieved encouraging experimental results in this initial study, demonstrating the potential of this new research direction.

Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to characterize this structure and unknown how exactly it would be recognized in a step-by-step process model. With a view towards sharpening this picture, we model the time course of hemodynamic activity within the brain during an extended episode of naturalistic language comprehension using Combinatory Categorial Grammar (CCG). CCG has well-defined incremental parsing algorithms, surface compositional semantics, and can explain long-range dependencies as well as complicated cases of coordination. We find that CCG-derived predictors improve a regression model of fMRI time course in six language-relevant brain regions, over and above predictors derived from context-free phrase structure. Adding a special Revealing operator to CCG parsing, one designed to handle right-adjunction, improves the fit in three of these regions. This evidence for CCG from neuroimaging bolsters the more general case for mildly context-sensitive grammars in the cognitive science of language.

It is reported that grammatical information is useful for machine translation (MT) task. However, the annotation of grammatical information requires the highly human resources. Furthermore, it is not trivial to adapt grammatical information to MT since grammatical annotation usually adapts tokenization standards which might not be suitable to capture the relation of two languages, and the use of sub-word tokenization, e.g., Byte-Pair-Encoding, to alleviate out-of-vocabulary problem might not be compatible with those annotations. In this work, we propose two methods to explicitly incorporate grammatical information without supervising annotation; first, latent phrase structure is induced in an unsupervised fashion from a multi-head attention mechanism; second, the induced phrase structures in encoder and decoder are synchronized so that they are compatible with each other using constraints during training. We demonstrate that our approach produces better performance and explainability in two tasks, translation and alignment tasks without extra resources. Although we could not obtain the high quality phrase structure in constituency parsing when evaluated monolingually, we find that the induced phrase structures enhance the explainability of translation through the synchronization constraint.

Siromoney et al. introduced a parallel/sequential generative model called Tabled Matrix Grammars (TMGs) by generalising phrase structure matrix grammars generating abstract families of languages (AFLs). James et al. introduced Parallel Contextual Array Insertion Deletion P Systems (PCAIDPSs) to generate two-dimensional array languages using insertion and deletion operations through parallel contextual mappings. In this paper, we compare the generative powers of PCAIDPSs and TMGs. We prove that the family of languages generated by PCAIDPS with two membranes properly includes the family of languages generated by Tabled Context-sensitive Matrix Grammars (TCSMGs).

This paper deliberates on the process of building the first constituency-to-dependency conversion tool of Turkish. The starting point of this work is a previous study in which 10,000 phrase structure trees were manually transformed into Turkish from the original PennTreebank corpus. Within the scope of this project, these Turkish phrase structure trees were automatically converted into UD-style dependency structures, using both a rule-based algorithm and a machine learning algorithm specific to the requirements of the Turkish language. The results of both algorithms were compared and the machine learning approach proved to be more accurate than the rule-based algorithm. The output was revised by a team of linguists. The refined versions were taken as gold standard annotations for the evaluation of the algorithms. In addition to its contribution to the UD Project with a large dataset of 10,000 Turkish dependency trees, this project also fulfills the important gap of a Turkish conversion tool, enabling the quick compilation of dependency corpora which can be used for the training of better dependency parsers.

Information-theoretic complexity metrics, such as Surprisal (Hale, 2001; Levy, 2008) and Entropy Reduction (Hale, 2003), are linking hypotheses that bridge theorized expectations about sentences and observed processing difficulty in comprehension. These expectations can be viewed as syntactic derivations constrained by a grammar. However, this expectation-based view is not limited to syntactic information alone. The present study combines structural and non-structural information in unified models of word-by-word sentence processing difficulty. Using probabilistic minimalist grammars (Stabler, 1997), we extend expectation-based models to include frequency information about noun phrase animacy. Entropy reductions derived from these grammars faithfully reflect the asymmetry between subject and object relatives (Staub, 2010; Staub, Dillon, & Clifton, 2017), as well as the effect of animacy on the measured difficulty profile (Lowder & Gordon, 2012; Traxler, Morris, & Seely, 2002). Visualizing probability distributions on the remaining alternatives at particular parser states allows us to explore new, linguistically plausible interpretations for the observed processing asymmetries, including the way that expectations about the relativized argument influence the processing of particular types of relative clauses (Wagers & Pendleton, 2016).

The balletto unfolds on a uniquely small scale: many balletti can be performed in less than a minute. The genre’s brevity supports a number of perceptual benefits that train listeners to attend to tonal dynamics at multiple scales. The shortest balletti lie at the perceptual limit for entraining hypermeter and within the boundary for remembering tonic. Dynamic attending theory posits that periodic cadences correspond with peaks of attention, facilitating comparison of distant harmonic events. The balletto’s repeat structure fosters a deeper knowledge of tonal and formal procedures, and repetition directs attention to larger groupings. Together, these principles enabled listeners to identify important harmonic events, compare them across broad time spans, and associate them with specific formal units. Furthermore, a comparison of Italian, English, and German balletti reveals important regional differences in tonal and harmonic norms, illustrating how English composers, especially Thomas Morley, maximally leveraged the genre’s profound perceptual benefits.

X' theory was a major milestone in the history of the development of generative grammar.1 It enabled important insights to be made into the phrase structure of human language, but it had a number of weaknesses, and has been essentially replaced in Chomskyan generativism by Bare Phrase Structure (BPS), which assumes fewer theoretical primitives than X0 theory, and also avoids several of the latter’s weaknesses. However, Bare Phrase Structure has not been widely adopted outside the Minimalist Program (MP), rather, X0 theory remains widespread. In this paper, we develop a new, fully formalized approach to phrase structure which incorporates insights and advances from BPS, but does not require the Minimalist-specific assumptions that come with BPS. We formulate our proposal within Lexical-Functional Grammar (LFG), providing an empirically and theoretically superior model for phrase structure compared with standard versions of X0 theory current in LFG.

The present study aims to investigate the role of L1 transfer effects on L2 sentence processing strategies during the interpretation of relative clause (RC) attachment ambiguities. The main body of the study is divided into two sections. The first section describes Experiment 1, which is designed to test the resolution of RC attachment ambiguities by Turkish learners of L2 English both in Turkish and English through the use of an off-line task (i.e., paper-and-pencil comprehension tests) and compare their processing preferences to those of native English speakers. The second section presents Experiment 2, which aims to investigate the real-time processing of the RC attachment ambiguities by the same participant groups employing eye-tracking methodology. The results indicated that L1 Turkish and L1 English RC attachment preferences differed and that Turkish learners of L2 English tended to transfer their Turkish sentence processing pattern to real-time interpretation of the English RC attachment ambiguities.

A well-designed game can offer enormous opportunities for pragmatics learning by providing an immersive environment where learners can practice L2 in a variety of social contexts. To examine the applicability of gaming to L2 pragmatics learning, this study used the platform Unity to develop a scenariobased digital game (Questaurant) to teach Chinese formulaic expressions. In the game, the player took the role of a robot who works in a restaurant in China and runs quests by interacting with built-in characters. The game incorporated four key gaming attributes: context (representation), goals, feedback, and interactivity. This paper reports the usability of these gaming attributes based on interview data collected from 12 learners of Chinese who completed the game. Results showed that the combination of context and interactivity in Questaurant delivered an engaging learning experience, while explicit feedback directly contributed to learning. Participants raised some concerns regarding the motivational appeal of goals and implicit feedback in the game. This paper further discusses implications for developing and utilizing digital games for pragmatics learning.

Neural oscillations track linguistic information during speech comprehension (e.g., Ding et al., 2016; Keitel et al., 2018), and are known to be modulated by acoustic landmarks and speech intelligibility (e.g., Zoefel & VanRullen, 2015). But, it is unclear what information (e.g., timing, rhythm, or content) the brain utilizes to generate linguistic structure and meaning beyond the information that is present in the physical stimulus. We used electroencephalography (EEG) to investigate whether oscillations are modulated by linguistic content over and above the speech stimulus’ rhythmicity and temporal distribution. We manipulated the presence of semantic and syntactic information apart from the timescale of their occurrence, and controlled for the acoustic-prosodic and lexical-semantic information in the signal. EEG was recorded while 29 adult native speakers of all genders listened to naturally-spoken Dutch sentences, jabberwocky controls with a sentence-like prosodic rhythm and morphemes, word lists with lexical content but no phrase structure, and backwards acoustically-matched controls. Mutual information (MI) analysis revealed sensitivity to linguistic content: Phase MI was highest for sentences at the phrasal (0.8-1.1 Hz) and lexical timescale (1.9-2.8 Hz), suggesting that the delta-band is modulated by lexically-driven combinatorial processing beyond prosody, and that linguistic content (i.e., structure and meaning) organizes the phase of neural oscillations beyond the timescale and rhythmicity of the stimulus. This pattern is consistent with neurophysiologically-inspired models of language comprehension (Martin, 2016, 2020; Martin & Doumas, 2017) where oscillations encode endogenously-generated linguistic content over and above exogenous or stimulus-driven timing and rhythm information.Significance StatementBiological systems like the brain encode their environment not only by reacting in a series of stimulus-driven responses, but by combining stimulus-driven information with endogenous, internally-generated, inferential knowledge and meaning. Understanding language from speech is the human benchmark for this. Much research focusses on the purely stimulus-driven response, but here, we focus on the goal of language behavior: conveying structure and meaning. To that end, we use naturalistic stimuli that contrast acoustic-prosodic and lexical-semantic information to show that, during spoken language comprehension, oscillatory modulations reflect computations related to inferring structure and meaning from the acoustic signal. Our experiment provides the first evidence to date that compositional structure and meaning organize the oscillatory response, above and beyond acoustic and lexical controls.

A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text. We address this problem by introducing explicit representations for objects and their relations by extracting scene graphs from the images. Utilizing an embedding of this scene graph enables our model to more explicitly reason over objects and their relations during story generation, compared to the global features from an object classifier used in previous work. We apply metrics that account for the diversity of words and phrases of generated stories as well as for reference to narratively-salient image features and show that our approach outperforms previous systems. Our experiments also indicate that our models obtain competitive results on reference-based metrics.

This study aims to establish whether the processing of different connectives (e.g., and, but) and different coherence relations (addition, contrast) can be modulated by a structural feature of the connected segments—namely, parallelism. While but is mainly used to contrast two expressions, and occurs in many different relations and has been shown to come with a processing cost. We report three self-paced reading experiments in which we manipulate whether the connected segments share a common verb phrase. Such parallel constructions frequently occur in contrastive relations, although they are typically treated as additive in comprehension research. We expect that parallelism will compensate for the cognitive complexity of contrast and for the ambiguity of and by further signaling the coherence relation. Our results indicate that parallelism speeds up processing and provides further evidence for priming in comprehension. However, parallelism interacted with connective ambiguity in an overt disambiguation task (Experiment 3) but not in a more natural reading task (Experiment 2). We argue that the processing of contrast remains shallow unless disambiguation is explicitly required.

Long Short-Term Memory recurrent neural network (LSTM) is widely used and known to capture informative long-term syntactic dependencies. However, how such information are reflected in its internal vectors for natural text has not yet been sufficiently investigated. We analyze them by learning a language model where syntactic structures are implicitly given. We empirically show that the context update vectors, i.e. outputs of internal gates, are approximately quantized to binary or ternary values to help the language model to count the depth of nesting accurately, as Suzgun et al. (2019) recently show for synthetic Dyck languages. For some dimensions in the context vector, we show that their activations are highly correlated with the depth of phrase structures, such as VP and NP. Moreover, with an $L_1$ regularization, we also found that it can accurately predict whether a word is inside a phrase structure or not from a small number of components of the context vector. Even for the case of learning from raw text, context vectors are shown to still correlate well with the phrase structures. Finally, we show that natural clusters of the functional words and the part of speeches that trigger phrases are represented in a small but principal subspace of the context-update vector of LSTM.

Extended versions of the Lambek Calculus currently used in computational linguistics rely on unary modalities to allow for the controlled application of structural rules affecting word order and phrase structure. These controlled structural operations give rise to derivational ambiguities that are missed by the original Lambek Calculus or its pregroup simplification. Proposals for compositional interpretation of extended Lambek Calculus in the compact closed category of FVect and linear maps have been made, but in these proposals the syntax-semantics mapping ignores the control modalities, effectively restricting their role to the syntax. Our aim is to turn the modalities into first-class citizens of the vectorial interpretation. Building on the directional density matrix semantics, we extend the interpretation of the type system with an extra spin density matrix space. The interpretation of proofs then results in ambiguous derivations being tensored with orthogonal spin states. Our method introduces a way of simultaneously representing co-existing interpretations of ambiguous utterances, and provides a uniform framework for the integration of lexical and derivational ambiguity., Comment: In Proceedings QPL 2020, arXiv:2109.01534