Making the most of MOGUL: Reflections on interlanguage in childhood language disorders

2017 ◽  
Vol 55 (4) ◽  
pp. 349-364
Author(s):  
Susan H. Foster-Cohen
Keyword(s):  
Language Processing ◽  
Language Change ◽  
Language Disorder ◽  
Language Model ◽  
Language Disorders ◽  
Relevance Theory ◽  
Psychological Resources ◽  
Wide Range ◽  
On Line ◽  
The Impact

Abstract Interlanguage is a concept that is manifest in any trajectory of language change in a learner: typical first language, second language or language disorder. To understand those trajectories we need a rich psychological model of what creates them. This paper applies one such model–Sharwood Smith and Truscott’s Modular On-Line Growth and Use of Language model (MOGUL)–to childhood developmental language disorders, and suggests that the model’s components of language processing and their interaction shed significant light on why children with a wide range of different language disorders exhibit the language behaviours that are the characteristic of their diagnoses. Following a brief summary of the MOGUL model, the paper explores the impact on language development of differences in sensori-motor input, in the functioning of the various modules and the interfaces between them, and in the storage and activation of memory. Like Relevance Theory, with which the paper makes a direct connection, MOGUL encourages one to take a view of developmental language disorder as emerging from the same set of psychological resources as typical development (first or second) but as being the result of adjustments to, or compensations for, differences in how the various modules function and connect with each other.

Understanding children with language problems. Shula Chiat. Cambridge: Cambridge University Press, 2000. Pp. 286.

2001 ◽  
Vol 22 (4) ◽  
pp. 647-650
Author(s):  
Stacy Silverman
Keyword(s):  
Language Processing ◽  
Language Disorder ◽  
Processing System ◽  
Language Disorders ◽  
Phonological Development ◽  
Language Abilities ◽  
Senior Lecturer ◽  
Problem Solve ◽  
The Impact

Examining the language abilities of children with language disorders should be a deductive process, using much more than the data that formal measures provide. The assessment should be a systematic, psycholinguistic exploration of aspects of a child's input and output, with a focus on the attempt to pinpoint specific areas of deficit within the language-processing system. Chiat, in this insightful and extremely accessible book, provides basic profiles of children with language disorders, along with case study examples, that both illustrate various forms of language disorder and demonstrate the use of tasks, commonly applied in psycholinguistic research, to problem-solve specific cases. Chiat is a senior lecturer in Linguistics at City University, London, where she is an established researcher who focuses primarily on phonological development and disorders and the impact of impaired phonology on lexical/semantic development.

scholarly journals Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Clifford Nangle ◽  
Stuart McTaggart ◽  
Margaret MacLeod ◽  
Jackie Caldwell ◽  
Marion Bennie
Keyword(s):  
Information System ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Drug Exposure ◽  
Drug Dose ◽  
Free Text ◽  
Wide Range ◽  
The Impact ◽  
Prescribing Information

ABSTRACT ObjectivesThe Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume. The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness. ApproachAn NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage. ResultsThe NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively. ConclusionThe NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure.

SongRecommend: From summarization to recommendation

2012 ◽  
Vol 20 (1) ◽  
pp. 29-67
Author(s):  
SWATI TATA ◽  
BARBARA DI EUGENIO
Keyword(s):  
Language Processing ◽  
Recommendation System ◽  
User Study ◽  
Portable Devices ◽  
Time On Task ◽  
Specific Recommendation ◽  
Music Recommendation ◽  
On Line ◽  
The Impact ◽  
Music Recommendation System

In recent years, the availability of too much information has become a fact of life for anybody connected with the Internet. The same is true for music: because of the penetration of portable devices and the availability of millions of tracks on the web, individual music collections have become unwieldy. Users need tools to help search their own song collections, and to recommend songs they may be interested in. Whereas recommendation systems have been developed for a variety of products, a music recommendation system presents special challenges, including the ability to recommend individual songs, as opposed to entire albums, even if only full album reviews are available on-line. SongRecommend, our music recommendation system, combines information extraction and generation techniques to produce summaries of reviews of individual songs from album reviews. We present a number of evaluations for SongRecommend: intrinsic evaluations of the extraction components, and of the informativeness of the summaries; and a user study of the impact of the song review summaries on users’ decision-making processes. When presented with the summary, users were able to make quicker decisions, and their choices were more varied. Whereas the smaller size of the summary has an impact on time-on-task, users do not appear to choose a specific recommendation only based on number of words. Our work demonstrates that state-of-the-art techniques in Natural Language Processing can be integrated into an effective end-to-end system.

Long Term Experience of the Real Time Fouling Deposits Thickness Measurements for On-Line Sootblowing Optimization

2015 ◽  
Author(s):  
B. Chudnovsky ◽  
N. Menn
Keyword(s):  
Renewable Energy ◽  
Power Plant ◽  
Real Time ◽  
Operation Conditions ◽  
Wide Range ◽  
On Line ◽  
Cycle Efficiency ◽  
The Impact ◽  
Long Term Experience

Over the past years there has been a dramatic increase in the regulatory requirements for low emissions. Renewable energy targets and CO2 emissions markets drive the transition to a cleaner and renewable energy production system. In addition to increasing the overall plant cycle efficiency, there two principal means of the reduction of the CO2 from coal fired power plants: by coal and biomass co-firing and by the capture and long term storage of the CO2 emitted from power plant. Carbon dioxide capture and storage will involve substantial capital investment, accompanied by a significant power plant cycle efficiency penalty, and is not currently available on a fully commercial basis. Co-firing biomass, in comparison with other renewable sources, is the main contributor to technologies meeting the world’s renewable energy target. However, the impact of biomass co-firing on boilers performance and integrity has been modest. Operational problems associated with the deposition and retention of ash materials can and do occur on all the major gas-side components of combustion and boilers. The process occurs over a wide range of flue gas and surface temperatures, and dependent both on the characteristics of the ash and on the design and operation conditions of the furnace and boiler. Development and validation of the predictive models have been hindered significantly by the practical difficulties in the obtaining reliable data from the boilers operated with coal and biomass. Although specialized on–line deposition monitoring and sootblowing control systems are commercially available, but they are based on a very simple estimates of the fouling factors, which results in crude and not reliable approach to optimization of sootblowers operation. In the present paper an alternative approach and a new technique based on electro-optical sensor are demonstrated. The long term experience with the system attached to the furnace wall and capable to move the compact sensor in and out of the furnace, allowing to measure simultaneously deposits thickness and reflectivity, is described in details. Results of our study show that dynamics of both parameters on the operated power unit can be registered simultaneously in real time and then interpreted separately. Experiments have been carried out with different coal types at 575MW unit equipped with CE tangential boiler and 550 Mw equipped with B&W boiler with opposite fired burners. The measurements were performed in different locations of the furnace. It was shown that dynamics of thickness and reflectivity variation just after the wall cleaning activation are quite different. Situations have been registered where changes of reflectivity have a significant impact on heat transfer, comparable and sometimes even greater than that of growing fouling thickness. Technique and device exploited in this study appears to be a very useful tool for sootblowing optimization and, as a result, for improvement of boiler efficiency and reduction of water wall erosion and corrosion in both pulverized coal and co-firing boilers.

scholarly journals Neural Language Models Capture Some, But Not All, Agreement Attraction Effects

2020 ◽  
Author(s):  
Suhas Arehalli ◽  
Tal Linzen
Keyword(s):  
Real Time ◽  
Language Processing ◽  
Language Production ◽  
Prediction Models ◽  
Language Model ◽  
Cognitive Model ◽  
Language Models ◽  
Word Prediction ◽  
Wide Range ◽  
Attraction Effects

The number of the subject in English must match the number of the corresponding verb (dog runs but dogs run). Yet in real-time language production and comprehension, speakers often mistakenly compute agreement between the verb and a grammatically irrelevant non-subject noun phrase instead. This phenomenon, referred to as agreement attraction, is modulated by a wide range of factors; any complete computational model of grammatical planning and comprehension would be expected to derive this rich empirical picture. Recent developments in Natural Language Processing have shown that neural networks trained only on word-prediction over large corpora are capable of capturing subject-verb agreement dependencies to a significant extent, but with occasional errors. The goal of this paper is to evaluate the potential of such neural word prediction models as a foundation for a cognitive model of real-time grammatical processing. We simulate six experiments taken from the agreement attraction literature with LSTMs, one common type of neural language model. The LSTMs captured the critical human behavior in three of them, indicating that (1) some agreement attraction phenomena can be captured by a generic sequence processing model, but (2) capturing the other phenomena may require models with more language-specific mechanisms

scholarly journals Inducing Relational Knowledge from BERT

2020 ◽  
Vol 34 (05) ◽  
pp. 7456-7463 ◽  
Author(s):  
Zied Bouraoui ◽  
Jose Camacho-Collados ◽  
Steven Schockaert
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Language Model ◽  
Language Models ◽  
Word Embeddings ◽  
Relational Knowledge ◽  
Wide Range ◽  
Fine Tune ◽  
Standard Word

One of the most remarkable properties of word embeddings is the fact that they capture certain types of semantic and syntactic relationships. Recently, pre-trained language models such as BERT have achieved groundbreaking results across a wide range of Natural Language Processing tasks. However, it is unclear to what extent such models capture relational knowledge beyond what is already captured by standard word embeddings. To explore this question, we propose a methodology for distilling relational knowledge from a pre-trained language model. Starting from a few seed instances of a given relation, we first use a large text corpus to find sentences that are likely to express this relation. We then use a subset of these extracted sentences as templates. Finally, we fine-tune a language model to predict whether a given word pair is likely to be an instance of some relation, when given an instantiated template for that relation as input.

scholarly journals Enhancing clinical concept extraction with contextual embeddings

2019 ◽  
Vol 26 (11) ◽  
pp. 1297-1304 ◽  
Author(s):  
Yuqi Si ◽  
Jingqi Wang ◽  
Hua Xu ◽  
Kirk Roberts
Keyword(s):  
Language Processing ◽  
Semantic Information ◽  
Medical Information ◽  
State Of The Art ◽  
Language Model ◽  
The State ◽  
Concept Extraction ◽  
The Impact ◽  
Clinical Concept ◽  
Embedding Methods

Abstract Objective Neural network–based representations (“embeddings”) have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText). Materials and Methods Both off-the-shelf, open-domain embeddings and pretrained clinical embeddings from MIMIC-III (Medical Information Mart for Intensive Care III) are evaluated. We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings and compare these on 4 concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015. We also analyze the impact of the pretraining time of a large language model like ELMo or BERT on the extraction performance. Last, we present an intuitive way to understand the semantic information encoded by contextual embeddings. Results Contextual embeddings pretrained on a large clinical corpus achieves new state-of-the-art performances across all concept extraction tasks. The best-performing model outperforms all state-of-the-art methods with respective F1-measures of 90.25, 93.18 (partial), 80.74, and 81.65. Conclusions We demonstrate the potential of contextual embeddings through the state-of-the-art performance these methods achieve on clinical concept extraction. Additionally, we demonstrate that contextual embeddings encode valuable semantic information not accounted for in traditional word representations.

Applications of Lexicographic Semirings to Problems in Speech and Language Processing

2014 ◽  
Vol 40 (4) ◽  
pp. 733-761
Author(s):  
Richard Sproat ◽  
Mahsa Yarmohammadi ◽  
Izhak Shafran ◽  
Brian Roark
Keyword(s):  
Language Processing ◽  
Language Model ◽  
Speech And Language ◽  
Word Sequence ◽  
Part Of Speech ◽  
Finite State ◽  
On Line ◽  
Word Lattice ◽  
Weighted Finite State Transducers ◽  
Speech And Language Processing

This paper explores lexicographic semirings and their application to problems in speech and language processing. Specifically, we present two instantiations of binary lexicographic semirings, one involving a pair of tropical weights, and the other a tropical weight paired with a novel string semiring we term the categorial semiring. The first of these is used to yield an exact encoding of backoff models with epsilon transitions. This lexicographic language model semiring allows for off-line optimization of exact models represented as large weighted finite-state transducers in contrast to implicit (on-line) failure transition representations. We present empirical results demonstrating that, even in simple intersection scenarios amenable to the use of failure transitions, the use of the more powerful lexicographic semiring is competitive in terms of time of intersection. The second of these lexicographic semirings is applied to the problem of extracting, from a lattice of word sequences tagged for part of speech, only the single best-scoring part of speech tagging for each word sequence. We do this by incorporating the tags as a categorial weight in the second component of a 〈Tropical, Categorial〉 lexicographic semiring, determinizing the resulting word lattice acceptor in that semiring, and then mapping the tags back as output labels of the word lattice transducer. We compare our approach to a competing method due to Povey et al. (2012).

scholarly journals Does rhythmic priming improve grammatical processing in Hungarian-speaking children with and without Developmental Language Disorder?

2020 ◽  
Author(s):  
Enikő Ladányi ◽  
Ágnes Lukács ◽  
Judit Gervain
Keyword(s):  
Language Processing ◽  
Language Disorder ◽  
Experimental Studies ◽  
Language Disorders ◽  
Judgment Task ◽  
List Type ◽  
Developmental Language Disorder ◽  
Musical Rhythm ◽  
Negative Effect ◽  
Regular Rhythm

AbstractResearch has described several features shared between musical rhythm and speech or language, and experimental studies consistently show associations between performance on tasks in the two domains as well as impaired rhythm processing in children with language disorders. Motivated by these results, in the current study our first aim was to explore whether the activation of the shared system underlying rhythm and language processing with a regular musical rhythm can improve subsequent grammatical processing in preschool-aged Hungarianspeaking children with and without Developmental Language Disorder (DLD). Second, we investigated whether rhythmic priming is specific to grammar processing by assessing priming in two additional domains: a linguistic but non-grammatical task (picture naming) and a non-linguistic task (nonverbal Stroop task). Third, to confirm that the rhythmic priming effect originates from the facilitating effect of the regular rhythm and not the negative effect of the control condition, we added a third condition, silence, for all the three tasks. Both groups of children showed better performance on the grammaticality judgment task in the regular compared to both the irregular and the silent conditions but no such effect appeared in the non-grammatical and non-linguistic tasks. These results suggest that 1) rhythmic priming can improve grammatical processing in Hungarian, a language with complex morphosyntax, both in children with and without DLD, 2) the effect is specific to grammar and 3) is a result of the facilitating effect of the regular rhythm.Research Highlights6-year-old Hungarian-speaking children with and without Developmental Language Disorder perform better on a grammatical task subsequent to exposure to a regular rhythm vs. an irregular rhythm/silenceThe effect of regular rhythm is specific: it improves performance on a grammatical task but not on a word retrieval or a non-linguistic taskDifference between performance following regular vs. irregular rhythm originates from the facilitating effect of the regular rhythm (not the negative effect of the irregular rhythm)The results highlight the importance of rhythm in speech processing, and point towards a possible intervention tool in language disorders

scholarly journals The Impact of the COVID-19 Pandemic on Villages: Results of a National Survey

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 959-960
Author(s):  
Natalie Galucia ◽  
Nancy Morrow-Howell ◽  
Peter Sun ◽  
Tanner Meyer ◽  
Ying Li
Keyword(s):  
Well Being ◽  
Social Connections ◽  
Non Profit ◽  
National Network ◽  
New Family ◽  
Wide Range ◽  
On Line ◽  
The Impact ◽  
The Village ◽  
Health And Well Being

Abstract This study, launched in June 2020, documents the impact of the COVID-19 pandemic on Villages nationally. Villages are non-profit, membership-based organizations that provide support from volunteers and social connections to enable aging in place. We distributed on-line surveys to the leaders of the 287 Villages in the national network to capture the effects of the pandemic on organizational operations, membership recruitment, service provision, and member well-being. A 40% response rate (n=116) was obtained. A majority of Villages reported that the pandemic greatly affected the organization, with the top concerns being: 1) membership recruitment, 2) the health and well-being of members and volunteers and 3) connecting with their members outside of normal in-person events. Over half of the respondents reported that the mental health of members had declined; and there were high levels of disruption to usual health care. New member recruitment efforts were thwarted and most Villages lost revenue. About 70% offered virtual programming but, in general, participation in these on-line events dropped. From the survey respondents’ perspective, the value of the Village to members and their family increased (48%) or remained the same (22%). New opportunities emerged that may be continued post-pandemic: new meal and medicine delivery volunteer services, more on-line communication and telephone reassurance, and new family and community connections. Findings indicate a wide range of experiences during the pandemic, with variation stemming from age of the Village and size of membership. The study informs the sustainability and growth efforts of Villages during and after the pandemic.

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