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Instituto Goyri Goyri

Las AMPAS de los coles e institutos seguimos unidas para que el Instituto María Goyri Goyri termine de construirse y los alumnos se trasladen el 8 de Enero 

Os enviamos comunicado :

📣📣📣 *Comisión Redes Butarque sigue en lucha* 📣📣📣

Vecinos y Familias anunciaros que la Huelga de Familias fue un éxito.

La Presencia en la puerta del Juan Ra a las 11 h  fue bastante elevada.

El seguimiento en los Institutos fue una victoria absoluta.

Tras ello no queremos parar, y en previsión de nuevas medidas que tomemos, anunciamos que seguimos activos en la Red X con nuevos Hashtag.

La metodología sigue siendo igual:

A las 8hr o cuando podáis twiteamos incluyendo:

 *#MePido1Goyri* *#CarbónParaLaCAM*

Etiquetamos a las mismas personas








Seguimos exigiendo  Traslado al María Goyri Goyri, dentro de este curso 23/24


Fiesta Navidad AMPA 2023

Fiesta de Navidad

Se acerca la Navidad y queremos celebrarlo con todos los socios del AMPA.

Ven a disfrutar de un chocolate con churros calentito !!!

Todos los alumnos que figurasen como socios del AMPA a fecha 1 de Diciembre 2023 tendrán un detalle.

¿Qué día?  19 de Diciembre

¿A qué hora? De 16:00 a 18:30


** Los regalos serán para los alumnos que figuren como socios del AMPA a FECHA 1 de Diciembre 2023.

Os recordamos: para figurar como socio no vale con pagar la cuota únicamente, tenéis que estar registrados en el formulario web. Si no aparece en el listado no tendrá regalo. Por favor, evitemos situaciones incómodas para todos y sobre todo para los niños.

**Si no podéis venir puede recoger el regalo otra persona en vuestro nombre, simplemente sabiendo el nombre y apellidos de los niños.

Muchísimas gracias


Semantic Analysis v s Syntactic Analysis in NLP

Semantic Features Analysis Definition, Examples, Applications

semantics nlp

Text summarization extracts words, phrases, and sentences to form a text summary that can be more easily consumed. The accuracy of the summary depends on a machine’s ability to understand language data. Automated semantic analysis works with the help of machine learning algorithms. It’s an essential sub-task of Natural Language Processing (NLP) and the driving force behind machine learning tools like chatbots, search engines, and text analysis. The lexical unit, in this context, is a pair of basic forms of a word (lemma) and a Frame. At frame index, a lexical unit will also be paired with its part of speech tag (such as Noun/n or Verb/v).

The tool analyzes every user interaction with the ecommerce site to determine their intentions and thereby offers results inclined to those intentions. A ‘search autocomplete‘ functionality is one such type that predicts what a user intends to search based on previously searched queries. You can foun additiona information about ai customer service and artificial intelligence and NLP. It saves a lot of time for the users as they can simply click on one of the search queries provided by the engine and get the desired result. Please ensure that your learning journey continues smoothly as part of our pg programs. Kindly provide email consent to receive detailed information about our offerings. Connect and share knowledge within a single location that is structured and easy to search.

Semantic Signal Separation. Understand Semantic Structures with… by Márton Kardos Feb, 2024 – Towards Data Science

Semantic Signal Separation. Understand Semantic Structures with… by Márton Kardos Feb, 2024.

Posted: Sun, 11 Feb 2024 08:00:00 GMT [source]

By leveraging these tools, we can extract valuable insights from text data and make data-driven decisions. Overall, sentiment analysis is a valuable technique in the field of natural language processing and has numerous applications in various domains, including marketing, customer service, brand management, and public opinion analysis. An innovator in natural language processing and text mining solutions, our client develops semantic fingerprinting technology as the foundation for NLP text mining and artificial intelligence software. Our client’s company, based in Vienna and San Francisco, addresses the challenges of filtering large amounts of unstructured text data, detecting topics in real-time on social media, searching in multiple languages across millions of documents, natural language processing, and text mining. Our client was named a 2016 IDC Innovator in the machine learning-based text analytics market as well as one of the 100 startups using Artificial Intelligence to transform industries by CB Insights.

Syntactic and Semantic Analysis

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy. Find centralized, trusted content and collaborate around the technologies you use most. Semantic analysis also takes into account signs and symbols (semiotics) and collocations (words that often go together). The phrases in the bracket are the arguments, while “increased”, “rose”, “rise” are the predicates.

Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments. Natural language processing (NLP) and Semantic Web technologies are both Semantic Technologies, but with different and complementary roles in data management. In fact, the merging of NLP and Semantic Web technologies enables people to combine structured and unstructured data in ways that are not viable using traditional tools. Argument identification is not probably what “argument” some of you may think, but rather refer to the predicate-argument structure [5]. In other words, given we found a predicate, which words or phrases connected to it. It is essentially the same as semantic role labeling [6], who did what to whom.

MLOps Tools Compared: MLflow vs. ClearML—Which One Is Right for You?

A semantic analysis algorithm needs to be trained with a larger corpus of data to perform better. That leads us to the need for something better and more sophisticated, i.e., Semantic Analysis. Keeping the advantages of natural language processing in mind, let’s explore how different industries are applying this technology.

And if NLP is unable to resolve an issue, it can connect a customer with the appropriate personnel. Syntactic analysis, also referred to as syntax analysis or parsing, is the process of analyzing natural language with the rules of a formal grammar. Grammatical rules are applied to categories and groups of words, not individual words. Now, we can understand that meaning representation shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relation and predicates to describe a situation.

Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language. Understanding Natural Language might seem a straightforward process to us as humans. However, due to the vast complexity and subjectivity involved in human language, interpreting it is quite a complicated task for machines.

This means we can convey the same meaning in different ways (i.e., speech, gesture, signs, etc.) The encoding by the human brain is a continuous pattern of activation by which the symbols are transmitted via continuous signals of sound and vision. This graph is built out of different knowledge sources like WordNet, Wiktionary, and BabelNET. The node and edge interpretation model is the symbolic influence of certain concepts. The basic idea of a semantic decomposition is taken from the learning skills of adult humans, where words are explained using other words.

Bidirectional encoder representation from transformers architecture (BERT)13. I am currently pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, Deep Learning, and Artificial Intelligence. For Example, you could analyze the keywords in a bunch of tweets that have been categorized as “negative” and detect which words or topics are mentioned most often.

It gives computers and systems the ability to understand, interpret, and derive meanings from sentences, paragraphs, reports, registers, files, or any document of a similar kind. Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding. Your phone basically understands what you have said, but often can’t do anything with it because it doesn’t understand the meaning behind it. Also, some of the technologies out there only make you think they understand the meaning of a text. An approach based on keywords or statistics or even pure machine learning may be using a matching or frequency technique for clues as to what the text is “about.” But, because they don’t understand the deeper relationships within the text, these methods are limited.

semantics nlp

These models would require a more complex setup, including fine-tuning on a large dataset and more sophisticated feature extraction methods. These two areas are very different and in a sense complementory to one another. Semantic Web technologies deal with representation, standardization and reasoning about «facts». Important issues include semantics nlp defining vocabularies and designing so called ontologies. Semantic Web technologies do not deal very much with the question where these «facts» come from (at most, data integration comes to mind). Natural Language Processing on the other hand deals with trying to automatically understand the meaning of natural language texts.

One of the main reasons people use virtual assistants and chatbots is to find answers to their questions. Question-answering systems use semantics to understand what a question is asking so that they can retrieve and relay the correct information. Thus, the ability of a machine to overcome the ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense Disambiguation. Customers benefit from such a support system as they receive timely and accurate responses on the issues raised by them.

Understanding Natural Language Processing

Beginning from what is it used for, some terms definitions, and existing models for frame semantic parsing. This article will not contain complete references to definitions, models, and datasets but rather will only contain subjectively important things. An alternative, unsupervised learning algorithm for constructing word embeddings was introduced in 2014 out of Stanford’s Computer Science department [12] called GloVe, or Global Vectors for Word Representation. While GloVe uses the same idea of compressing and encoding semantic information into a fixed dimensional (text) vector, i.e. word embeddings as we define them here, it uses a very different algorithm and training method than Word2Vec to compute the embeddings themselves. In any ML problem, one of the most critical aspects of model construction is the process of identifying the most important and salient features, or inputs, that are both necessary and sufficient for the model to be effective.

ELMo uses character level encoding and a bi-directional LSTM (long short-term memory) a type of recurrent neural network (RNN) which produces both local and global context aware word embeddings. From an ML/DL perspective, NLP is just one of many of its applications where standard input formats (vectors, matrices, tensors, etc.) are developed such that they can be input into advanced, highly scalable and flexible ML & DL models and frameworks, by means of which these NLP and other applications can be developed at scale. Machines of course understand numbers, or data structures of numbers, from which they can perform calculations for optimization, and in a nutshell this is what all ML and DL models expect in order for their techniques to be effective, i.e. for the machine to effectively learn, no matter what the task. NLP applications are no different from an ML and DL perspective and as such a fundamental aspect of NLP as a discipline is the collection, parsing and transformation of textual (digital) input into data structures that machines can understand, a description of which is the topic of this paper (Figure 1).

Then it starts to generate words in another language that entail the same information. Insurance companies can assess claims with natural language processing since this technology can handle both structured and unstructured data. NLP can also be trained to pick out unusual information, allowing teams to spot fraudulent claims. With sentiment analysis we want to determine the attitude (i.e. the sentiment) of a speaker or writer with respect to a document, interaction or event. Therefore it is a natural language processing problem where text needs to be understood in order to predict the underlying intent.

Semantic Analysis Is Part of a Semantic System

The categories under «characteristics» and «quantity» map directly to the types of attributes needed to describe products in categories like apparel, food and beverages, mechanical parts, and more. Our models can now identify more types of attributes from product descriptions, allowing us to suggest additional structured attributes to include in product catalogs. The «relationships» branch also provides a way to identify connections between products and components or accessories. While semantic analysis is more modern and sophisticated, it is also expensive to implement. You see, the word on its own matters less, and the words surrounding it matter more for the interpretation.

In WSD, the goal is to determine the correct sense of a word within a given context. By disambiguating words and assigning the most appropriate sense, we can enhance the accuracy and clarity of language processing tasks. WSD plays a vital role in various applications, including machine translation, information retrieval, question answering, and sentiment analysis. Semantics gives a deeper understanding of the text in sources such as a blog post, comments in a forum, documents, group chat applications, chatbots, etc. With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text.

  • It may be defined as the words having same spelling or same form but having different and unrelated meaning.
  • Summarization – Often used in conjunction with research applications, summaries of topics are created automatically so that actual people do not have to wade through a large number of long-winded articles (perhaps such as this one!).
  • Please ensure that your learning journey continues smoothly as part of our pg programs.
  • In this article, you will learn how to apply the principles of lexical semantics to NLP and AI, and how they can improve your applications and research.

For example, consider the query, “Find me all documents that mention Barack Obama.” Some documents might contain “Barack Obama,” others “President Obama,” and still others “Senator Obama.” When used correctly, extractors will map all of these terms to a single concept. Natural language processing (NLP) and Semantic Web technologies are both Semantic Technologies, but with different and complementary roles in data management. In fact, the combination of NLP and Semantic Web technologies enables enterprises to combine structured and unstructured data in ways that are simply not practical using traditional tools. Sure, you use semantics subconsciously throughout the day, but with an English degree, you can dive deeper into the world of words to analyze word and sentence meaning, ambiguity, synonymy, antonymy, and more. If the idea of becoming a linguist or computational linguist (someone who works at the intersection of linguistics and computer science) piques your interest, consider earning your BA or MA in English at UTPB. With semantics on our side, we can more easily interpret the meaning of words and sentences to find the most logical meaning—and respond accordingly.

How Does Semantic Analysis Work?

The main difference is semantic role labeling assumes that all predicates are verbs [7], while in semantic frame parsing it has no such assumption. 6While there are methods for reducing this “feature size”, an elemental task in all machine learning problems (e.g., simply limiting the word count to the most used, or frequently used, top N words, or more advanced methods such as Latent Semantic Analysis), such methods are beyond the scope of this paper. Word2Vec is trained on the Google News Dataset on about 100 billion words and supports both word similarity as well as word prediction capabilities and as such has applicability in a variety of NLP applications such as Recommendation Engines, Knowledge Discovery (Search), as well as Text Classification problems. The meaning representation can be used to reason for verifying what is correct in the world as well as to extract the knowledge with the help of semantic representation. Though natural language processing tasks are closely intertwined, they can be subdivided into categories for convenience. It unlocks an essential recipe to many products and applications, the scope of which is unknown but already broad.

  • Powered by machine learning algorithms and natural language processing, semantic analysis systems can understand the context of natural language, detect emotions and sarcasm, and extract valuable information from unstructured data, achieving human-level accuracy.
  • Word Sense Disambiguation

    Word Sense Disambiguation (WSD) involves interpreting the meaning of a word based on the context of its occurrence in a text.

  • Therefore, the goal of semantic analysis is to draw exact meaning or dictionary meaning from the text.
  • Dictionaries provide definitions and examples of lexical items; thesauri provide synonyms and antonyms of lexical items; ontologies provide hierarchical and logical structures of concepts and their relations; and corpora provide real-world texts and speech data.
  • Affixing a numeral to the items in these predicates designates that

    in the semantic representation of an idea, we are talking about a particular

    instance, or interpretation, of an action or object.

” At the moment, the most common approach to this problem is for certain people to read thousands of articles and keep  this information in their heads, or in workbooks like Excel, or, more likely, nowhere at all. Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that involves identifying and classifying named entities in text into predefined categories such as person names, organization names, locations, date expressions, and more. The goal of NER is to extract and label these named entities to better understand the structure and meaning of the text.

Furthermore, once calculated, these (pre-computed) word embeddings can be re-used by other applications, greatly improving the innovation and accuracy, effectiveness, of NLP models across the application landscape. Approaches such as VSMs or LSI/LSA are sometimes as distributional semantics and they cross a variety of fields and disciplines from computer science, to artificial intelligence, certainly to NLP, but also to cognitive science and even psychology. The methods, which are rooted in linguistic theory, use mathematical techniques to identify and compute similarities between linguistic terms based upon their distributional properties, with again TF-IDF as an example metric that can be leveraged for this purpose. Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to help computers understand language as well as we do.

So this is more of a low-level activity that can serve as input for Semantic Web. The output of NLP is usually not modeled in a sophisticated manner, but comes as «X is an entity», «X relates to Y», etc. Furthermore, NLP does not deliver results that are 100% correct as many of its techniques are based on statistics (neither does Semantic Web, obviously, but I am unaware that questions of precision and especially recall play an important role there). When combined with machine learning, semantic analysis allows you to delve into your customer data by enabling machines to extract meaning from unstructured text at scale and in real time.

semantics nlp

The field’s ultimate goal is to ensure that computers understand and process language as well as humans. With its ability to process large amounts of data, NLP can inform manufacturers on how to improve production workflows, when to perform machine maintenance and what issues need to be fixed in products. And if companies need to find the best price for specific materials, natural language processing can review various websites and locate the optimal price. While NLP and other forms of AI aren’t perfect, natural language processing can bring objectivity to data analysis, providing more accurate and consistent results. Another remarkable thing about human language is that it is all about symbols. According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system.

2In Python for example, the most popular ML language today, we have libraries such as spaCy and NLTK which handle the bulk of these types of preprocessing and analytic tasks. For Example, Tagging Twitter mentions by sentiment to get a sense of how customers feel about your product and can identify unhappy customers in real-time. Both polysemy and homonymy words have the same syntax or spelling but the main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. In this component, we combined the individual words to provide meaning in sentences. Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) have not been needed anymore.

semantics nlp

This part of NLP application development can be understood as a projection of the natural language itself into feature space, a process that is both necessary and fundamental to the solving of any and all machine learning problems and is especially significant in NLP (Figure 4). Each of these applications in one way or another needs to understand the semantic spatial relationship between and among all of the different component parts of a given textual corpus in order to be effective, and as such word embeddings, and the semantic space to which they belong, become an integral part of the NLP/ML pipeline. Semantic analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context.

What is semantics in artificial intelligence?

Semantic AI and data platforms consolidate all your sources and types of data into a single logically unified, searchable, and comprehensible knowledge pool. It identifies logical relationships within the data, enabling you to glean valuable business insights and use that information to solve real-world problems.

NLP-powered apps can check for spelling errors, highlight unnecessary or misapplied grammar and even suggest simpler ways to organize sentences. Natural language processing can also translate text into other languages, aiding students in learning a new language. The very first reason is that with the help of meaning representation the linking of linguistic elements to the non-linguistic elements can be done. The purpose of semantic analysis is to draw exact meaning, or you can say dictionary meaning from the text. The combination of NLP and Semantic Web technologies provide the capability of dealing with a mixture of structured and unstructured data that is simply not possible using traditional, relational tools. Due to the lack of structure in news clippings, it is very difficult for a pharmaceutical competitive intelligence officer to get answers to questions such as, “Which companies have published information in the last 6 months referencing compounds that target a specific pathway that we’re targeting this year?

For example, the word «dog» can mean a domestic animal, a contemptible person, or a verb meaning to follow or harass. The meaning of a lexical item depends on its context, its part of speech, and its relation to other lexical items. Lexical semantics plays an important role in semantic analysis, allowing machines to understand relationships between lexical items like words, phrasal verbs, etc.

More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above). Natural language processing has its roots in the 1940s.[1] Already in 1940, Alan Turing published an article titled «Computing Machinery and Intelligence» which proposed what is now called the Turing test as a criterion of intelligence, though at the time that was not articulated as a problem separate from artificial intelligence. The proposed test includes a task that involves the automated interpretation and generation of natural language. Homonymy refers to two or more lexical terms with the same spellings but completely distinct in meaning under elements of semantic analysis.

Lexical analysis is the process of identifying and categorizing lexical items in a text or speech. It is a fundamental step for NLP and AI, as it helps machines recognize and interpret the words and phrases that humans use. Lexical analysis involves tasks such as tokenization, lemmatization, stemming, part-of-speech tagging, named entity recognition, and sentiment analysis. Today, semantic analysis methods are extensively used by language translators.

For example, when your professor says your contributions to today’s discussion were “interesting,” you may wonder whether she was complimenting your input or implying that it needed improvement (hopefully the former). In the ever-expanding era of textual information, it is important for organizations to draw insights from such data to fuel businesses. Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts.

Professor Martha Palmer recognized for lifetime of contributions to computational linguistics – University of Colorado Boulder

Professor Martha Palmer recognized for lifetime of contributions to computational linguistics.

Posted: Fri, 04 Aug 2023 07:00:00 GMT [source]

GL Academy provides only a part of the learning content of our pg programs and CareerBoost is an initiative by GL Academy to help college students find entry level jobs. Description Logic provides the mathematical foundation for knowledge representation systems and can be used to reason with the information. The automated process of identifying in which sense is a word used according to its context.

semantics nlp

In the form of chatbots, natural language processing can take some of the weight off customer service teams, promptly responding to online queries and redirecting customers when needed. NLP can also analyze customer surveys and feedback, allowing teams to gather timely intel on how customers feel about a brand and steps they can take to improve customer sentiment. If you’re interested in using some of these techniques with Python, take a look at the Jupyter Notebook about Python’s natural language toolkit (NLTK) that I created. You can also check out my blog post about building neural networks with Keras where I train a neural network to perform sentiment analysis. It is the first part of the semantic analysis in which the study of the meaning of individual words is performed. Future work uses the created representation of meaning to build heuristics and evaluate them through capability matching and agent planning, chatbots or other applications of natural language understanding.

semantics nlp

So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis. Neural machine translation, based on then-newly-invented sequence-to-sequence transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for statistical machine translation. The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach.

What is pragmatics in NLP?

Pragmatic Analysis(PA):

It deals with overall communicative and social content and its effect on interpretation. It means abstracting the meaningful use of language in situations. In this analysis, the main focus always on what was said is reinterpreted on what is intended.

Our client partnered with us to scale up their development team and bring to life their innovative semantic engine for text mining. Our expertise in REST, Spring, and Java was vital, as our client needed to develop a prototype that was capable of running complex meaning-based filtering, topic detection, and semantic search over huge volumes of unstructured text in real time. Creating a complete code example for Compositional Semantic Analysis in Python, along with a synthetic dataset and plots, involves several steps.

What is semantic and pragmatic example?

For example , I am hungry , semantically means that feeling when someone does not eat for a certain period of time; pragmatically, depending on the context, means can we postpone the meeting? , let's go to a restaurant, or I could not understand your speech …etc.

What is the difference between syntactic and semantic analysis in NLP?

Unlike syntactic analysis, which focuses on the structure, semantic analysis looks at the content and context, aiming to uncover the underlying meaning conveyed by the text. This step is critical for extracting insights, answering questions, and making sense of language in NLP applications. 1.

What is NLP and its syntax and semantics?

NLP is used to understand the structure and meaning of human language by analyzing different aspects like syntax, semantics, pragmatics, and morphology. Then, computer science transforms this linguistic knowledge into rule-based, machine learning algorithms that can solve specific problems and perform desired tasks.




🏫 Por el cumplimiento el *8/1/24* del traslado del alumnado al IES María Goyri Goyri.


Taller de pancartas 🪧 para tod@s l@s que quieran participar –> Martes 28/11/23 en LOCAL AVIB.

Huelga viernes 1 de diciembre: NO ACUDIR A CLASE 🚫
*11:00* Concentración puerta IES JUAN RAMÓN JIMÉNEZ
*12:00* Lectura del manifiesto

– La situación afecta a todo el alumnado de Butarque: primaria y secundaria. Para aquell@s a quienes sea imposible hacer la huelga ese día, proponemos llevar camiseta, lazo o complemento amarillo 💛.


Mañana Asamblea Informativa en AVIB



Mañana 23 de Noviembre 


Se tratará la falta de cumplimiento de la finalización del Instituto Goyri Goyri y la huelga de familias planteada para el 1 de Diciembre.

Es un tema importante para todos!!

Huelga de familias 1 De Diciembre


La obra del IES María Goyri Goyri no estará finalizada para el 30 de noviembre de 2023 como nos prometieron, nuestras hij@s no pueden seguir siendo víctimas del deterioro de la Educación Pública.

El hacinamiento y masificación en el IES Juan Ramón Jiménez, contraviene el derecho digno de la educación pública de nuestr@s hij@s en el barrio, merma su calidad de enseñanza, se les está privando de espacios esenciales y necesarios para el pleno desempeño de todas las enseñanzas.

Hart@s de engaños, frente el abandono,
la desidia e incumplimiento de la Comunidad de Madrid.


¿En qué consiste la huelga de familias?

Que este día nuestr@s hij@s no acudan a los centros educativos y se participe en los actos de la huelga que se convoquen.

¿Qué familias pueden ejercer su derecho a la huelga?

Todas aquellas que tengan escolarizad@s hij@s en el barrio de Butarque, IES María Goyri Goyri, IES Juan Ramón Jiménez, CEIP el Greco, CEIP Ausías March y CEIP los Rosales.

¿Qué repercusión tendrá para nuestr@s hij@s?

Ninguna.  Además esta jornada de huelga ha sido comunicada a los equipos directivos de estos centros educativos, para su participación e implicación, no solo beneficia a nuestr@s hij@s, sino también a toda la Comunidad Educativa, de la que el profesorado forma parte.

¿La falta de asistencia a clase?

Se justifica por los mismos medios que se viene haciendo, raíces o cualquier otra vía que cada centro tenga establecida.

¿Tengo que indicar el motivo de la falta de asistencia?

No, no hay que indicarlo. En cualquiera de los casos si así se hiciera, con indicar motivos personales es suficiente.

¡Basta ya! ¡Por los derechos y educación pública de nuestr@s hij@s!
Por un inicio de clases el 8 de enero 2024 en el IES María Goyri Goyri



Ayuda! Instituto sin terminar

  1. 📣📣📣Atención Vecinos, padres y amigos de Butarque📣📣📣

Estamos llevando a cabo 1 iniciativa fácil y rápida de posicionamiento en la Red X, antes Twitter.
La Campaña se llama *Buenos días*

El Motivo es alarmar y crear expectación por la situación que viven nuestros menores en el Instituto Juan Ramón Jiménez y Maria Goyri Goyri.

Cómo sabréis, el nuevo Instituto María Goyri Goyri sigue sin terminar. Son 1400 menores hacinados en el Juan Ramón Jiménez. Lo cual supone un peligro en cuanto a seguridad, además de perjudicar la calidad de la enseñanza.

Desde Educación mantienen la fecha de entrega de obra el 30 de Noviembre, pero creemos que no se va cumplir, dada la lentitud de las obras.

*#1DFamiliasEnHuelga* este es nuestro Hashtag.
Úsalo todas las mañanas a las 8 de la mañana, o cuando puedas, Felicitando el día laboral de nuestros gobernantes.

Se puede poner también una cuenta atrás, señalando los días que quedan hasta la fecha prometida de entrega.

Queda 57 dias
Buenos días! Mi hijo está hacinado en el Instituto del barrio Butarque, Madrid. Hoy comienza tu jornada laboral. Feliz Jueves

Navega en Redes y verás que a partir de esta idea cada vecino saludamos de distinta manera, pero lo más importante y no te olvides es

También incluye a las personas mayormente responsables

La ideal es hacer ruido en redes, que no se olviden de nosotros, que vean un barrio unido, porque esto es un problema de todos, los del Juanra, los del Goyri, los del Greco, los del Ausias, los del Rosales,…. Es un perjuicio para todo el barrio.
Tenemos que estar unidos y reclamar lo que nos prometieron hace años!!

📣📣📣Máxima difusión.
Información proporcionada por la Comisión de Redes del Barrio


Quedan pocas plazas para cubrir el mínimo en las actividades de:

  • AJEDREZ (PRIMARIA): Martes y Jueves de 16 a 17 h. Viernes de 16 a 17 h

  • COLPBOL (DE 3º A 6º PRIMARIA): Viernes de 16 a 17 h

  • GUITARRA (PRIMARIA) : Viernes de 16 a 17 h

  • CHIQUIRRITMO (INFANTIL): Viernes de 16 a 17 h

Por si queda algún rezagado o indeciso, que se anime para poder sacar un grupo.

Información e Inscripciones en la web del ampa en el apartado de extraescolares.

Os recordamos también que está abierto el plazo para inscribirse en la Formación de 12 sesiones sobre temas tan interesantes como aprender a escucharme y escuchar, comprender a nuestros hijos, autoestima, respeto, poner límites, …. 

Son poquitas plazas!!


Formación completa, con un grupo reducido de personas para sacar el máximo partido a la experiencia, con mucha práctica, para que sea un aprendizaje real, y con el acompañamiento de las dos formadoras expertas en la materia, que nos guiarán en este proceso de transformación.





HORARIO DE 17:15 H  A 19:45 H

Puedes consultar información más detallada de las 12 sesiones en el siguiente archivo PDF


Para inscribirte rellena y envía el siguiente formulario

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Buenos días!

Os recordamos que la semana que viene ya es Octubre.

Horario de Octubre a Mayo

Clase de 9 a 14 h

Comedor de 14 h a 16 h

Continúan abiertas las inscripciones para las actividades extraescolares.

Listado de todas las actividades que tenemos en el cole:

English for Joy        

Inglés Trinity

Art Attack                            






Fútbol sala




Gimnasia Rítmica


Baile Moderno




Acrobacias aéreas


Información detallada de cada una de ellas en el apartado del menú «Actividades extraescolares 2023/24» de la página web del AMPA