Named Entity Recognition and Text Compression

Import 13/01/2017In recent years, social networks have become very popular. It is easy for users to share their data using online social networks. Since data on social networks is idiomatic, irregular, brief, and includes acronyms and spelling errors, dealing with such data is more challenging than that of news or formal texts. With the huge volume of posts each day, effective extraction and processing of these data will bring great benefit to information extraction applications. This thesis proposes a method to normalize Vietnamese informal text in social networks. This method has the ability to identify and normalize informal text based on the structure of Vietnamese words, Vietnamese syllable rules, and a trigram model. After normalization, the data will be processed by a named entity recognition (NER) model to identify and classify the named entities in these data. In our NER model, we use six different types of features to recognize named entities categorized in three predefined classes: Person (PER), Location (LOC), and Organization (ORG). When viewing social network data, we found that the size of these data are very large and increase daily. This raises the challenge of how to decrease this size. Due to the size of the data to be normalized, we use a trigram dictionary that is quite big, therefore we also need to decrease its size. To deal with this challenge, in this thesis, we propose three methods to compress text files, especially in Vietnamese text. The first method is a syllable-based method relying on the structure of Vietnamese morphosyllables, consonants, syllables and vowels. The second method is trigram-based Vietnamese text compression based on a trigram dictionary. The last method is based on an n-gram slide window, in which we use five dictionaries for unigrams, bigrams, trigrams, four-grams and five-grams. This method achieves a promising compression ratio of around 90% and can be used for any size of text file.In recent years, social networks have become very popular. It is easy for users to share their data using online social networks. Since data on social networks is idiomatic, irregular, brief, and includes acronyms and spelling errors, dealing with such data is more challenging than that of news or formal texts. With the huge volume of posts each day, effective extraction and processing of these data will bring great benefit to information extraction applications. This thesis proposes a method to normalize Vietnamese informal text in social networks. This method has the ability to identify and normalize informal text based on the structure of Vietnamese words, Vietnamese syllable rules, and a trigram model. After normalization, the data will be processed by a named entity recognition (NER) model to identify and classify the named entities in these data. In our NER model, we use six different types of features to recognize named entities categorized in three predefined classes: Person (PER), Location (LOC), and Organization (ORG). When viewing social network data, we found that the size of these data are very large and increase daily. This raises the challenge of how to decrease this size. Due to the size of the data to be normalized, we use a trigram dictionary that is quite big, therefore we also need to decrease its size. To deal with this challenge, in this thesis, we propose three methods to compress text files, especially in Vietnamese text. The first method is a syllable-based method relying on the structure of Vietnamese morphosyllables, consonants, syllables and vowels. The second method is trigram-based Vietnamese text compression based on a trigram dictionary. The last method is based on an n-gram slide window, in which we use five dictionaries for unigrams, bigrams, trigrams, four-grams and five-grams. This method achieves a promising compression ratio of around 90% and can be used for any size of text file.460 - Katedra informatikyvyhově

Data-efficient methods for information extraction

Strukturierte Wissensrepräsentationssysteme wie Wissensdatenbanken oder Wissensgraphen bieten Einblicke in Entitäten und Beziehungen zwischen diesen Entitäten in der realen Welt. Solche Wissensrepräsentationssysteme können in verschiedenen Anwendungen der natürlichen Sprachverarbeitung eingesetzt werden, z. B. bei der semantischen Suche, der Beantwortung von Fragen und der Textzusammenfassung. Es ist nicht praktikabel und ineffizient, diese Wissensrepräsentationssysteme manuell zu befüllen. In dieser Arbeit entwickeln wir Methoden, um automatisch benannte Entitäten und Beziehungen zwischen den Entitäten aus Klartext zu extrahieren. Unsere Methoden können daher verwendet werden, um entweder die bestehenden unvollständigen Wissensrepräsentationssysteme zu vervollständigen oder ein neues strukturiertes Wissensrepräsentationssystem von Grund auf zu erstellen. Im Gegensatz zu den gängigen überwachten Methoden zur Informationsextraktion konzentrieren sich unsere Methoden auf das Szenario mit wenigen Daten und erfordern keine große Menge an kommentierten Daten. Im ersten Teil der Arbeit haben wir uns auf das Problem der Erkennung von benannten Entitäten konzentriert. Wir haben an der gemeinsamen Aufgabe von Bacteria Biotope 2019 teilgenommen. Die gemeinsame Aufgabe besteht darin, biomedizinische Entitätserwähnungen zu erkennen und zu normalisieren. Unser linguistically informed Named-Entity-Recognition-System besteht aus einem Deep-Learning-basierten Modell, das sowohl verschachtelte als auch flache Entitäten extrahieren kann; unser Modell verwendet mehrere linguistische Merkmale und zusätzliche Trainingsziele, um effizientes Lernen in datenarmen Szenarien zu ermöglichen. Unser System zur Entitätsnormalisierung verwendet String-Match, Fuzzy-Suche und semantische Suche, um die extrahierten benannten Entitäten mit den biomedizinischen Datenbanken zu verknüpfen. Unser System zur Erkennung von benannten Entitäten und zur Entitätsnormalisierung erreichte die niedrigste Slot-Fehlerrate von 0,715 und belegte den ersten Platz in der gemeinsamen Aufgabe. Wir haben auch an zwei gemeinsamen Aufgaben teilgenommen: Adverse Drug Effect Span Detection (Englisch) und Profession Span Detection (Spanisch); beide Aufgaben sammeln Daten von der Social Media Plattform Twitter. Wir haben ein Named-Entity-Recognition-Modell entwickelt, das die Eingabedarstellung des Modells durch das Stapeln heterogener Einbettungen aus verschiedenen Domänen verbessern kann; unsere empirischen Ergebnisse zeigen komplementäres Lernen aus diesen heterogenen Einbettungen. Unser Beitrag belegte den 3. Platz in den beiden gemeinsamen Aufgaben. Im zweiten Teil der Arbeit untersuchten wir Strategien zur Erweiterung synthetischer Daten, um ressourcenarme Informationsextraktion in spezialisierten Domänen zu ermöglichen. Insbesondere haben wir backtranslation an die Aufgabe der Erkennung von benannten Entitäten auf Token-Ebene und der Extraktion von Beziehungen auf Satzebene angepasst. Wir zeigen, dass die Rückübersetzung sprachlich vielfältige und grammatikalisch kohärente synthetische Sätze erzeugen kann und als wettbewerbsfähige Erweiterungsstrategie für die Aufgaben der Erkennung von benannten Entitäten und der Extraktion von Beziehungen dient. Bei den meisten realen Aufgaben zur Extraktion von Beziehungen stehen keine kommentierten Daten zur Verfügung, jedoch ist häufig ein großer unkommentierter Textkorpus vorhanden. Bootstrapping-Methoden zur Beziehungsextraktion können mit diesem großen Korpus arbeiten, da sie nur eine Handvoll Startinstanzen benötigen. Bootstrapping-Methoden neigen jedoch dazu, im Laufe der Zeit Rauschen zu akkumulieren (bekannt als semantische Drift), und dieses Phänomen hat einen drastischen negativen Einfluss auf die endgültige Genauigkeit der Extraktionen. Wir entwickeln zwei Methoden zur Einschränkung des Bootstrapping-Prozesses, um die semantische Drift bei der Extraktion von Beziehungen zu minimieren. Unsere Methoden nutzen die Graphentheorie und vortrainierte Sprachmodelle, um verrauschte Extraktionsmuster explizit zu identifizieren und zu entfernen. Wir berichten über die experimentellen Ergebnisse auf dem TACRED-Datensatz für vier Relationen. Im letzten Teil der Arbeit demonstrieren wir die Anwendung der Domänenanpassung auf die anspruchsvolle Aufgabe der mehrsprachigen Akronymextraktion. Unsere Experimente zeigen, dass die Domänenanpassung die Akronymextraktion in wissenschaftlichen und juristischen Bereichen in sechs Sprachen verbessern kann, darunter auch Sprachen mit geringen Ressourcen wie Persisch und Vietnamesisch.The structured knowledge representation systems such as knowledge base or knowledge graph can provide insights regarding entities and relationship(s) among these entities in the real-world, such knowledge representation systems can be employed in various natural language processing applications such as semantic search, question answering and text summarization. It is infeasible and inefficient to manually populate these knowledge representation systems. In this work, we develop methods to automatically extract named entities and relationships among the entities from plain text and hence our methods can be used to either complete the existing incomplete knowledge representation systems to create a new structured knowledge representation system from scratch. Unlike mainstream supervised methods for information extraction, our methods focus on the low-data scenario and do not require a large amount of annotated data. In the first part of the thesis, we focused on the problem of named entity recognition. We participated in the shared task of Bacteria Biotope 2019, the shared task consists of recognizing and normalizing the biomedical entity mentions. Our linguistically informed named entity recognition system consists of a deep learning based model which can extract both nested and flat entities; our model employed several linguistic features and auxiliary training objectives to enable efficient learning in data-scarce scenarios. Our entity normalization system employed string match, fuzzy search and semantic search to link the extracted named entities to the biomedical databases. Our named entity recognition and entity normalization system achieved the lowest slot error rate of 0.715 and ranked first in the shared task. We also participated in two shared tasks of Adverse Drug Effect Span detection (English) and Profession Span Detection (Spanish); both of these tasks collect data from the social media platform Twitter. We developed a named entity recognition model which can improve the input representation of the model by stacking heterogeneous embeddings from a diverse domain(s); our empirical results demonstrate complementary learning from these heterogeneous embeddings. Our submission ranked 3rd in both of the shared tasks. In the second part of the thesis, we explored synthetic data augmentation strategies to address low-resource information extraction in specialized domains. Specifically, we adapted backtranslation to the token-level task of named entity recognition and sentence-level task of relation extraction. We demonstrate that backtranslation can generate linguistically diverse and grammatically coherent synthetic sentences and serve as a competitive augmentation strategy for the task of named entity recognition and relation extraction. In most of the real-world relation extraction tasks, the annotated data is not available, however, quite often a large unannotated text corpus is available. Bootstrapping methods for relation extraction can operate on this large corpus as they only require a handful of seed instances. However, bootstrapping methods tend to accumulate noise over time (known as semantic drift) and this phenomenon has a drastic negative impact on the final precision of the extractions. We develop two methods to constrain the bootstrapping process to minimise semantic drift for relation extraction; our methods leverage graph theory and pre-trained language models to explicitly identify and remove noisy extraction patterns. We report the experimental results on the TACRED dataset for four relations. In the last part of the thesis, we demonstrate the application of domain adaptation to the challenging task of multi-lingual acronym extraction. Our experiments demonstrate that domain adaptation can improve acronym extraction within scientific and legal domains in 6 languages including low-resource languages such as Persian and Vietnamese

Understanding Social Media through Large Volume Measurements

The amount of user-generated web content has grown drastically in the past 15 years and many social media services are exceedingly popular nowadays. In this thesis we study social media content creation and consumption through large volume measurements of three prominent social media services, namely Twitter, YouTube, and Wikipedia. Common to the services is that they have millions of users, they are free to use, and the users of the services can both create and consume content. The motivation behind this thesis is to examine how users create and consume social media content, investigate why social media services are as popular as they are, what drives people to contribute on them, and see if it is possible to model the conduct of the users. We study how various aspects of social media content be that for example its creation and consumption or its popularity can be measured, characterized, and linked to real world occurrences. We have gathered more than 20 million tweets, metadata of more than 10 million YouTube videos and a complete six-year page view history of 19 different Wikipedia language editions. We show, for example, daily and hourly patterns for the content creation and consumption, content popularity distributions, characteristics of popular content, and user statistics. We will also compare social media with traditional news services and show the interaction with social media, news, and stock prices. In addition, we combine natural language processing with social media analysis, and discover interesting correlations between news and social media content. Moreover, we discuss the importance of correct measurement methods and show the effects of different sampling methods using YouTube measurements as an example.Sosiaalisen median suosio ja sen käyttäjien luoman sisällön määrä on kasvanut valtavasti viimeisen 15 vuoden aikana ja palvelut kuten Facebook, Instagram, Twitter, YouTube ja Wikipedia ovat erittäin suosittuja. Tässä väitöskirjassa tarkastellaan sosiaalisen median sisällön luonti- ja kulutusmalleja laajavoluumisen mittausdatan kautta. Väitöskirja sisältää mittausdataa Twitter-, YouTube- ja Wikipedia -palveluista. Yhteistä näille kolmelle palvelulle on muuan muassa se, että niillä on miljoonia käyttäjiä, niitä voi käyttää maksutta ja käyttäjät voivat luoda sekä kuluttaa sisältöä. Mittausdata sisältää yli 20 miljoona Twitter -viestiä, metadatatietoja yli kymmenestä miljoonasta YouTube -videosta ja täydellisen artikkelien katselukertojen tiedot kuudelta vuodelta 19 eri Wikipedian kieliversiosta. Tutkimuksen tarkoituksena on tarkastella kuinka käyttäjät luovat ja kuluttavat sisältöä sekä löytää niihin liittyviä malleja, joita voi hyödyntää tiedon jaossa, replikoinnissa ja tallentamisessa. Tutkimuksessa pyritään siis selvittämään miksi miksi sosiaalisen median palvelut ovat niin suosittuja kuin ne nyt ovat, mikä saa käyttäjät tuottamaan sisältöä niihin ja onko palveluiden käyttöä mahdollista mallintaa ja ennakoida. Väitöskirjassa verrataan myös sosiaalisen median ja tavallisten uutispalveluiden luonti- ja kulutusmalleja. Lisäksi näytetään kuinka sosiaalisen median sisältö, uutiset ja pörssikurssi hinnat ovat vuorovaikutuksessa toisiinsa. Väitöskirja sisältää myös pohdintaa oikean mittausmenetelmän valinnasta ja käyttämisestä sekä näytetään eri mittausmenetelmien vaikutuksista tuloksiin YouTube -mittausdatan avulla

Addicting via hashtags: How is Twitter making addiction?

Persons, substances, bodies, consumption: an ever widening process of ‘‘addicting’’ is underway in Western societies. In this article, we turn our attention to the production of addiction on the microblogging social media platform, Twitter, as an important emerging site in which the addicting of contemporary societies is also occurring. Our analysis explores two questions. First, we investigate the ways in which addiction is enacted via Twitter. How is addiction being made on Twitter? Second, we ask how the technology of Twitter itself is shaping meaning: how do the technological ‘‘affordances’’ of Twitter help constitute the kinds of addiction being materialized? While we find a multiplicity of meanings in the 140-character messages, we also find a pattern: a tendency toward extremes—addiction riven between pain and pleasure. In addition, we find significant areas of commonality between approaches and notable silences around alternatives to common understandings of addiction. We argue that the constraints on communication imposed by Twitter technology afford a ‘‘shorthand’’ of addiction that is both revealing and productive. Illuminated is the importance of addiction as a piece of cultural shorthand that draws on and simultaneously reproduces simplistic, reductive addiction objects. In concluding, we consider what these realities of addiction being enacted through Twitter can tell us about contemporary conditions of possibility for drug use in society and for individual subjectivities and experiences

Temporal models for mining, ranking and recommendation in the Web

Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, heterogeneous temporal datasets i.e., the Web, collaborative knowledge bases and social networks have been emerged as gold-mines for content analytics of many sorts. In those collections, time plays an essential role in many crucial information retrieval and data mining tasks, such as from user intent understanding, document ranking to advanced recommendations. There are two semantically closed and important constituents when modeling along the time dimension, i.e., entity and event. Time is crucially served as the context for changes driven by happenings and phenomena (events) that related to people, organizations or places (so-called entities) in our social lives. Thus, determining what users expect, or in other words, resolving the uncertainty confounded by temporal changes is a compelling task to support consistent user satisfaction. In this thesis, we address the aforementioned issues and propose temporal models that capture the temporal dynamics of such entities and events to serve for the end tasks. Specifically, we make the following contributions in this thesis: (1) Query recommendation and document ranking in the Web - we address the issues for suggesting entity-centric queries and ranking effectiveness surrounding the happening time period of an associated event. In particular, we propose a multi-criteria optimization framework that facilitates the combination of multiple temporal models to smooth out the abrupt changes when transitioning between event phases for the former and a probabilistic approach for search result diversification of temporally ambiguous queries for the latter. (2) Entity relatedness in Wikipedia - we study the long-term dynamics of Wikipedia as a global memory place for high-impact events, specifically the reviving memories of past events. Additionally, we propose a neural network-based approach to measure the temporal relatedness of entities and events. The model engages different latent representations of an entity (i.e., from time, link-based graph and content) and use the collective attention from user navigation as the supervision. (3) Graph-based ranking and temporal anchor-text mining inWeb Archives - we tackle the problem of discovering important documents along the time-span ofWeb Archives, leveraging the link graph. Specifically, we combine the problems of relevance, temporal authority, diversity and time in a unified framework. The model accounts for the incomplete link structure and natural time lagging in Web Archives in mining the temporal authority. (4) Methods for enhancing predictive models at early-stage in social media and clinical domain - we investigate several methods to control model instability and enrich contexts of predictive models at the “cold-start” period. We demonstrate their effectiveness for the rumor detection and blood glucose prediction cases respectively. Overall, the findings presented in this thesis demonstrate the importance of tracking these temporal dynamics surround salient events and entities for IR applications. We show that determining such changes in time-based patterns and trends in prevalent temporal collections can better satisfy user expectations, and boost ranking and recommendation effectiveness over time

MEGA: Multilingual Evaluation of Generative AI

Generative AI models have shown impressive performance on many Natural Language Processing tasks such as language understanding, reasoning, and language generation. An important question being asked by the AI community today is about the capabilities and limits of these models, and it is clear that evaluating generative AI is very challenging. Most studies on generative LLMs have been restricted to English and it is unclear how capable these models are at understanding and generating text in other languages. We present the first comprehensive benchmarking of generative LLMs - MEGA, which evaluates models on standard NLP benchmarks, covering 16 NLP datasets across 70 typologically diverse languages. We compare the performance of generative LLMs including Chat-GPT and GPT-4 to State of the Art (SOTA) non-autoregressive models on these tasks to determine how well generative models perform compared to the previous generation of LLMs. We present a thorough analysis of the performance of models across languages and tasks and discuss challenges in improving the performance of generative LLMs on low-resource languages. We create a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field.Comment: EMNLP 202

NusaCrowd: Open Source Initiative for Indonesian NLP Resources

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken