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We present several neural networks to address the task of named entity recognition for morphologically complex languages (MCL). Kazakh is a morphologically complex language in which each root/stem can produce hundreds or thousands of variant word ...

Named entity recognition (NER) system is often realized using supervised methods such as CRF and LSTM-CRF. However, supervised methods often require large training data. In some low-resource languages, annotated data is often hard to obtain. ...

When analyzing the spread of viruses, epidemiologists often need to identify the location of infected hosts. This information can be found in public databases, such as GenBank [3], however, information provided in these databases are usually ...$\mathrm{}$$\mathrm{}$

Named entity recognition is a fundamental task in natural language processing and has been widely studied. The construction of a recognizer requires training data that contains annotated named entities. However, it is expensive to construct such ...

Automatically extracting different types of knowledge from authoritative biomedical texts, e.g., scientific medical literature, electronic health records etc., and representing it in a computer analyzable as well as human-readable form is an ...

We propose PYHSCRF, a novel tagger for domain-specific named entity recognition that only requires a few seed terms, in addition to unannotated corpora, and thus permits the iterative and incremental design of named entity (NE) classes for new ...

Estimating semantic similarity between texts is of vital importance in many areas of natural language processing like information retrieval, question answering, text reuse, or plagiarism detection.

Prevalent semantic similarity estimates based on ...

Meaning is the foundation stone of intercultural communication. Languages are continuously changing, and words shift their meanings for various reasons. Semantic divergence in related languages is a key concern of historical linguistics. In this ...

The paper focuses on the complex research procedure based on hypothesis-deduction method (with semantic experiment as its integral part), corpus-based experiment, and the analysis of search engine results. The process of verification that ...

When people verbalize what they felt with various sensory functions, they could represent different meanings with the same words or the same meaning with different words; we might mean a different degree of coldness when we say ‘this beer is icy ...

Semantic role theory is a widely used approach for verb representation. Yet, there are multiple indications that semantic role paradigm is necessary but not sufficient to cover all elements of verb structure. We conducted a statistical analysis of ...

The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. We are the first to argue that these two important properties can play a major role in ...

Over the last decade, health communities (known as forums) have evolved into platforms where more and more users share their medical experiences, thereby seeking guidance and interacting with people of the community. The shared content, though ...

The present research aims to demonstrate how powerful Finite State Automata (FSA) can be, into a domain in which the vagueness of the human opinions and the subjectivity of the user generated contents make the automatic “understanding” of texts ...

Aggression and hate speech is a rising concern in social media platforms. It is drawing significant attention in the research community who are investigating different methods to detect such content. Aggression, which can be expressed in many ...

Today, online reviews have a great influence on consumers’ purchasing decisions. As a result, spam attacks, consisting of the malicious inclusion of fake online reviews, can be detrimental to both customers as well as organizations. Several ...

Detecting stance of online posts is a crucial task to understand online content and trends. Existing approaches augment models with complex linguistic features, target-dependent properties, or increase complexity with attention-based modules or ...

Expressing the polarity of sentiment as ‘positive’ and ‘negative’ usually have limited scope compared with the intensity/degree of polarity. These two tasks (i.e. sentiment classification and sentiment intensity prediction) are closely related and ...

Handling long queries can involve either reducing its size by retaining only useful sentences, or decomposing the long query into several short queries based on their content. A proper sentence classifi- cation improves the utility of these ...

In this paper, we present evaluation of three in-house sentiment analysis (SA) systems originally designed for three distinct SA tasks, in a highly multilingual setting. For the evaluation, we collected a large number of available gold standard ...

In this paper, we explore the use of sentiment analysis of influential messages on social media to improve political election forecasting. While social media users are not necessarily representative of the overall electors, bias correction of ...

We present an empirical analysis of basic and depression specific multi-emotion mining in Tweets, using state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion model consisting of the commonly identified ...

Sentiments of words can differ from one corpus to another. Inducing general sentiment lexicons for languages and using them cannot, in general, produce meaningful results for different domains. In this paper, we combine contextual and supervised ...

Automatic recognition of human emotions is a relatively new field and is attracting significant attention in research and development areas because of the major contribution it could make to real applications. Previously, several studies reported ...