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Word embeddings are used to convert human language into a numerical form by encoding the semantic properties of words. Using it each word can be transformed to a set of N-dimensional vectors. It plays a vital role in processing of linguistic applications like natural language inference, information retrieval, sentiment analysis, etc. The goal of word embedding is to capture the meaning of words in their context. And it also find the semantic relationships and similarities between words. The aim of this work is to summarize the existing embedding techniques for words and available corpus for Malayalam language. Since Malayalam is a resource-constrained Indian language, this paper is expected to help NLP researchers in Malayalam to identify the existing resources and to improve the current research trend.

Reation Extraction(RE) is the subprocess of Information Extraction(IE) which focuses on determining and extracting the reation between two participating entities. Most of the past work focus on extracting relations within a sentence. Nowadays, research on relation extraction focuses on identifying and determining relationship between participating entities across sentences. This paper proposes a bi-directional GRU model with self attention mechanism for inter-sentential relation extraction. First, a bi-directional GRU with self attention mechanism is used to capture the information about the relation from intermediary terms between two entities. Then a bi-directional GRU is used to capture the information represented by entities, which plays a vital role in relation extraction. Finally, the proposed model combines both word embeddings and entity embeddings for extracting a relation. Experimental results show that the proposed Bi-directional GRU model can deliver state-of-the-art results on relation classification. Application of self attention mechanism on intermediary terms improves the performance of relation extraction. Experimental results show that F-measure of the proposed inter-sentential relation extraction is 0.75, which is better than state-of-the-art systems of inter-sentential relation extraction with the same dataset.

Most of the past work on relation extraction(RE) has focused on identifying relationships between entities within a sentence. Nowadays, most of the research in the field of RE has got interested in relation extraction between entity pairs across sentence boundaries. This paper proposes a Bi-directional LSTM model for for inter-sentential RE. Experimental results show that the proposed Bi-LSTM model can achieve better results on relation classification by capturing the information hidden in long-distance relation patterns.

The goal of an information retrieval system is to provide the information that is relevant to the user's query. In some cases the information relevant to the user request may not exist in the user's native language. Situations may also arise where the user is able to read documents in languages different from the native one, but might have difficulty in formulating queries in them. The main intention behind Multilingual Information Retrieval is to find the relevant information available irrespective of the language used in the query.

The morphological richness and the agglutinative nature of Malayalam make it necessary to retrieve the root word from its inflected form in most of the NLP tasks. This paper presents an approach to identify the suffixes of Malayalam words using MBLP approach. The idea here is to use Memory Based Language Processing (MBLP) algorithm for Malayalam suffix identification. MBLP is an approach to language processing based on exemplar storage during learning and analogical reasoning during processing. Sandhi splitting is essential for morphological analysis, document indexing and topic modeling. Suffix separation improves the quality of machine translated text. Training instances created from words are manually annotated for their segmentation and the system is trained using TiMBL (Tilberg Memory Based Learner). The paper presents memory-based model of Malayalam suffix identification and its generalization accuracy.

Word Sense Disambiguation (WSD) is the task of identifying the correct sense of a word in a specific context when the word has multiple meaning. WSD is very important as an intermediate step in many Natural Language Processing (NLP) tasks, especially in Information Extraction(IE), Machine Translation(MT) and Question/Answering Systems. Word sense ambiguity arises when a particular word has more than one possible sense. The peculiarity of any language is that it includes a lot of ambiguous words. Since the sense of a word depends on its context of use, disambiguation process requires the understanding of word knowledge. Automatic WSD systems are available for structured languages like English, Chinese, etc. But Indian languages are morphologically rich and thus the processing task is very complex. The aim of this work is to develop a WSD system for Malayalam, a language spoken in India, predominantly used in the state of Kerala. The proposed system uses a corpus which is collected from various Malayalam web documents. For each possible sense of the ambiguous word, a relatively small set of training examples (seed sets) are identified which represents the sense. Collocations and most co-occurring words are considered as training examples. Seed set expansion module extends the seed set by adding most similar words to the seed set elements. These extended sets act as sense clusters. The most similar sense cluster to the input text context is considered as the sense of the target word.

As the global energy consumption is rising dramatically, wind energy is a prominent one among the renewable energy sources. The penetration of wind energy into grid is increasing day by day. In order to carry huge amount of wind power during the grid integration of large scale wind farms, high transmission line capability is demanded. In order to improve the power carrying capability of the transmission line and to improve the stability of the system, series compensation is the best practical solution. Series compensation can result in Sub-Synchronous Resonance (SSR) oscillations in the electrical system which will lead to damages in the system such as shaft failure. In this paper, a novel idea of using the Real Time Recurrent Learning (RTRL) based adaptive neuro controller is proposed for damping SSR oscillations in grid connected windfarms. The controller is trained in real time without a reference model. The effectiveness of the proposed controller is tested under varying series compensation, wind speeds and grid impedance conditions and it has been proved that the proposed controller performs far better than any other linear controllers.

The power system complexity is increasing day by day and the requirement of stable, secure and high quality electrical power is mandatory in present scenario. Flexible AC Transmission System (FACTS) devices such as Thyristor Controlled Series Capacitor (TCSC) are commonly used nowadays to improve the power system performance. This paper presents the design and Implementation of non-linear, Adaptive Real Time Recurrent Learning Algorithm (RTRL) based controller for TCSC to damp power system oscillations and enhance the stability of the system. This control scheme requires two sets of neural networks. The first set is a neuro-identifier and the second set is a neuro-controller which generate the required control signals for the thyristors.

Words are the important building blocks of every language. Morphological generator is used to get the inflected form of a word, given its root word and a set of properties such as lexical category and morphological properties. Morphological Generation and analysis are necessary for developing computational grammars as well as machine translation systems. This paper presents a morphological generator for Malayalam nouns using Memory Based Language Processing (MBLP) approach. MBLP is an approach to language processing based on exemplar storage during learning, and analogical reasoning during processing. For training the system, a training corpus is created. It contains the basic examples of root words and their features. The feature set for this Malayalam noun generation system includes number, case, and the last syllable of the root word. Tilburg Memory based Learner (TiMBL) is used for training the system. The system doesn't require a dictionary or rules for its working. It gives a satisfactory result, having an accuracy of 93.68%

Text chunking consists of dividing a text into syntactically correlated parts of words. Given the words and their morphosyntactic class, a chunker will decide which words can be grouped as chunks. Malayalam is a free word order language and has relatively unrestricted phrase structures that make the problem of chunking quite challenging. This paper aims to develop a text chunker for Malayalam using Memory-Based Learning (MBL) approach. Memory-Based Learning is a machine learning methodology based on the idea that the direct reuse of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples. The chunker was trained using the tool Memory-Based Tagger (MBT) with words and their POS tags as features. The chunker demonstrated an accuracy of 97.14%.

Transliteration forms an essential part of transcription which converts text from one writing system to another. The need for translating data has become larger than before as the world is getting together through social media. Machine transliteration has emerged as a part of information retrieval and machine translation projects to translate named entities, that are not registered in the dictionary, based on phonemes and graphemes. This paper proposes a machine learning technique that performs transliteration from Tamil to Malayalam, two languages that belong to Dravidian family. Transliteration can be used to supplement machine translation process by handling the issues that can happen due to the presence of named entities.

This paper presents a novel design of a co-ordinated controller for series connected FACTS devices like Thyristor Controlled Series Capacitor(TCSC) and Thyristor controlled Power Angle Regulator (TCPAR). The scheme can be used for non-linear system control, in which the exact linearized mathematical model of the system is not required, can be used to control many FACTS devices with a single controller. The basis of the proposed design is the Real Time Recurrent Learning (RTRL) algorithm in which the Neural Network (NN) is trained in real time. This requires two sets of neural networks. The first set is a fully connected Recurrent Neural Network (RNN) which acts as a neuro-identifier that provides the dynamic model of the system. The second set of neural network is the neuro-controller, used to generate the required control signals for the thyristors. Simulations results of the system using MATLAB/SIMULINK show that the performance of the system with the proposed controller is better than the conventional PI controllers and GA-based PI controllers.