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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 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.

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 an approach to morphological analysis of Malayalam words as a classification Problem. The idea here is to use Memory Based Language Processing (MBLP) algorithm for Malayalam morphological analysis. MBLP is an approach to language processing based on exemplar storage during learning, and analogical reasoning during processing. The aim of the system is to find the citation forms (or meaningful parts) of words rather than a detailed morphological analysis. Training instances created from words are manually annotated for their segmentation and the system is trained using TiMBL (Tilburg Memory based Learner). The paper presents memory based model of Malayalam morphological analysis and its generalization accuracy.

In this paper, Sentence level Sentiment Analysis of Malayalam movie reviews is done by classifying the polarity of opinions obtained from the user as positive, negative and neutral. Sentiment analysis is an application of Natural Language Processing and text analysis which helps to identify the emotions in a given context. In this work a hybrid approach for Sentiment Analysis is used in which Machine Learning method is used for tagging and Fuzzy Logic is used to find the membership of the review in each sentiment class. Fuzzy logic can be used to handle the vagueness in natural language. Manually tagged data are trained using TnT and Fuzzy rules are incorporated for identifying and classifying the emotions. Certain other rules are also incorporated to handle certain special cases. The fuzzy rules yield output that varies in degree between 0 and 1.

In this paper, we present the details of a phonotactic language identification (LID) system developed for five Indian languages, English (Indian), Hindi, Malayalam, Tamil and Kan-nada. Since there are no publicly available speech databases for English, Malayalam and Kannada, we developed the database for each of the target languages by downloading the audio files from YouTube videos and removing the non-speech signals manually. The system was tested using a test data set consisting of 40 utterances with duration of 30, 10, and 3 sees, in each of 5 target languages. The performance evaluation was done separately accordingly to the NIST benchmarking sessions, for 30s, 10s and 3s segments separately. For the baseline system, we got an overall EER of 10.41 %, 19.56 % and 31.45 % for 30, 10, and 3 sees segments when tested with a 3-gram language model. The use of 4-gram language model has helped enhance the performance of the LID system to 9.81 %, 19.38 % and 32.77% respectively for 30,10 and 3 sees test segments. Further, by using the n-gram smoothing, we were able to improve the EER of the LID system, 9.02 %, 18.70 % and 29.24 % for 3-gram language models and 8.88 %, 16.46 % and 32.03 % for 4-gram language models, respectively for 30,10, and 3 sec test segments. The study shows that the use of 4-gram language models can help enhance the performance of LID systems for Indian languages.

Malayalam, a classical language in India, is spoken by over 40 million people. This paper proposes an effective information retrieval system for Malayalam, which retrieve Malayalam documents relevant to the user's information need. The proposed system improves effectiveness by considering synonyms and negations of the terms specified in the query. Though the evaluation of the system is performed over a small corpus, the results are promising. The proposed system is thus relevant for various natural language processing tasks in an highly agglutinative language like Malayalam.

Stemming is the process of removing the affixes from inflections and to return the root form. Malayalam is highly agglutinative in nature and hundreds of inflections are possible for each word. An effective stemmer in Malayalam is not yet implemented. This paper presents a lightweight stemmer for Malayalam, which conflates terms by suffix removal. The proposed stemmer is both computationally inexpensive and domain independent and will serve as a vital part in many areas of Malayalam Language Computing.

In natural language processing applications, string matching is the main time-consuming operation due to the large size of lexicon. Data dependence is minimal in string matching operations, and hence it is ideal for parallelization. A dedicated hardware for string matching that uses memory interleaving and parallel processing techniques can relieve the host CPU from this burden, thereby making the system suitable for real-time applications. This paper reports the FPGA design of such a system with m parallel matching units. The time complexity of the proposed algorithm is O (log2 n), where n is the total number of lexical entries. This has been achieved by a proper selection of the value of m. A special memory organization technique, which reduces the storage space by nearly 70%, has been adopted for storing lexical entries. The techniques used for matching and storage of lexical entries make the system language independent

Present day information retrieval systems largely ignore the issues of lexical and compositional semantics, and rely mainly on some statistical measures for choosing or evolving an indexing scheme. This has been the reason for the decreasing precision in their responses, given an exponentially increasing number of Web pages. The work reported in this paper addresses this issue from a linguistic point of view. We show that the detection of domain-specific phrases can capture the task-specific semantics of documents. We introduce the notion of n*-gram formalism to characterize the domain-specific phrases and their variants, taking a few sample domains. A method to construct a phrase grammar from a small set of documents is proposed. A method of conceptual indexing based on the phrase grammar has also been proposed. In order to demonstrate the effectiveness of the proposed method, we have designed a versatile system that can perform concept-based retrieval, in addition to several document-processing tasks, such as text classification, extraction-based summarization, context tracking, and semantic tagging. Collectively, the system can address the semantic content of documents. Considering the fact that an average user prefers highly relevant results in the top-ranked subset to an exhaustively retrieved set, it is shown that the proposed system performs better in that it retrieves documents that are more conceptually relevant than those retrieved by Google, and at 95% confidence level. Copyright ? 2005 Taylor & Francis Inc.

In Natural Language Processing applications, string matching is the main time-consuming operation. A dedicated co-processor for string matching that uses memory interleaving and parallel processing techniques can relieve the host CPU from this burden. This paper reports the FPGA design of such a system with m parallel matching units. It has been shown to improve the performance by a factor of nearly m, without increasing the chip area by more than 45% The time complexity of the proposed algorithm is O(log/sub 2/ n), where n is the number of lexical entries. The memory used by the lexicon has been efficiently organized and the space saving achieved is about 67%.

A story may be analyzed to identify the main characters and to extract the relationship between them. Relation extraction problems are generally solved either through supervised or unsupervised learning algorithms. In the former, there should be a text corpus for which the entities and their relation types are already known. Such algorithms typically learn to classify new entity pairs into any of the relation types it has already seen, based on some recurring patterns. On the other hand, the unsupervised learning approach is used when there is no such marked up corpus. Such algorithms typically identify patterns relevant to the relation extraction task, occurring within the corpus and then use these patterns to group entities such that the entities within a group share similar relationships. The proposed method is a hybrid approach which combines the features of unsupervised and supervised learning methods. It also uses some rules to extract relationships. The method identifies the main characters and collects the sentences related to them. Then these sentences are analyzed and classified to extract relationships. The main applications are story summarization and analysis of the major characters in stories.