Ekbal et al., 2009 - Google Patents
Named entity recognition in Bengali: A multi-engine approachEkbal et al., 2009
View PDF- Document ID
- 4883901951095925538
- Author
- Ekbal A
- Bandyopadhyay S
- Publication year
- Publication venue
- Northern European Journal of Language Technology
External Links
Snippet
This paper reports about a multi-engine approach for the development of a Named Entity Recognition (NER) system in Bengali by combining the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM) with the …
- 238000000034 method 0 abstract description 37
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2765—Recognition
- G06F17/277—Lexical analysis, e.g. tokenisation, collocates
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2705—Parsing
- G06F17/2715—Statistical methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30613—Indexing
- G06F17/30619—Indexing indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2765—Recognition
- G06F17/2775—Phrasal analysis, e.g. finite state techniques, chunking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/21—Text processing
- G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
- G06K9/6807—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries
- G06K9/6842—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries according to the linguistic properties, e.g. English, German
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ekbal et al. | Named entity recognition in Bengali: A multi-engine approach | |
Ekbal et al. | Named entity recognition using support vector machine: A language independent approach | |
Cao et al. | A joint model for word embedding and word morphology | |
Ekbal et al. | A conditional random field approach for named entity recognition in Bengali and Hindi | |
Ekbal et al. | Part of speech tagging in bengali using support vector machine | |
KR20050036857A (en) | Character string identification | |
Na | Conditional random fields for Korean morpheme segmentation and POS tagging | |
Wu et al. | Extracting named entities using support vector machines | |
Ekbal et al. | A hidden markov model based named entity recognition system: Bengali and hindi as case studies | |
Ekbal et al. | Maximum entropy based Bengali part of speech tagging | |
Amarappa et al. | Named entity recognition and classification in kannada language | |
Grönroos et al. | Morfessor EM+ Prune: Improved subword segmentation with expectation maximization and pruning | |
Noshin Jahan et al. | Bangla real-word error detection and correction using bidirectional lstm and bigram hybrid model | |
Singh et al. | Named entity recognition for manipuri using support vector machine | |
Ekbal et al. | Voted NER system using appropriate unlabeled data | |
Singh et al. | Manipuri POS tagging using CRF and SVM: A language independent approach | |
Ekbal et al. | Web-based Bengali news corpus for lexicon development and POS tagging | |
Mekki et al. | Tokenization of Tunisian Arabic: a comparison between three Machine Learning models | |
Ekbal et al. | Named entity recognition using appropriate unlabeled data, post-processing and voting | |
Ma et al. | Feature-enriched word embeddings for named entity recognition in open-domain conversations | |
Banisakher et al. | Improving the identification of the discourse function of news article paragraphs | |
Ekbal et al. | Bengali named entity recognition using classifier combination | |
Le et al. | A maximum entropy approach to sentence boundary detection of Vietnamese texts | |
Ekbal et al. | Voted approach for part of speech tagging in bengali | |
Bandyopadhyay et al. | HMM based POS Tagger and Rule-based Chunker for Bengali |