Mehrnoush Shamsfard


2019

bib
Knowledge-Based Word Sense Disambiguation with Distributional Semantic Expansion
Hossein Rouhizadeh | Mehrnoush Shamsfard | Masoud Rouhizadeh
Proceedings of the 2019 Workshop on Widening NLP

In this paper, we presented a WSD system that uses LDA topics for semantic expansion of document words. Our system also uses sense frequency information from SemCor to give higher priority to the senses which are more probable to happen.

pdf bib
EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks
Rayehe Hosseini Pour | Mehrnoush Shamsfard
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words or concepts are important parts of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manually construction and extension of lexico-semantic relations for WordNets or knowledge graphs are very time-consuming. Using automatic relation extraction methods can speed up this process. In this study, we exploit an ensemble of lstm and convolutional neural networks in a supervised manner to capture lexico-semantic relations which can either be used directly in NLP applications or compose the edges of wordnets. The whole procedure of learning vector space representation of relations is language independent. We used Princeton WordNet 3.1, FarsNet 3.0 (the Persian wordnet), Root09 and EVALution as golden standards to evaluate the predictive performance of our model and the results are comparable on the two languages. Empirical results demonstrate that our model outperforms the state of the art models.

pdf bib
Beheshti-NER: Persian named entity recognition Using BERT
Ehsan Taher | Seyed Abbas Hoseini | Mehrnoush Shamsfard
Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers

2017

pdf bib
Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity
Niloofar Ranjbar | Fatemeh Mashhadirajab | Mehrnoush Shamsfard | Rayeheh Hosseini pour | Aryan Vahid pour
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper, we describe our proposed method for measuring semantic similarity for a given pair of words at SemEval-2017 monolingual semantic word similarity task. We use a combination of knowledge-based and corpus-based techniques. We use FarsNet, the Persian Word Net, besides deep learning techniques to extract the similarity of words. We evaluated our proposed approach on Persian (Farsi) test data at SemEval-2017. It outperformed the other participants and ranked the first in the challenge.

2016

pdf bib
Using Data Mining Techniques for Sentiment Shifter Identification
Samira Noferesti | Mehrnoush Shamsfard
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Sentiment shifters, i.e., words and expressions that can affect text polarity, play an important role in opinion mining. However, the limited ability of current automated opinion mining systems to handle shifters represents a major challenge. The majority of existing approaches rely on a manual list of shifters; few attempts have been made to automatically identify shifters in text. Most of them just focus on negating shifters. This paper presents a novel and efficient semi-automatic method for identifying sentiment shifters in drug reviews, aiming at improving the overall accuracy of opinion mining systems. To this end, we use weighted association rule mining (WARM), a well-known data mining technique, for finding frequent dependency patterns representing sentiment shifters from a domain-specific corpus. These patterns that include different kinds of shifter words such as shifter verbs and quantifiers are able to handle both local and long-distance shifters. We also combine these patterns with a lexicon-based approach for the polarity classification task. Experiments on drug reviews demonstrate that extracted shifters can improve the precision of the lexicon-based approach for polarity classification 9.25 percent.

2012

pdf bib
Corpus based Semi-Automatic Extraction of Persian Compound Verbs and their Relations
Somayeh Bagherbeygi | Mehrnoush Shamsfard
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Nowadays, Wordnet is used in natural language processing as one of the major linguistic resources. Having such a resource for Persian language helps researchers in computational linguistics and natural language processing fields to develop more accurate systems with higher performances. In this research, we propose a model for semi-automatic construction of Persian wordnet of verbs. Compound verbs are a very productive structure in Persian and number of compound verbs is much greater than simple verbs in this language This research is aimed at finding the structure of Persian compound verbs and the relations between verb components. The main idea behind developing this system is using the wordnet of other POS categories (here means noun and adjective) to extract Persian compound verbs, their synsets and their relations. This paper focuses on three main tasks: 1.extracting compound verbs 2.extracting verbal synsets and 3.extracting the relations among verbal synsets such as hypernymy, antonymy and cause.

2010

pdf bib
Extracting Lexico-conceptual Knowledge for Developing Persian WordNet
Mehrnoush Shamsfard | Hakimeh Fadaei | Elham Fekri
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Semantic lexicons and lexical ontologies are some major resources in natural language processing. Developing such resources are time consuming tasks for which some automatic methods are proposed. This paper describes some methods used in semi-automatic development of FarsNet; a lexical ontology for the Persian language. FarsNet includes the Persian WordNet with more than 10000 synsets of nouns, verbs and adjectives. In this paper we discuss extraction of lexico-conceptual relations such as synonymy, antonymy, hyperonymy, hyponymy, meronymy, holonymy and other lexical or conceptual relations between words and concepts (synsets) from Persian resources. Relations are extracted from different resources like web, corpora, Wikipedia, Wiktionary, dictionaries and WordNet. In the system presented in this paper a variety of approaches are applied in the task of relation extraction to extract ladled or unlabeled relations. They exploit the texts, structures, hyperlinks and statistics of web documents as well as the relations of English WordNet and entries of mono and bi-lingual dictionaries.

pdf bib
STeP-1: A Set of Fundamental Tools for Persian Text Processing
Mehrnoush Shamsfard | Hoda Sadat Jafari | Mahdi Ilbeygi
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Many NLP applications need fundamental tools to convert the input text into appropriate form or format and extract the primary linguistic knowledge of words and sentences. These tools perform segmentation of text into sentences, words and phrases, checking and correcting the spellings, doing lexical and morphological analysis, POS tagging and so on. Persian is among languages with complex preprocessing tasks. Having different writing prescriptions, spacings between or within words, character codings and spellings are some of the difficulties and challenges in converting various texts into a standard one. The lack of fundamental text processing tools such as morphological analyser (especially for derivational morphology) and POS tagger is another problem in Persian text processing. This paper introduces a set of fundamental tools for Persian text processing in STeP-1 package. STeP-1 (Standard Text Preparation for Persian language) performs a combination of tokenization, spell checking, morphological analysis and POS tagging. It also turns all Persian texts with different prescribed forms of writing to a series of tokens in the standard style introduced by Academy of Persian Language and Literature (APLL). Experimental results show high performance.

2008

pdf bib
Towards Semi Automatic Construction of a Lexical Ontology for Persian
Mehrnoush Shamsfard
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Lexical ontologies and semantic lexicons are important resources in natural language processing. They are used in various tasks and applications, especially where semantic processing is evolved such as question answering, machine translation, text understanding, information retrieval and extraction, content management, text summarization, knowledge acquisition and semantic search engines. Although there are a number of semantic lexicons for English and some other languages, Persian lacks such a complete resource to be used in NLP works. In this paper we introduce an ongoing project on developing a lexical ontology for Persian called FarsNet. We exploited a hybrid semi-automatic approach to acquire lexical and conceptual knowledge from resources such as WordNet, bilingual dictionaries, mono-lingual corpora and morpho-syntactic and semantic templates. FarsNet is an ontology whose elements are lexicalized in Persian. It provides links between various types of words (cross POS relations) and also between words and their corresponding concepts in other ontologies (cross ontologies relations). FarsNet aggregates the power of WordNet on nouns, the power of FrameNet on verbs and the wide range of conceptual relations from ontology community.

pdf bib
A Hybrid Morphology-Based POS Tagger for Persian
Mehrnoush Shamsfard | Hakimeh Fadaee
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In many applications of natural language processing (NLP) grammatically tagged corpora are needed. Thus Part of Speech (POS) Tagging is of high importance in the domain of NLP. Many taggers are designed with different approaches to reach high performance and accuracy. These taggers usually deal with inter-word relations and they make use of lexicons. In this paper we present a new tagging algorithm with a hybrid approach. This algorithm combines the features of probabilistic and rule-based taggers to tag Persian unknown words. In contrast with many other tagging algorithms this algorithm deals with the internal structure of the words and it does not need any built in knowledge. The introduced tagging algorithm is domain independent because it uses morphological rules. In this algorithm POS tags are assigned to unknown word with a probability which shows the accuracy of the assigned POS tag. Although this tagger is proposed for Persian, it can be adapted to other languages by applying their morphological rules.

pdf bib
A Bottom Up approach to Persian Stemming
Amir Azim Sharifloo | Mehrnoush Shamsfard
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

2007

pdf bib
Summarization and Evaluation; Where are we today?!
Mehrnoush Shamsfard | Amir Saffarian | Samaneh Ghodratnama
Proceedings of the 21st Pacific Asia Conference on Language, Information and Computation

2006

pdf bib
Unsupervised Discovery of Persian Morphemes
Mohsen Arabsorkhi | Mehrnoush Shamsfard
Demonstrations