Aniruddha Deshpande's repositories
VPE-Processing-EN-HI
Code to the Paper "Processing English Verb Phrase Ellipsis for Conversational English-Hindi Machine Translation"
acl-style-files
Official style files for papers submitted to venues of the Association for Computational Linguistics
CLDR_CLNER_models
Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the token level representations of the sentence obtained with a CharacterBERT (El Boukkouri et al., 2020) model. The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method. Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines. Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.
demoinfo
A library to analyze CS:GO demos in C#
demoinfo-csgo-python
Prototyping with CSGO demo parsing in python
ellipsis-detection
A rule-based NLP Project to detect licensor and antecedent of nominal ellipsis in a sentence.
Felix-Unity
Github Page for the game Felix as a part of the course Game Design and Engineering. The game was developed using Unity version 2018.4.12f1
Handling-English-VPE-for-English-Hindi-MT
English-Hindi machine translation systems have difficulty interpreting verb phrase ellipsis (VPE) in English, and commit errors in translating sentences with VPE. We present a solution and theoretical backing for the treatment of English VPE, with the specific scope of enabling English-Hindi MT, based on an understanding of the syntactical phenomenon of verb-stranding verb phrase ellipsis in Hindi (VVPE). We implement a rule-based system to perform the following sub-tasks: 1) Verb ellipsis identification in the English source sentence, 2) Elided verb phrase head identification 3) Identification of verb segment which needs to be induced at the site of ellipsis 4) Modify input sentence; i.e. resolving VPE and inducing the required verb segment. This system obtains 94.83 percent precision and 83.04 percent recall on subtask (1), tested on 3900 sentences from the BNC corpus [Leech, 1992]. This is competitive with state-of-the-art results. We measure accuracy of subtasks (2) and (3) together, and obtain a 91 percent accuracy on 200 sentences taken from the WSJ cor- pus[Paul and Baker, 1992]. We carried out a manual analysis of the MT outputs of 100 sentences after passing it through our system. We set up a basic metric (1-5) for this evaluation, where 5 indicates drastic improvement, and obtained an average of 3.55.
LeetCode-Questions-CompanyWise
Contains Company Wise Questions sorted based on Frequency and all time
notebooks
Some notebooks for NLP
processeditor
Home of the true Open Source BPM Framework.
tntspa
Official repository of "Neural Machine Translating from Natural Language to SPARQL"