Questions tagged [natural-language-process]

Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation. See NLP.

123 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
4
votes
1answer
161 views

How do we pass data to a RNN?

Let's say we have A1, A2, ... , Am different articles in the corpus and each of them has W1, W2, ....., Ww words. We are training a language model on them. Do we: Scheme 1 Take the first batch of ...
3
votes
1answer
65 views

Word2Vec: Why do some dimensions of an embedding have an interpretation, and why does addition/subtraction of embedding vectors work?

I'm reading about Word2Vec from this source: http://jalammar.github.io/illustrated-word2vec/. Below is the heatmap of the embeddings for various words. In the source, it's claimed that we can get an ...
3
votes
0answers
29 views

Dataset availability for automatic text summarization

I'm working on an automatic text summarization NLP problem and looking for a dataset with USA legal case reports similar to the Australian legal case reports dataset in UCI repository. Can you please ...
3
votes
4answers
52 views

Pre-trained models

I am starting off with machine learning so could someone tell if there is some site where one can find the current best performing trained models for any specific problem like sentiment analysis or ...
3
votes
1answer
71 views

How do I visualize data for a natural language processing project?

I am using a question-and-answer dataset. My neural network takes a question and an article content, and outputs where an answer starts (as an integer). To visualize my data, how should I process it ...
3
votes
1answer
132 views

Why does joint embedding of word and images work?

I often see some papers where the authors do point-wise multiplication of word and image embedding (e.g the image below). Why does this implementation works? I do not understand.
3
votes
0answers
105 views

multiple intents for modifying an intent of a sentence?

Say I have a sentence like 'I refuse to fly' or 'I'd like to fly'. I also have a sentence like 'I don't want to sit'. When training custom intents in one of the available NLU engines (rasa/wit/luis), ...
3
votes
2answers
96 views

Is NLP suitable for my legal contract parsing problem?

My company has a product that involves the extraction of a variety of fields from legal contract PDFs. The current approach is very time consuming and messy, and I am exploring if NLP is a suitable ...
2
votes
0answers
23 views

Medical NER for French language

I'm currently exploring the options to extract medical NER specifically for French language. I tried SpaCy's general French NER but it wasn't helpful to the cause (...
2
votes
0answers
41 views

Is it acceptable to append information to word embeddings?

Let's say I have my 300 dimensional word embedding trained with Word2Vec and it contains 10,000 word vectors. I have additional data on the 10,000 words in the form of a vector (10,000x1), containing ...
2
votes
1answer
51 views

word2vec: usefulness of context vectors in classification

I've been working on a NN-based classification system that accepts document vectors as input. I can't really talk about what I'm specifically training the neural net on, so i'm hoping for a more ...
2
votes
1answer
112 views

KeyError: Selecting text from a dataframe based on values of another dataframe

I have the following two dataframes badges and comments. I have created a list of 'gold users' from ...
2
votes
0answers
17 views

Practical example and working of Laplace Smoothing or Linear Interpolation in Natural Language Processing (NLP)

Let us suppose we have a document where total_words = 50 (for example -> is,the,now,is,am,here,now) total_unique_words = 40 (for example -> is,the,am,here,now) How can we apply the ...
2
votes
0answers
23 views

What could be proper terms for a research direction in natural language processing to measure meaningfulness?

For some time, I did assessments to design metrics on how to recognize well-written and meaningful software requirements. Then I decided to work with Stack Overflow question posts because they are a ...
2
votes
0answers
13 views

Generate Intro-Text for Newsletter

I am trying to implement the following idea. For a daily newsletter I would like to generate an engaging and funny intro text, such as: Good morning. Sorry if there are beer stains and buffalo ...
2
votes
0answers
22 views

Step extraction from a paragraph

Came across an interesting problem: Given a paragraph describing how to do a process, need to break it down to various steps. Basically, need to determine for each sentence in the paragraph, if this ...
2
votes
1answer
220 views

Guidelines to debug REINFORCE-type algorithms?

I implemented a self-critical policy gradient (as described here), for text summarization. However, after training, the results are not as high as expected (actually lower than without RL...). I'm ...
2
votes
0answers
88 views

pros and cons of lexical vs machine learning methods for text mining

I wanted to know what are the pros and cons are of using lexical methods and machine learning methods for classifying texts based topic. I have used a simple method of mining documents related to a ...
2
votes
0answers
40 views

Assigning tags to posts using predefined set of tags

I want to tag the text of a post with a predefined set of tags. A post could have multiple tags such as health, addiction, etc. I want to recommend up to $5$ tags. Total of $60$ tags is present. ...
2
votes
0answers
260 views

Integration of NLP and Angular application

I'm doing a small POC in which I've trained my Machine Learning model (Naive Bayes) and is saved in ".pkl" (pickle) format. Now my next task is to develop a web application which asks the user to ...
2
votes
0answers
26 views

Natural language Generator using Data from table

I am working on some natural language generator part. eg.1 Input to it will be Col1 Col2 Col3 A B 13 X Y 14 Output should be two sentence, one ...
2
votes
2answers
797 views

How does BERT deal with catastrophic forgetting?

In the ULMFit paper authors propose a strategy of gradual unfreezing in order to deal with catastrophic forgetting. That is, when the model starts be fine-tuned according to a downstream task, there ...
2
votes
0answers
38 views

How to prepare the data for text generation task

First, I'm not sure whether the model contains the encoder during training. EOS means end-of-sentence. Encoder and decoder are part of transformer network. If without-encoder, training time: ...
2
votes
2answers
39 views

Is there any text similarity databse available for phrases?

I want to train my application for phrase similarity. I want my model to predict similarity score for phrases as shown in below examples. ex- ...
2
votes
0answers
22 views

What approach to use to detect violations of media ethics in news?

I've been trying to come up with a solution to detect violations of media ethics in news articles. For example, I need to detect if an article has mentioned the name of a victim of rape. So far, I've ...
2
votes
0answers
28 views

How do I perform Sentiment Analysis on Tweets in the following pattern:

I have tweets obtained based on matches (football) before the match begins. I have tweets which specify a team will win 3-1 and so on which are easily analyzed using regular expressions. I am facing ...
1
vote
1answer
19 views

Should we also include negative instance in cross-validation process of one-class classifiers?

For a one-class classifier to do text classification, only positive instances are used for training. However, in the cross-validation process to select the best hyperparameters, should we also include ...
1
vote
1answer
27 views

Preparing training data for NLP machine learning task

I have the natural language sentences as follows: This is a black chair. It is next to the table. Each phrase that represents an object is annotated with an object ...
1
vote
0answers
38 views

Learning words embedding for bigrams and unigrams in a corpus

I am working on a topic modeling for tweets projects. I have generated my topics using both unigrams and bigrams. Topics are defined with a mixture of both bigrams and unigrams. Now I am planning to ...
1
vote
1answer
18 views

How to handle like meaning sentences when working on text summarization

Suppose we have a text like Today is a very bad day. Very bad day is today. I wont come to play. What kind of technique should I use to summarize similar texts like ...
1
vote
0answers
10 views

auto updating text comparison model

I have a need to create a model that compares and groups distinct snippets of text based on keywords. I can extract similar keywords with NLP methods and simply comparing sentence text. I want these ...
1
vote
1answer
27 views

Working Behavior of BERT vs Transformers vs Self-Attention+LSTM vs Attention+LSTM on the scientific STEM data classification task?

So I just used BERT pre-trained with Focal Loss to classify Physics, Chemistry, Biology and Mathematics and got a good f-1 macro of 0.91. It is good given it only had to look for the tokens like ...
1
vote
0answers
17 views

Trying to use CBOW for tweet classification

I'm trying to use the Continuous Bag Of Words method for word embedding on a corpus of 7503 tweets. In particular, I'm trying to use CBOW on this Kaggle competition, which involves classifying tweets ...
1
vote
2answers
81 views

State-of-the-art Python packages that can evaluate language similarity

I am trying to evaluate the likelihood of generating a specific sentence out of a large set of sentences. To do this, I start from a simple approach: training a custom n-gram language model and ...
1
vote
0answers
25 views

Semantic networks and conceptual graphs

I would like to use semantic networks to understand changes in texts. For example when I add/remove some words within a text. ...
1
vote
1answer
64 views

Semantic network using word2vec

I have thousands of headlines and I would like to build a semantic network using word2vec, specifically google news file. My sentences look like ...
1
vote
0answers
21 views

How to do embedding for nested dictionary with varying size?

I'm working on an RL task in which the agent needs have some observation. Instead using images, I want to use available information of the environment as the observation. The information regarding the ...
1
vote
3answers
145 views

NLP: Compare tags semantically with machine learning? (finding synonyms)

Let's say I have multiple tags that I need to compare semantically. For example: ...
1
vote
0answers
12 views

How to classify very short text for spend analytics?

I have a very small description of the item, example below- ACCELATOR SHAFT ADAPTOR –EGR PIPE & EGR VALVE(125KVA) ADAPTOR-EGR COOLER AND VALVE-125 KVA ADJUSTING LATCH - ALTERNATOR-125 KVA Some ...
1
vote
0answers
58 views

Removing duplicate records before training

I am currently working on a project classifying text into classes. The specific problem is classifying job titles into various industry codes. For example "McDonalds Employee" might get classified to ...
1
vote
1answer
19 views

What are some of the available methods for handling multi-label classification for longer sequences of text

I am looking to solve a multi-class classification problem with long sequences of text with some rows having 1000's of tokens. Some of the state of the art methods such as BERT have a token limit and ...
1
vote
0answers
646 views

Keras: Vector regression: ValueError:can not squeeze dim[1]

I'm trying to create a NLP model which takes x_train_padded_2 (padded/tokenized text sequences) as input and try to approximate ...
1
vote
3answers
56 views

approach to classify text with natural language processing methods

I have a problem with regards to text classification/categorization. The task is bugging me for days already and as I am pretty new to AI and the field of natural language processing (NLP) I am just ...
1
vote
1answer
108 views

How to implement LSTM using Doc2Vec vectors to get representation?

Hi all. I'm a newbie in ML. I read and found a paper about A Multi-Level Plagiarism Detection System Based on Deep Learning Algorithms and want to implement this model . But I can't find more about ...
1
vote
0answers
11 views

Taking huge time to execute piepeline text classification model using sklearn?

I have created a pipeline model for text classification using python ,Firstly i have tried on 30k records dataset it is working fine got the good results , but when it comes to huge data set like 50k ...
1
vote
0answers
10 views

Looking for suggestions on performing Sementic Analysis of ASR text

Currently I am working on a project where I have ASR on which I am performing semantic analysis to extract meaning out of it. The ASR text contains huge amount of vague conversational text which needs ...
1
vote
1answer
101 views

Train a deep learning model in chunks/sequentially to avoid memory error

How do I train/fit a model in chunks so as to escape the dreaded memory error? ...
1
vote
0answers
24 views

Using transformers for information extraction

Task I am trying to do some information extraction on earnings reports. I am trying to extract certain metrics, e.g. net sales for quarters. The earnings reports differ quite a lot in how they are ...
1
vote
0answers
217 views

Flair Custom NER

I'm working on a problem in the domain of NER. I have a dataset wherein I need to have custom tags for different entities. I don't know how to start or even where to start. I know that there are ...
1
vote
1answer
80 views

How to generalize comments using NLP

I have list of log comments in CSV file. I want to cluster those log comments using K-Means and after that I want convert each cluster comments into general form. for eg. I have bunch of comments in ...