I have a set of topics each described with a list of keywords. {Sports:['Ronaldo Messi Zidane','Football Baseball', 'Barcelona Real']...}
The task is to classify a particular document. The classification can be also multi-label. A document can belong to topic1,topic2 etc. I don't have enough data thus can't approach the problem using machine learning. Because I want to retrieve highly precise documents I approached the problem using k-gram index.
I treat a given set of topic keywords as queries and built a k-gram index around it. So I have all the keys as character bigrams and the values as terms which contain the bigram. These terms are terms present in the document that I want to classify. After traversing the postings list for every keyword of a topic I get a set of candidate terms and their corresponding jaccard similarity score.
Within a topic How do I combine jaccard score of all candidate terms ?
Within all topics how do I decide which topic this document belongs to ?
Do you think this approach can give me results with high precision ?
Thank you.
This seems like a multi-class multi-label classification problem. Since the questioner is comfortable using a detailed lexical approach. This article here will help in building a pragmatic solution.
Related
So, I have a task where I need to measure the similarity between two texts. These texts are short descriptions of products from a grocery store. They always include a name of a product (for example, milk), and they may include a producer and/or size, and maybe some other characteristics of a product.
I have a whole set of such texts, and then, when a new one arrives, I need to determine whether there are similar products in my database and measure how similar they are (on a scale from 0 to 100%).
The thing is: the texts may be in two different languages: Ukrainian and Russian. Also, if there is a foreign brand (like, Coca Cola), it will be written in English.
My initial idea on solving this task was to get multilingual word embeddings (where similar words in different languages are located nearby) and find the distance between those texts. However, I am not sure how efficient this will be, and if it is ok, what to start with.
Because each text I have is just a set of product characteristics, some word embeddings based on a context may not work (I'm not sure in this statement, it is just my assumption).
So far, I have tried to get familiar with the MUSE framework, but I encountered an issue with faiss installation.
Hence, my questions are:
Is my idea with word embeddings worth trying?
Is there maybe a better approach?
If the idea with word embeddings is okay, which ones should I use?
Note: I have Windows 10 (in case some libraries don't work on Windows), and I need the library to work with Ukrainian and Russian languages.
Thanks in advance for any help! Any advice would be highly appreciated!
You could try Milvus that adopted Faiss to search similar vectors. It's easy to be installed with docker in windows OS.
Word embedding is meaningful inside the language but can't be transferrable to other languages. An observation for this statement is: if two words co-occur with a lot inside sentences, their embeddings can be near each other. Hence, as there is no one-to-one mapping between two general languages, you cannot compare word embeddings.
However, if two languages are similar enough to one-to-one mapping words, you may count on your idea.
In sum, without translation, your idea is not applicable to two general languages anymore.
Does the data contain lots of numerical information (e.g. nutritional facts)? If yes, this could be used to compare the products to some extent. My advice is to think of it not as a linguistic problem, but pattern matching as these texts have been assumably produced using semi-automatic methods using translation memories. Therefore similar texts across languages may have similar form and if so this should be used for comparison.
Multilingual text comparison is not a trivial task and I don't think there are any reasonably good out-of-box solutions for that. Yes, multilingual embeddings exist, but they have to be fine-tuned to work on specific downstream tasks.
Let's say that your task is about a fine-grained entity recognition. I think you have a well defined entities: brand, size etc...
So, these features that defines a product each could be a vector, which means your products could be represented with a matrix.
You can potentially represent each feature with an embedding.
Or mixture of the embedding and one-hot vectors.
Here is how.
Define a list of product features:
product name, brand name, size, weight.
For each product feature, you need a text recognition model:
E.g. with brand recognition you find what part of the text is its brand name.
Use machine translation if it is possible to make unified language representation for all sub texts. E.g. Coca Cola to
ru Кока-Кола, en Coca Cola.
Use contextual embeddings (i.e. huggingface multilingial BERT or something better) to convert prompted text into one vector.
In order to compare two products, compare their feature vectors: what is the average similarity between two feature array. You can also decide what is the weight on each feature.
Try other vectorization methods. Perhaps you dont want to mix brand knockoffs: "Coca Cola" is similar to "Cool Cola". So, maybe embeddings aren't good for brand names and size and weight but good enough for product names. If you want an exact match, you need a hash function for their text. On their multi-lingual prompt-engineered text.
You can also extend each feature vectors, with concatenations of several embeddings or one hot vector of their source language and things like that.
There is no definitive answer here, you need to experiment and test to see what is the best solution. You cam create a test set and make benchmarks for your solutions.
I'm trying to match an input text (e.g. a headline of a news article) to sets of keywords, s.t. the best-matching set can be selected.
Let's assume, I have some sets of keywords:
[['democracy', 'votes', 'democrats'], ['health', 'corona', 'vaccine', 'pandemic'], ['security', 'police', 'demonstration']]
and as input the (hypothetical) headline: New Pfizer vaccine might beat COVID-19 pandemic in the next few months.. Obviously, it fits well to the second set of keywords.
Exact matching words is one way to do it, but more complex situations might arise, for which it might make sense to use base forms of words (e.g. duck instead of ducks, or run instead of running) to enhance the algorithm. Now we're talking NLP already.
I experimented with Spacy word and document embeddings (example) to determine similarity between a headline and each set of keywords. Is it a good idea to calculate document similarity between a full sentence and a limited number of keywords? Are there other ways?
Related: What NLP tools to use to match phrases having similar meaning or semantics
There is not one correct solution for such a task. you have to try what fits your problem!
Possible ways to solve your problem I can think of:
Matching: either exact or more elaborated such as lemma/stemming, or Levensthein.
Embedding Similarity: I guess word similarity would outperform document-keywords similarity, but again, just experiment with it.
Classification: Your problem seems to be a classic classification problem, which each set being one class. If you don't have enough labeled training data, you could try active-learning.
I'm trying to classify a list of documents. I'm using CountVectorizer and TfidfVectorizer to vectorize the documents before the classification. The results are good but I think that they could be better if we will consider not only the existence of specific words in the document but also the order of these words. I know that it is possible to consider also pairs and triples of words but I'm looking for something more inclusive.
Believe it or not, but bag of words approaches work quite well on a wide range of text datasets. You've already thought of bi-grams or tri-grams. Let's say you had 10-grams. You have information about the order of your words, but it turns out there are rarely more than one instance of each 10-gram, so there would be few examples for your classification model to learn from. You could try some other custom feature engineering based on the text, but it would be a good amount of work that rarely help much. There are other successful approaches in Natural Language Processing, especially in the last few years, but they usually focus on more than word ordering.
The context is : I already have clusters of words (phrases actually) resulting from kmeans applied to internet search queries and using common urls in the results of the search engine as a distance (co-occurrence of urls rather than words if I simplify a lot).
I would like to automatically label the clusters using semantics, in other words I'd like to extract the main concept surrounding a group of phrases considered together.
For example - sorry for the subject of my example - if I have the following bunch of queries : ['my husband attacked me','he was arrested by the police','the trial is still going on','my husband can go to jail for harrassing me ?','free lawyer']
My study deals with domestic violence, but clearly this cluster is focused on the legal aspect of the problem so the label could be "legal" for example.
I am new to NPL but I have to precise that I don't want to extract words using POS tagging (or at least this is not the expected final outcome but maybe a necessary preliminary step).
I read about Wordnet for sense desambiguation and I think that might be a good track, but I don't want to calculate similarity between two queries (since the clusters are the input) nor obtain the definition of one selected word thanks to the context provided by the whole bunch of words (which word to select in this case ?). I want to use the whole bunch of words to provide a context (maybe using synsets or categorization with the xml structure of the wordnet) and then summarize the context in one or few words.
Any ideas ? I can use R or python, I read a little about nltk but I don't find a way to use it in my context.
Your best bet is probably is to label the clusters manually, especially if there are few of them. This a difficult problem even for humans to solve, because you might need a domain expert. Anyone claiming they could do that automatically and reliably (except in some very limited domains) is probably running a startup and trying to get your business.
Also, going through the clusters yourself will have benefits. 1) you may discover you had the wrong number of clusters (k parameter) or that there was too much junk in the input to begin with. 2) you will gain qualitative insight into what is being talked about and what topic there are in the data (which you probably can't know before looking at the data). Therefore, label manually if qualitative insight is what you are after. If you need quantitative result too, you could then train a classifier on the manually labelled topics to 1) predict topics for the rest of the clusters, or 2) for future use, if you repeat the clustering, get new data, ...
When we talk about semantics in this area we mean Statistical Semantics. The statistical or distributional semantics is very different from other definitions of semantics which has logic and reasoning behind it. Statistical semantics is based on Distributional Hypothesis, which considers context as meaning aspect of words and phrases. Meaning in very abstract and general sense in different litterers is called topics. There are several unsupervised methods for modelling topics, such as LDA or even word2vec, which basically provide word similarity metric or suggest a list of similar words for a document as another context. Usually when you have these unsupervised clusters, you need a domain expert to tell the meaning of each cluster.
However, for several reasons you might accept low accuracy assignment of a word as the general topic (or as in your words "global semantic") to a list of phrases. If this is the case, I would suggest to take a look at Word Sense Disambiguation tasks which look for coarse grained word senses. For WordNet, it might be called supersense tagging task.
This paper worth to take a look: More or less supervised supersense tagging of Twitter
And about your question about choosing words from current phrases, there is also an active question about "converting phrase to vectors", my answer to that question in word2vec fashion might be useful:
How can a sentence or a document be converted to a vector?
I can add more related papers later if it comes to my mind.
The paper Automatic Labelling of Topic Models explains the author's approach to this problem. To provide an overview I can tell you that they generate some label candidates using the information retrieved from Wikipedia and Google, and once they have the list of candidates in place they rank those candidates to find the best label.
I think the code is not available online, but I have not looked for it.
The package chowmein claims to do this in python using the algorithm outlined in Automatic Labeling of Multinomial Topic Models.
One possible approach, which the below papers suggest is identifying the set of keywords from the cluster, getting all the synonyms and then finding the hypernyms for each synonym.
The idea is to get a more abstract meaning for the cluster by using the hypernym.
Example: A word cluster containing words dog and wolf should not be labelled with either word but as canids. They achieve it using synonymy and hypernymy.
Cluster Labeling by Word Embeddings
and WordNet’s Hypernymy
Automated Text Clustering and Labeling using Hypernyms
I am dealing with a problem of text summarization i.e. given a large chunk(s) of text, I want to find the most representative "topics" or the subject of the text. For this, I used various information theoretic measures such as TF-IDF, Residual IDF and Pointwise Mutual Information to create a "dictionary" for my corpus. This dictionary contains important words mentioned in the text.
I manually sifted through the entire 50,000 list of phrases sorted on their TFIDF measure and hand-picked 2,000 phrases (I know! It took me 15 hours to do this...) that are the ground truth i.e. these are important for sure. Now when I use this as a dictionary and run a simple frequency analysis on my text and extract the top-k phrases, I am basically seeing what the subject is and I agree with what I am seeing.
Now how can I evaluate this approach? There is no machine learning or classification involved here. Basically, I used some NLP techniques to create a dictionary and using the dictionary alone to do simple frequency analysis is giving me the topics I am looking for. However, is there a formal analysis I can do for my system to measure its accuracy or something else?
I'm not an expert of machine learning, but I would use cross-validation. If you used e.g. 1000 pages of text to "train" the algorithm (there is a "human in the loop", but no problem), then you could take another few hundred test pages, and use your "top-k phrases algorithm" to find the "topic" or "subject" of these. The ratio of test pages where you agree with the outcome of the algorithm gives you a (somewhat subjective) measure of how well your method performs.