Topic Modeling AI Papers

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Natural Language Processing (NLP) is a hot topic in machine learning. It involves analyzing and understanding text-based data. Clustering algorithms are quite popular in NLP. They group a set of unlabeled texts in such a way that texts in the same cluster are more like one another. Topic modeling is one application of clustering in NLP. It uses unsupervised learning to extract topics from a collection of documents. Other applications include automatic document organization and fast information retrieval or filtering.

You’ll learn how to use Cohere’s NLP tools to perform semantic search and clustering of AI Papers. This will help you discover trends in AI. You’ll scrape the Journal of Artificial Intelligence Research. The output is a list of recently published AI papers. You’ll use Cohere’s Embed Endpoint to generate word embeddings using your list of AI papers. Finally, visualize the embeddings and proceed to build semantic search and topic modeling.

To follow along with this tutorial, you need to be familiar with Python. Make sure you have python version 3.6+ installed in your development machine. You can also use Google Colab to try out the project in the cloud. Finally, you need to have a Cohere Account. If you haven’t signed up already, register for a New Cohere Account. All new accounts receive $75 free credits. You’ll access a Pay-As-You-Go option after finishing your credits.

First, you need to install the python dependencies required to run the project. Use pip to install them using the command below

PYTHON
1pip install requests beautifulsoup4 cohere altair clean-text numpy pandas sklearn > /dev/null

Create a new python file named cohere_nlp.py. Write all your code in this file. import the dependencies and initialize Cohere’s client.

PYTHON
1import cohere
2
3api_key = '<api_key>'
4co = cohere.Client(api_key)

This tutorial focuses on applying topic modeling to look for recent trends in AI. This task requires you to source a list of titles for AI papers. Use web scraping techniques to collect a list of AI papers. Use the Journal of Artificial Intelligence Research as your data source. Finally, you will clean this data by removing unwanted characters and stop words.

First, import the required libraries to make web requests and process the web content .

PYTHON
1import requests
2from bs4 import BeautifulSoup
3import pandas as pd
4import numpy as np
5from cleantext import clean

Next, make an HTTP request to the source website that has an archive of the AI papers.

PYTHON
1URL = "https://www.jair.org/index.php/jair/issue/archive"
2page = requests.get(URL)

Use this archive to get the list of AI papers published. This archive has papers published since 2015. This tutorial considers papers published recently, on or after 2020 only.

PYTHON
1soup = BeautifulSoup(page.content, "html.parser")
2archive_links = []
3
4for link in soup.select('a.title'):
5  vol = link.text
6  link = link.get('href')
7  year = int(vol[vol.find("(")+1:vol.find(")")])
8  if year >= 2020:
9    archive_links.append({ 'year': year, 'link': link })

Finally, you’ll need to clean the titles of the AI papers gathered. Remove trailing white spaces and unwanted characters. Use the NTLK library to get English stop words and filter them out.

PYTHON
1papers = []
2for archive in archive_links:
3  page = requests.get(archive['link'])
4  soup = BeautifulSoup(page.content, "html.parser")
5  links = soup.select('h3.media-heading a')
6  for link in links:
7    # clean the title
8    title = clean(text=link.text,
9            fix_unicode=True,
10            to_ascii=True,
11            lower=True,
12            no_line_breaks=False,
13            no_urls=False,
14            no_emails=False,
15            no_phone_numbers=False,
16            no_numbers=False,
17            no_digits=False,
18            no_currency_symbols=False,
19            no_punct=False,
20            replace_with_punct="",
21            replace_with_url="This is a URL",
22            replace_with_email="Email",
23            replace_with_phone_number="",
24            replace_with_number="123",
25            replace_with_digit="0",
26            replace_with_currency_symbol="$",
27            lang="en")
28    papers.append({ 'year': archive['year'], 'title': title, 'link': link.get('href') })

The dataset created using this process has 258 AI papers published between 2020 and 2022. Use pandas library to create a data frame to hold our text data.

PYTHON
1df = pd.DataFrame(papers)
2print(len(df))
260

Word embedding is a technique for learning the representation of words. Words that have same meanings have similar representation. You can use these embeddings to:

• cluster large amounts of text
• match a query with other similar sentences
• perform classification tasks like sentiment classification

Cohere’s platform provides an Embed Endpoint that returns text embeddings. An embedding is a list of floating-point numbers. They capture the semantic meaning of the represented text. Models used to create these embeddings are available in 3 sizes: small, medium, and large. Small models are faster while large models offer better performance.

Write a function to create the word embeddings using Cohere. The function should read as follows:

PYTHON
1def get_embeddings(text,model='medium'):
2  output = co.embed(
3                model=model,
4                texts=[text])
5  return output.embeddings[0]

Create a new column in your pandas data frame to hold the embeddings created.

PYTHON
1df['title_embeds'] = df['title'].apply(get_embeddings)

Congratulations! You have created the word embeddings . Now, you will proceed to visualize the embeddings using a scatter plot. First, you need to reduce the dimensions of the word embeddings. You’ll use the Principal Component Analysis (PCA) method to achieve this task. Import the necessary packages and create a function to return the principle components.

PYTHON
1from sklearn.decomposition import PCA
2
3def get_pc(arr,n):
4  pca = PCA(n_components=n)
5  embeds_transform = pca.fit_transform(arr)
6  return embeds_transform

Next, create a function to generate a scatter plot chart. You’ll use the altair library to create the charts.

PYTHON
1import altair as alt
2def generate_chart(df,xcol,ycol,lbl='off',color='basic',title=''):
3  chart = alt.Chart(df).mark_circle(size=500).encode(
4    x= alt.X(xcol,
5      scale=alt.Scale(zero=False),
6      axis=alt.Axis(labels=False, ticks=False, domain=False)
7    ),
8
9    y= alt.Y(ycol,
10      scale=alt.Scale(zero=False),
11      axis=alt.Axis(labels=False, ticks=False, domain=False)
12    ),
13
14    color= alt.value('#333293') if color == 'basic' else color,
15    tooltip=['title']
16  )
17
18  if lbl == 'on':
19    text = chart.mark_text(align='left', baseline='middle',dx=15, size=13,color='black').encode(text='title', color= alt.value('black'))
20  else:
21    text = chart.mark_text(align='left', baseline='middle',dx=10).encode()
22
23  result = (chart + text).configure(background="#FDF7F0"
24        ).properties(
25        width=800,
26        height=500,
27        title=title
28       ).configure_legend(
29  orient='bottom', titleFontSize=18,labelFontSize=18)
30
31  return result

Finally, use the embeddings with reduced dimensionality to create a scatter plot.

PYTHON
1sample = 200
2embeds = np.array(df['title_embeds'].tolist())
3embeds_pc2 = get_pc(embeds,2)
4df_pc2 = pd.concat([df, pd.DataFrame(embeds_pc2)], axis=1)
5
6df_pc2.columns = df_pc2.columns.astype(str)
7print(df_pc2.iloc[:sample])
     year                                              title  \
0    2022  metric-distortion bounds under limited informa...
1    2022  recursion in abstract argumentation is hard --...
2    2022  crossing the conversational chasm: a primer on...
3    2022  hebo: pushing the limits of sample-efficient h...
4    2022  learning bayesian networks under sparsity cons...
..    ...                                                ...
195  2020  using machine learning for decreasing state un...
196  2020  mapping the landscape of artificial intelligen...
197  2020  contrasting the spread of misinformation in on...
198  2020  to regulate or not: a social dynamics analysis...
199  2020  qualitative numeric planning: reductions and c...
                                                  link  \
0    https://www.jair.org/index.php/jair/article/vi...
1    https://www.jair.org/index.php/jair/article/vi...
2    https://www.jair.org/index.php/jair/article/vi...
3    https://www.jair.org/index.php/jair/article/vi...
4    https://www.jair.org/index.php/jair/article/vi...
..                                                 ...
195  https://www.jair.org/index.php/jair/article/vi...
196  https://www.jair.org/index.php/jair/article/vi...
197  https://www.jair.org/index.php/jair/article/vi...
198  https://www.jair.org/index.php/jair/article/vi...
199  https://www.jair.org/index.php/jair/article/vi...
                                          title_embeds          0          1
0    [-0.7879443, 0.14064652, -1.1886923, 1.0581255...   3.773588  13.307009
1    [0.37486058, 2.2867563, 0.48023587, -1.3632454...  13.585676   8.620154
2    [-0.7070194, -0.5557753, 2.6077378, 0.11462678...  17.785715  -8.959141
3    [-0.19081053, 0.05036301, -0.48858774, 0.66812...  -0.499191  -5.828358
4    [0.84096915, -1.0650194, -0.8836163, -1.631231...  -1.372444  -5.549065
..                                                 ...        ...        ...
195  [-1.4959816, 0.8587867, 1.1109167, -0.9420541,... -12.141110 -21.291473
196  [-1.7567614, 0.12333965, -0.41682896, 0.820096...   9.577528  -8.201695
197  [-0.25555933, -1.6548307, -1.1497015, -1.00241...   4.017263  16.588557
198  [-1.1415248, 0.9333024, 0.18291989, 2.2976398,...  -2.903703   6.331687
199  [-0.8152119, 0.7301979, -1.6634299, -0.395152,... -12.204317 -11.396169
[200 rows x 6 columns]

Here’s a chart demonstrating the word embeddings for AI papers. It is important to note that the chart represents a sample size of 200 papers.

PYTHON
1generate_chart(df_pc2.iloc[:sample],'0','1',title='2D Embeddings')

Data searching techniques focus on using keywords to retrieve text-based information. You can take this a level higher. Use search queries to determine the intent and contextual meaning. In this section, you’ll use Cohere to create embeddings for the search query. Use the embeddings to get the similarity with your dataset’s embeddings. The output is a list of similar AI papers.

First, create a function to get similarities between two embeddings. This will use the cosine similarity algorithm from the sci-kit learn library.

PYTHON
1from sklearn.metrics.pairwise import cosine_similarity
2
3def get_similarity(target,candidates):
4  # Turn list into array
5  candidates = np.array(candidates)
6  target = np.expand_dims(np.array(target),axis=0)
7
8  # Calculate cosine similarity
9  sim = cosine_similarity(target,candidates)
10  sim = np.squeeze(sim).tolist()
11  sort_index = np.argsort(sim)[::-1]
12  sort_score = [sim[i] for i in sort_index]
13  similarity_scores = zip(sort_index,sort_score)
14
15  # Return similarity scores
16  return similarity_scores

Next, create embeddings for the search query

PYTHON
1new_query = "graph network strategies"
2
3new_query_embeds = get_embeddings(new_query)

Finally, check the similarity between the two embeddings. Display the top 10 similar papers using your result

PYTHON
1similarity = get_similarity(new_query_embeds,embeds[:sample])
2
3print('Query:')
4print(new_query,'\n')
5
6print('Similar queries:')
7for idx,sim in similarity:
8  print(f'Similarity: {sim:.2f};',df.iloc[idx]['title'])
Query:
graph network strategies
Similar queries:
Similarity: 0.49; amp chain graphs: minimal separators and structure learning algorithms
Similarity: 0.46; pure nash equilibria in resource graph games
Similarity: 0.44; general value function networks
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Similarity: 0.42; graph kernels: a survey
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Similarity: 0.32; multi-agent advisor q-learning
Similarity: 0.32; fairness in influence maximization through randomization
Similarity: 0.32; a sufficient statistic for influence in structured multiagent environments
Similarity: 0.32; constraint and satisfiability reasoning for graph coloring
Similarity: 0.31; sum-of-products with default values: algorithms and complexity results
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Similarity: 0.29; preferences single-peaked on a tree: multiwinner elections and structural results
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Similarity: 0.29; lilotane: a lifted sat-based approach to hierarchical planning
Similarity: 0.29; cost-optimal planning, delete relaxation, approximability, and heuristics
Similarity: 0.29; learning over no-preferred and preferred sequence of items for robust recommendation
Similarity: 0.28; a few queries go a long way: information-distortion tradeoffs in matching
Similarity: 0.28; constrained multiagent markov decision processes: a taxonomy of problems and algorithms
Similarity: 0.28; contiguous cake cutting: hardness results and approximation algorithms
Similarity: 0.28; online relaxation refinement for satisficing planning: on partial delete relaxation, complete hill-climbing, and novelty pruning
Similarity: 0.28; playing codenames with language graphs and word embeddings
Similarity: 0.28; a theoretical perspective on hyperdimensional computing
Similarity: 0.28; reasoning with pcp-nets
Similarity: 0.28; improving the effectiveness and efficiency of stochastic neighbour embedding with isolation kernel
Similarity: 0.27; teaching people by justifying tree search decisions: an empirical study in curling
Similarity: 0.27; learning optimal decision sets and lists with sat
Similarity: 0.27; efficient multi-objective reinforcement learning via multiple-gradient descent with iteratively discovered weight-vector sets
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Similarity: 0.27; planning with critical section macros: theory and practice
Similarity: 0.27; regarding goal bounding and jump point search
Similarity: 0.27; using machine learning for decreasing state uncertainty in planning
Similarity: 0.26; strategyproof mechanisms for additively separable and fractional hedonic games
Similarity: 0.26; ranking sets of objects: the complexity of avoiding impossibility results
Similarity: 0.26; socially responsible ai algorithms: issues, purposes, and challenges
Similarity: 0.26; inductive logic programming at 30: a new introduction
Similarity: 0.26; to regulate or not: a social dynamics analysis of an idealised ai race
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Similarity: 0.25; evolutionary dynamics and phi-regret minimization in games
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Similarity: 0.22; set-to-sequence methods in machine learning: a review
Similarity: 0.22; the ai liability puzzle and a fund-based work-around
Similarity: 0.22; agent-based markov modeling for improved covid-19 mitigation policies
Similarity: 0.22; the parameterized complexity of motion planning for snake-like robots
Similarity: 0.22; induction and exploitation of subgoal automata for reinforcement learning
Similarity: 0.22; point at the triple: generation of text summaries from knowledge base triples
Similarity: 0.22; trends in integration of vision and language research: a survey of tasks, datasets, and methods
Similarity: 0.21; multi-document summarization with determinantal point process attention
Similarity: 0.21; reward machines: exploiting reward function structure in reinforcement learning
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Similarity: 0.21; properties of switch-list representations of boolean functions
Similarity: 0.21; conceptual modeling of explainable recommender systems: an ontological formalization to guide their design and development
Similarity: 0.21; predicting decisions in language based persuasion games
Similarity: 0.20; epidemioptim: a toolbox for the optimization of control policies in epidemiological models
Similarity: 0.20; ffci: a framework for interpretable automatic evaluation of summarization
Similarity: 0.20; analysis of the impact of randomization of search-control parameters in monte-carlo tree search
Similarity: 0.20; output space entropy search framework for multi-objective bayesian optimization
Similarity: 0.20; multiobjective tree-structured parzen estimator
Similarity: 0.19; on the decomposition of abstract dialectical frameworks and the complexity of naive-based semantics
Similarity: 0.19; hebo: pushing the limits of sample-efficient hyper-parameter optimisation
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Similarity: 0.19; explainable deep learning: a field guide for the uninitiated
Similarity: 0.19; multi-label classification neural networks with hard logical constraints
Similarity: 0.19; declarative algorithms and complexity results for assumption-based argumentation
Similarity: 0.19; worst-case bounds on power vs. proportion in weighted voting games with an application to false-name manipulation
Similarity: 0.19; collie: continual learning of language grounding from language-image embeddings
Similarity: 0.18; a word selection method for producing interpretable distributional semantic word vectors
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Similarity: 0.18; metric-distortion bounds under limited information
Similarity: 0.18; neural natural language generation: a survey on multilinguality, multimodality, controllability and learning
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Similarity: 0.18; autotelic agents with intrinsically motivated goal-conditioned reinforcement learning: a short survey
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Similarity: 0.18; madras : multi agent driving simulator
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Similarity: 0.17; visually grounded models of spoken language: a survey of datasets, architectures and evaluation techniques
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Similarity: 0.17; agent-based modeling for predicting pedestrian trajectories around an autonomous vehicle
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Similarity: 0.10; recursion in abstract argumentation is hard --- on the complexity of semantics based on weak admissibility
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Visualizing semantic search: https://github.com/cohere-ai/notebooks/blob/main/notebooks/Visualizing_Text_Embeddings.ipynb

Clustering is a process of grouping similar documents into clusters. It allows you to organize many documents into a smaller number of groups. As a result, you can discover emerging patterns in the documents. In this section, you will use the k-Means clustering algorithm to identify the top 5 clusters.

First, import the k-means algorithm from the scikit-learn package. Then configure two variables: the number of clusters and a duplicate dataset.

PYTHON
1from sklearn.cluster import KMeans
2
3df_clust = df_pc2.copy()
4n_clusters=5

Next, initialize the k-means model and use it to fit the embeddings to create the clusters.

PYTHON
1kmeans_model = KMeans(n_clusters=n_clusters, random_state=0)
2classes = kmeans_model.fit_predict(embeds).tolist()
3print(classes)
4df_clust['cluster'] = (list(map(str,classes)))
[2, 0, 3, 4, 4, 3, 1, 1, 0, 3, 0, 2, 0, 1, 1, 0, 0, 2, 0, 1, 3, 2, 1, 3, 0, 2, 2, 0, 2, 1, 1, 2, 2, 1, 0, 1, 1, 1, 2, 2, 2, 4, 3, 3, 3, 3, 2, 1, 2, 4, 3, 0, 2, 0, 1, 1, 0, 4, 0, 2, 2, 3, 1, 2, 4, 1, 2, 1, 4, 0, 3, 3, 4, 2, 0, 2, 2, 2, 0, 0, 0, 4, 1, 4, 1, 2, 0, 4, 1, 1, 4, 1, 4, 1, 4, 1, 0, 0, 4, 2, 4, 3, 4, 3, 2, 0, 2, 1, 1, 4, 2, 4, 2, 2, 0, 3, 1, 3, 2, 3, 1, 2, 0, 4, 4, 1, 0, 0, 4, 1, 1, 2, 2, 1, 2, 3, 0, 0, 1, 1, 1, 0, 4, 1, 4, 2, 4, 2, 4, 3, 2, 0, 1, 4, 1, 1, 2, 2, 0, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 0, 4, 2, 4, 2, 1, 0, 3, 0, 1, 0, 2, 2, 1, 4, 1, 3, 4, 1, 0, 2, 1, 2, 0, 2, 4, 1, 4, 2, 2, 1, 0, 0, 1, 0, 2, 1, 0, 4, 1, 4, 0, 2, 1, 4, 1, 3, 2, 4, 2, 0, 1, 0, 3, 0, 2, 4, 1, 1, 3, 2, 3, 1, 3, 4, 2, 2, 0, 1, 1, 1, 4, 1, 0, 4, 3, 2, 2, 2, 2, 0, 1, 3, 1, 3, 4, 2, 4, 2, 1, 3]

Finally, plot a scatter plot to visualize the 5 clusters in our sample size.

PYTHON
1df_clust.columns = df_clust.columns.astype(str)
2generate_chart(df_clust.iloc[:sample],'0','1',lbl='off',color='cluster',title='Clustering with 5 Clusters')

Let’s recap the NLP tasks implemented in this tutorial. You’ve created word embeddings, perform a semantic search, and text clustering. Cohere’s platform provides NLP tools that are easy and intuitive to integrate. You can create digital experiences that support powerful NLP capabilities like text clustering. It’s easy to Register a Cohere account and gain access to an API key. New cohere accounts have $75 free credits for the first 3 months. It also offers a Pay-as-you-go Pricing Model that bills you upon usage.